This PDF file contains the front matter associated with SPIE Proceedings Volume 10970, including the Title Page, Copyright information, Table of Contents, Author and Conference Committee lists.

Non-contact structural health monitoring is a promising field for assessing civil structures, such as bridges. Not having to access the structure avoids different issues: the closure of the structure, the use of special equipment to access it, and others. This study uses digital image processing, machine learning, and parallel computing to detect the vibration of a flexible structure. If a structure is too stiff, a reinforced concrete short-span bridge or a multi-story building, it is hard to identify its natural frequencies without some sort of target panel or target feature. Instead, if the structure is flexible, it is possible to identify its displacement and its natural frequencies, but it is a challenge with high computational cost. This study presents an unsupervised machine-learning algorithm to identify a structure, its displacement, and its natural frequencies. The algorithm was deployed on a simple supported beam using a commercially available camera and an inexpensive GPU.

Phase-based motion magnification is one the most recent advances in computer vision and computational photography that has introduced new approaches for structural dynamics identification. Unwanted artifacts and distortions are often present in the motion-magnified videos that can be traced back to the reconstruction procedure after manipulating the phase variations. Within this article, we have studied this phenomenon more closely to find out the relations between the distortions and the choice of filter bank, and the magnification factor. It has been shown that Fourier transformation can reconstruct motion-magnified frames without inducing any distortions, and on the other hand using other filter banks such as Gabor wavelets will generate motion-magnified frames that have distortions in them. The source of these distortions are also discussed briefly.

Existing infrastructure in the U.S. is deteriorating; the symptoms of overdue maintenance and underinvestment are ever-present in our society (rated with D+ by American Society of Civil Engineers, ASCE). To ensure the safety of existing infrastructure, on-site life-time inspections and structural health monitoring are required. While these methods yield a great deal of raw and analyzed data, current methods for their simple and intuitive management, (i.e., simple and intuitive integration, documentation, access, and visualization), are severely lacking and can lead to mismanagement of infrastructure resources. New technologies such as virtual and augmented reality, combined with informational modeling, have a great potential to overcome this issue. The objective of this work is to develop a method of digital documentation for structural health assessment which is both efficient and effective. Therefore, it must not only achieve maximum productivity with minimum wasted expense in terms of time, cost, and effort, but it must also be flexible enough to retain a diverse set of records such as logs from sensors during structural health monitoring, notes from visual inspection, etc. To address the above challenges, this work proposes a methodology for integrating existing data about a structure and meta-data into a combination of virtual tour (VT), augmented reality (AR) and informational modeling (IM) environment. This method will enable on- and off-site presentation of engineering assessment data in an organized, intuitive, and interactive manner, and additionally foster communication between different parties involved with a structure simultaneously or at different periods.

Due to their mobility and autonomy, unmanned aerial vehicles (UAVs) provide an unprecedented opportunity to perform data gathering in a wide array of civil engineering applications such as visual inspection of infrastructure. Given their versatility, the role of UAVs can be expanded by leveraging their autonomous operations to deploy wireless sensing resources. This can be especially valuable in numerous field applications such as shear wave velocity (Vs) assessment of the subsurface. This study explores the feasibility of automating the autonomous placement and pickup of wireless geophone sensors using UAVs for multichannel analysis of surface waves (MASW) for subsurface characterization. Typically, autonomous navigation of UAVs is based on the fusion of inertial sensors and GPS to control the UAV flight trajectory. However, this approach is not sufficiently accurate for missions requiring precision placement and pickup of payloads (such as sensors). Hence, computer vision using fiducial markers can be used to augment traditional inertial sensing to add accuracy to the localization of the UAV relative to payloads. In this study, we use a set of fiducial markers of varying sizes as tracking targets during navigation missions. Pose information extracted from the marker images are integrated into a sensor fusion controller based on the Kalman filter. The work conducts field validation of the proposed computer vision navigation method showing accuracy of the UAV landing on a user defined target within 10 cm; as the UAV descends, smaller fiducial markers are shown to increase the precision of the UAV placement on the ground.

In structural health monitoring (SHM) applications, one of the advantages of utilizing a surface bonded fiber Bragg grating (FBG) sensor for damage detection is its increased sensitivity in collecting ultrasonic waves. Recent studies have demonstrated that for a certain bonding condition the output FBG response can be increased by bonding the optical fiber at a distance away from the FBG to collect optical fiber guided wave (L₀₁ mode) that is converted from S₀ Lamb wave, referred as remote bonding. However in order to apply the remote bonding configuration in practical situations, the S₀ mode conversion to L₀₁ mode through an adhesive bond under various conditions must be characterized. This work investigates how the coupled L₀₁ mode changes with varying input S₀ mode frequency and angle of incidence through an adhesive bond. The goal of this work is to better understand the S₀ mode conversion to the L₀₁ mode in order to implement the remote bonding configuration for an improved SHM of a structure.

Ultrasonic transducers typically used for inspection such as wall thickness measurement are not suitable for high temperature applications and the inspections are more of a periodic and manual in nature instead of continuous. A waveguide based method combined with synthetic focusing image reconstruction has been proposed for Structural Health Monitoring (SHM) of the components operating in hostile environments and is sometimes too complex and inaccessible for manual inspection. This work follows to develop a waveguide based sensing method to implement a robust and accurate monitoring of surface back wall damage with moderate coverage range. Finite Element (FE) Analysis has been carried out to study the multi-modal behavior in waveguides of rectangular cross sectional geometry and to choose optimal mode(s) and geometry which are best suited for the application. Simulations based on 3D Elastodynamic and 2D Acoustic FE models has been taken to study and implement the reconstruction for different cases representing various levels of wall loss. The waveguide is being used for transmitting the ultrasonic guided waves into the structure under investigation and the received signals from the structure has been processed using Synthetic focusing technique for the surface back wall reconstruction to access damage caused by corrosion like phenomenon.

Ease of implementation of any sensor system must involve simple instrumentation techniques along with adequate adaptability to change in specimen configuration. The past decade has witnessed an exponential upsurge in applications of piezoelectric transducers in various fields of engineering and biotechnology, whereas sensor types has remained the same, narrowing its suitability in certain fields. The present research deals with development of two novel piezo sensor configurations namely the single clamp non-bonded sensor (SCNS) and re-useable bolted sensor (RBS). The sensor was developed to accommodate change in dimension and is tested on different specimens based on its design. Fundamentally, SCNS is developed for circular structures such as pipelines and RBS is developed for 2-D plates. Effectiveness of both sensors is tested by mounting it on a specimen based on its design. Damage sensitivity of the sensors was studied by analysing the conductance signatures extracted from the piezo patches based on electromechanical impedance (EMI) technique. Repeatability of less than 2% was observed in both cases. At different levels of damage created shifts in signatures is observed. A statistical index used to quantify the amount of shift showed a gradual increase in magnitude corresponding to increase in damage. Similar trends are observed in both sensor data making both sensors suitable for application. Through the damage sensitivity tests, SCNS and RBS its proved viability for application in field studies. However, it might be noted that all tests were conducted in controlled environments of temperature and pressure and negating its effects by application of correction factors must be carried out for field application.

Guided waves (GW) are one of the most promising tools for structural health monitoring (SHM). They allow fast inspection of a large area. Thus GW based SHM is finding applications in several fields like aerospace, automotive, wind energy, etc. The GW propagate along the surface of the sample and get reflected from the boundaries and damage. Through proper signal processing of the reflected waves based on their time of arrival, the damage can be detected and isolated. For complex structures a higher number of sensors may be required which increases the cost of the equipment as well as the mass. The mass increase is detrimental in aerospace applications where the mass leads to increase in operation cost. Thus there is an effort to reduce the number of sensors. In addition, for the safety and reliability of the aircraft it is of utmost importance that the entire structure can be investigated. Hence it is necessary to optimize the locations of the sensors in order to maximise the coverage while limiting the number of sensors used. A genetic algorithm (GA) based optimization strategy was previously proposed by the authors for the use in a simple aluminium plate. This paper extends the optimization methodology for a composite plate with additional structural elements. The work starts with extension of the optimization strategy and the implementation of the anisotropic properties and structural elements in the optimization problem.

In this paper, we explore a metamaterial lens layer integrated within the pipe design to amplify the signal energy at the positions of the receiving sensors and damage initiation position enabling the transmission of damage information long distances. The gradient-index metamaterial layer is composed of unit cells with a hyperbolic secant profile distribution of refractive index to focus the wave energy transmitted by ultrasonic wave used in guided wave. The proposed methodology is implemented on a steel pipe typically used in the distribution networks utilized in cities and wave focusing performance is verified through numerical models.

We previously reported an elastomeric, optical foam sensor that can sense different types of deformation1 . The elastomeric foam is embedded with optical fibers that shine light into the foam while simultaneously transmitting scattered light exiting the foam. We applied machine learning techniques to the optical fiber data to form a prediction model that predicts whether the foam is being twisted or bent (classification), as well as the magnitude and direction of the deformation (regression). The best classification model had 100% accuracy on new data points, and the best regression models had a mean absolute error of 0.06 degrees on new data points. This kind of proprioceptive ability could give soft robots much more information about their physical state and therefore improve our ability to control them; however, prediction error increases with time due to drift in the optical fiber outputs. This paper presents an attempt to address this drift. We applied a technique based on work presented by Di Carlo et. al² . This unsupervised technique uses the evolutionary optimization process “covariance matrix adaptation evolution strategy” (CMA-ES) to compute a correction factor that can be applied to unobserved, drifted data points. The best solutions reduced classification error by 49% and regression mean absolute error by 36%.

In our previous work, we demonstrated how to use inductive bias to infuse a convolutional neural network (CNN) with domain knowledge from fatigue analysis for aircraft visual NDE. We extend this concept to SHM and therefore in this paper, we present a novel framework called DeepSHM which involves data augmentation of captured sensor signals and formalizes a generic method for end-to-end deep learning for SHM. The study case is limited to ultrasonic guided waves SHM. The sensor signal response from a Finite-Element-Model (FEM) is pre-processed through wavelet transform to obtain the wavelet coefficient matrix (WCM), which is then fed into the CNN to be trained to obtain the neural weights. In this paper, we present the results of our investigation on CNN complexities that is needed to model the sensor signals based on simulation and experimental testing within the framework of DeepSHM concept.

Composite is one of the most widely used industrial materials because of high strength, low weight, and high corrosion resistance properties. Different parts of composite structures are normally joined using adhesives or fasteners that are prone to defects and damages. A reliable method for prediction of the defect location is needed for an efficient structural health monitoring (SHM) process. Heterodyne effect is recently utilized for damage detection in the bonding zone of composite structures where debonding is expected to change the linear characteristics of the system into nonlinear characteristics. This paper briefly introduces this novel defect locating approach in composite plates using the heterodyne effect. For the first time, an Artificial Neural Network methodology is utilized with heterodyne effect method to find the defect location in composite plates. The main objective of this article is to develop a neural network based methodology for prediction of damage location, particularly for the bond inspection of composite plates.

This paper introduces the concept of an aerospace-grade electronic textile and summarizes design studies and early prototype development for on-fabric hypervelocity impact characterization. Whereas most damage detection technologies for aerospace systems rely on enhancements to the structure's inner shell, the outermost protective skin of a space habitat or a spacesuit - traditionally a woven fabric - is directly exposed to the relevant environment. Therefore, we propose weaving sensory fibers into traditional fibrous aerospace skins for direct measurement of local conditions, yielding a material that can simultaneously sense and protect. Specifically, this paper documents design considerations for multifunctional Beta cloth, in which piezoelectric yarn is directly woven into Teflon-coated fiberglass, the material used as the outermost skin of the International Space Station. A review of hypervelocity plasma generation then motivates a strawman design for on-textile plasma charge and RF emission sensing, which may be useful for further characterization of hypervelocity impactors. An aerospace-grade electronic textile is distinct from a traditional e-textile in that it must be validated not only for its sensing capabilities but also for its robustness to hazards presented in a space environment.

FeGa-based alloys (Fe_1-xGa_x, Galfenol) belong to a branch of magnetic materials called “magnetostrictive” materials, in which their dimensions change in response to changes in the magnetization. Magnetostrictive materials also experience an inverse effect, called the Villari effect, where magnetization and permeability changes occur in response to changes in applied stress/strain. In this study, an active mode water level sensor has been developed. The sensor has been designed to work when the water is both in motion and still. A Galfenol-brass unimorph beam has been constructed, with Galfenol as the active layer. The beam is clamped at one end, and, when it is flexed, there will be a stress concentration near the clamped region. This change in stress can be measured by a magnetic field sensor, which can detect local fluctuations in magnetic field due to the Villari effect. Two magnetic coils are used, one for alternating current (AC) magnetic field generation and the other for measuring the magnetic field response of the strip. The resonance frequency of the beam in air is higher than in water. By choosing an operating vibrational frequency higher than the resonance frequency in air, we can separate the pick-up coil impedance responses when the beam is surrounded by air or water, or even by sediment. The vertical response of the beam has also been measured; as the beam is covered by more water, its resonance frequency should incrementally change. The experimental results are compared with a simulation using a theoretical vibration model for a cantilever beam under water. The beam was tested both as the water level increased and decreased, showing a 10% increase from not submerged to fully submerged and the results were verified by the simulation. The relative sensor impedance between air and water was also evaluated from room temperature (25°C) to 80 °C to verify signal differentiation.

The presence of a single damage in structure tends to change its modal parameters (natural frequency and mode shapes).This fact is used by many researchers to predict the damage parameters i.e. damage depth and its location by measuring the changes in the modal parameters, by establishing a functional relationship. But in many cases, the severity of damage has to be large enough to be detected, thus making minute damages difficult to detect. In addition, the absence of baseline data poses problem for structures constructed long back when the concept of structural health monitoring was not in mainstream. The objective of this work is to develop and present a technique for identifying the location of multiple structural damages in a beam using the present modal data only. The method operates solely on the mode shape from the damaged structure and does not require a priori knowledge of the undamaged structure, thus developing a baseline free procedure. Multiple damages are induced for a simply supported beam by considering flexural vibrations and including two hairline crack of 0.08 mm at the bottom of the beam. Modal analysis has been done by carrying out parametric studies using ANSYS software to evaluate the natural frequencies and the corresponding mode shapes for different damage parameters (severity and location) of the simply supported beams. Secondly, curvature mode shapes are determined using finite difference approximation from displacement mode shapes. These curvature mode shapes can locate the damage when its thickness reduction is greater than 10%. However, for less severe damage, further processing of curvature mode shapes is required before damage can be located. Curve smoothening is done by use of cubic polynomial to the finite difference approximation to get the healthy signature of the beam. Post-processing of the damaged signature by determining the value of the intercept between the damaged signature and the cubic curve gives us the difference function which can be used to quantify the damage and compare its extent with other cases of damage. It is also concluded that this procedure is best suited to the mode shape obtained from the fundamental natural frequency. The mode shapes from lower natural frequencies are more sensitive to detect the damage as compared to higher frequencies modes. Experimental studies are also under way on real size I shaped simply supported steel beam to verify the simulation findings.

The study deals with a development of a novel analytical model based on extended ZM’s model together with Timoshenko beam theory for a superelastic shape memory alloy (SMA) cantilever beam taking into account the tension-compression asymmetry stress effect during loading/unloading process. The beam structure evolution is first identified by the trial method based on the geometric relation and force equilibrium. Subsequently, an analytical moment-curvature-neutral axis deviation and shear force-shear strain relations are derived for the identified beam structure. The moment-curvature response from theoretical model based on asymmetric effect, and theory and 3D FEM based on symmetric effect are shown to reveal the difference between them. Subsequently, the neutral axis deviation-tip load response and distribution of mertensite volume fraction along the beam length obtained from theory and 3D FEM based on asymmetric effect is demonstrated.

With advances in sensing and communication technologies, engineering systems are now commonly instrumented with sensors for system monitoring and management. Occasionally, when sensors become malfunction, it is advantageous to automatically determine faulty sensors in the system and, if possible, recover missing or faulty data. This paper investigates the use of machine learning techniques for sensor data reconstruction and anomaly detection. Specifically, bidirectional recurrent neural network (BRNN) is employed to build a data-driven model for sensor data reconstruction based on the spatiotemporal correlation among the sensor data. The reconstructed sensor data can be used not only for recovering the data of the faulty sensors, but also for detecting anomalies based on an analytical redundancy approach. The proposed method is tested with vibration data based on a numerical simulation of a sensor network for bridge monitoring application. In terms of prediction accuracy, the results show that the BRNN-based sensor data reconstruction method performs better than other existing sensor data reconstruction methods. Furthermore, the sensor data reconstructed can be used to detect and isolate the anomalies caused by faulty sensors.

The vehicle classification system developed by Federal Highway Administration (FHWA) of United States divides vehicle type into 13 categories depending on the number of axles and the wheelbase. However, establishing a fixed threshold for classifying a vehicle is difficult. The overlapping between vehicles pattern in the system needs a pattern recognition technique to distinguish between different vehicle categories. In this study, machine learning algorithms were used to classify various vehicles based on the collected traffic data from the embedded three-dimension Glass Fiber-Reinforced Polymer packaged Fiber Bragg Grating sensors (3D GFRP-FBG). The investigated machine learning algorithms include the support vector machines (SVM), Neural Network, and k-nearest neighbors (KNN) algorithms.

The widespread availability of cost-effective sensing technologies is translating into an increasing number of highway bridges being instrumented with structural health monitoring (SHM) systems. Current bridge SHM systems are only capable of measuring bridge responses and lack the ability to directly measure the traffic loads inducing bridge responses. The output-only nature of the monitoring data available often leaves damage detection algorithms ill-posed and incapable of robust detection. Attempting to overcome this challenge, this study leverages state-of-the-art computer vision techniques to establish a means of reliably acquiring load data associated with the trucks inducing bridge responses. Using a cyberenabled highway corridor consisting of cameras, bridge monitoring systems, and weigh-in-motion (WIM) stations, computer vision methods are used to track trucks as they excite bridges and pass WIM stations where their weight parameters are acquired. Convolutional neural network (CNN) methods are used to develop automated vehicle detectors embedded in GPU-enabled cameras along highway corridors to identify and track trucks from real-time traffic video. Detected vehicles are used to trigger the bridge monitoring systems to ensure structural responses are captured when trucks pass. In the study, multiple one-stage object detection CNN architectures have been trained using a customized dataset to identify various types of vehicles captured at multiple locations along a highway corridor. YOLOv3 is selected for its competitive speed and precision in identifying trucks. A customized CNN-based embedding network is trained following a triplet architecture to convert each truck image into a feature vector and the Euclidean distance of two feature vectors is used as a measure of truck similarity for reidentification purposes. The performance of the CNN-based feature extract is proved to be more robust than a hand-crafted method. Reidentification of the same vehicle allows truck weights measured at the WIM station to be associated with measured bridge responses collected by bridge monitoring systems.

Structural condition assessment of critical infrastructures involves smart instrumentation by a suitable grid of sensors. As instrumenting of large-scale civil structures is costly, design of optimal instrumentation plans that reflect the structural behavior of the bridges while minimizing the project costs is a crucial issue in their health monitoring. An optimal instrumentation in terms of type and number of sensors with their distribution pattern throughout the bridge depends on the structural behavior of interest. The sensitivity of instrumentation plan to different performance measures is dependent to the type and placement of sensors. Therefore, the effectiveness of a damage detection algorithm could be significantly impacted by the sensor layout. In this study, the dynamic instrumentation of a vertical lift steel gusset-less truss bridge is investigated. Several damage scenarios are simulated in its analytical model while various acceleration measurement locations of one of the fixed spans are considered in each case. The vibration of the bridge fixed span used in this study is due to the vertical impact of the lift span on the piers. The damage detection methodology relies on the application of the wavelet theory to demonstrate the structural condition of the bridge based on the predicted accelerations of the structure. As the current instrumentation layout of the bridge has been designed mainly for model updating and fatigue assessment of its gussetless connections, this paper provides some guidelines for modifications of the sensor configuration for future condition assessment and health monitoring purposes of the bridge.

The identification of the acoustic Green’s function (or, equivalently, the dynamic transfer function) of a medium is of interest to many fields, including structural testing, inspections and health monitoring. This paper will focus on the passive identification utilizing pairs of receivers and exploiting dynamic excitations that occur naturally in the structure. Several opportunities for this passive extraction exist, including: bridges under traffic excitation, buildings under seismic excitation, oceans under natural flows, and railroad tracks under train wheels excitations, among many others. A special signal processing approach is proposed to ensure that the Green’s function (or the transfer function) identification occurs without the influence of the random and generally unknown excitation and without the influence of uncorrelated noise that may affect the receivers. In particular, a special version of Welch’s periodogram technique is proposed where averages of the two outputs are taken both for the same time segments (“intra-segment” averaging) and for different time segments (“inter-segment” averaging) in order to eliminate the influence of noise at both receivers, in addition to eliminating the excitation source spectrum. It will be demonstrated, both analytically and experimentally, that this special signal processing is optimum for robust dual-output passive transfer function estimation. This technique will be then applied to the high-speed inspection of rail tracks by passive extraction of the rail acoustic Green’s function in the ultrasonic regime from the natural train wheel excitations. In this application, the dynamic outputs are collected by pairs of non-contact air-coupled receivers that have a 2-in stand-off from the rail surface. Changes in the passively-extracted Green’s function are then related to the presence of internal flaws (e.g. cracks) in the rail. Previously. a prototype based on this concept has been built and tested at the Transportation Technology Center (TTC) in Pueblo, Colorado, at speeds up to 80 mph. These speeds are unprecedented in the field of rail inspections, that are today carried out at ~30 mph at most by specialized test vehicles. This paper presents preliminary results from a second field test performed in the Fall of 2018 at TTC using a revised prototype design an speeds up to 40 mph. The successful development of this technique would revolutionize many aspects of rail maintenance by, for example, allowing regular trains to perform the inspections with no traffic disruption and great opportunity for redundancy due to the multiple train passes on the same track.

A smart infrastructure with many sensors becomes a complex hybrid system. Excluding external adversaries from infiltrating into the infrastructure is a key problem. With a formalized adversary model, we propose a decentralized architecture for smart infrastructure security. First, it effectively embeds information from the physical features of infrastructures into security. Second, our mechanism is managed through a decentralized blockchain architecture. Third, our approach is especially scalable to ever growing smart infrastructures. We prototyped an authentication blockchain for smart grids with a high penetration of distributed energy resources (DERs). Our implementation demonstrates the effectiveness of our mechanism compared to existing approaches.

In mountainous regions, the usability of roads and railway tracks strongly depends on the safety and the condition of retaining structures. Consequently, an early detection and identification of structural degradation, damages and potential failure mechanisms is of major interest. Currently, single point measurements using total stations or other sensors depict the state-of-the-art in deformation monitoring of retaining structures. Due to the large number of such structures in mountainous regions, e.g. the Alps, establishing a monitoring system for every object is unfeasible. This paper presents an approach for a large-scale deformation monitoring with a mobile mapping system (MMS). A MMS with two highquality laser scanners has been installed on the roof of a standard car. A newly developed algorithm processes the gathered data in a partially automated manner in order to perform deformation analysis on the one hand and to detect structural deficiencies (e.g. concrete spalling) on the other hand. Data of multiple (periodical) measurement campaigns of selected retaining structures have been used to evaluate the proposed approach. It is demonstrated, that mobile mapping in combination with targeted processing algorithms is a promising, efficient and comprehensive alternative to traditional static, single point monitoring solutions.

Triboelectric energy harvesters or nanogenerators exploit both contact electrification and electrostatic induction to scavenge excess energy from random motions of mechanical structures. This study focuses on the modeling of triboelectric energy harvesters in the configuration of contact-separation impact oscillators. While mechanical and electrostatic elements in such systems can be satisfactorily modeled based on existing theories, the underlying physics of contact electrification is still under debate. The aim of this work is to introduce the surface charge density of dielectric layers as a variable into the macroscopic equations of motion of triboelectric impact oscillators by experimentally investigating the relation between the impact force and the charge transfer during contact electrification. Specifically, specimens with selected pairs of materials are put under a solenoid-driven pressing tester which charges the specimens with a vertical force whose magnitude, frequency and duty cycle can be controlled. An electrometer is used to monitor the short circuit charge ow between the electrodes from which the charge accumulation on dielectric layers can be extracted. With results from parameter-sweep tests, the produced map from contact force to surface charge density can be integrated into equations of motion via curve fitting or interpolation.

Applications of low-cost wireless sensing are increasing in the power industry. With the addition of energy harvesting capability, the sensors become easy to deploy, install and retrofit to existing equipment. This paper will summarize progress on low-cost, easy-to-deploy, self-powering wireless sensor nodes integrated with existing solutions into wireless sensor networks, to monitor the temperature of selected equipment in an industrial power plant (motor, pump, etc.). Temperature data is important for monitoring the operating conditions of the equipment. Each sensor node includes: (a) energy-harvesting device and transducer, which converts the vibrational motion of the equipment into electricity, made by a piezoelectric material; (b) a power management circuit with cold start function for AC to DC, DC to DC, and voltage regulation; (c) temperature sensor; (d) an ultra-low power wireless communication unit using ZigBee protocol. The quiescent current consumption of the circuit is calibrated to be less than 15 µA, and the sensor and RF transmitter have an average current consumption of less than 10 µA. Multiple sensors in a network communicate with a powered hub unit intermittently while continuously harvesting and storing energy, maintaining always-on operation without external energy input. A robust packaging method is developed for easy installation and facile retrofitting in the relevant industry environment. The temperature information of an equipment is monitored for about 2 months. We demonstrate that the self-powering wireless sensor node can be implemented in a generic industrial power plant equipment with minimal cost and effort for maintenance while providing real-time temperature sensing and long-term, continuous monitoring.

Next generation of smart infrastructure is heavily dependent on distributed sensing technology to monitor the state of urban infrastructure. The smart sensor networks should react in time, establish automated control, and collect information for intelligent decision making. In this paper, we highlight our interdisciplinary research to address three main technical challenges related to smart infrastructure: (1) development of smart wireless sensors for civil infrastructure monitoring, (2) finding an innovative, cost-effective and sustainable energy resource for empowering heterogeneous, wireless sensor networks, and (3) designing advanced data analysis frameworks for the interpretation of the information provided by these emerging monitoring systems. More specifically, we focus on development of a self-powered piezo-floating-gate (PFG) sensor that uses only self-generated electrical energy harvested by piezoelectric transducers directly from a structure under vibration. The performance of this sensing technology is discussed for different civil infrastructure systems with complex behavior. Subsequently, the proposed data interpretation systems integrating deterministic, machine learning and statistical methods are reviewed. We outline our thoughtful vision for the proposed framework to serve as an integral part of future smart civil infrastructure, which will be capable of self-charging and the self-diagnosis of damage well in advance of the occurrence of failure.

This paper proposes an improved electromagnetic damper cum energy harvester (EMDEH), consisting of an electromagnetic damper and a specially designed energy harvesting circuit (EHC). EMDEH is intended to suppress structural vibrations by harvesting vibration energy of structures. A microcontroller unit (MCU) is applied in the EHC to control the power switch by adjusting the duty cycle appropriately through a feed-forward control. Consequently, the EHC can achieve a controllable equivalent damping coefficient. The performance of the proposed EHC was validated through a cyclic test on a universal testing machine. Although the total energy conversion is relatively low, a desirable constant resistance feature could be achieved and an electrical power with hundreds of mWs could be charged into a battery. These experimental results revealed the benefit of using MCU in a feed-forward control system in EMDEH, and the improved EMDEH could fulfill dual-functions, namely, simultaneous vibration control and energy harvesting, with an improved performance.

This study proposes a novel multistable mechanism to detect thermal limits though harvesting energy from thermally induced deformation. A detecting device is developed consisting of a bilaterally constrained beam equipped with a piezoelectric polyvinylidene fluoride (PVDF) transducer. Under thermally induced displacement, the bilaterally confined beam is buckled. The post-buckling response is deployed to convert low-rate and low-frequency excitations into high-rate motions. The attached PVDF transducer harvests the induced energy and converts it to electrical signals, which are later used to measure the thermal limits. The efficiency of the proposed method was verified through a numerical study on a prestressed concrete bridge. To this aim, finite element simulations were conducted to obtain the thermally induced deformation of the bridge members between the deck and girder. In addition, an experimental study was carried out on a 3D printed measuring device to simulate the thermal loading of bridge. In this phase, the correlation between the electrical signals generated by the PVDF film and the corresponding deck-girder displacement was investigated. Based on the results, the proposed method effectively measures the mechanical response of concrete bridges under thermal loading.

In-situ exploration the solar system planetary bodies requires the ability to penetrate the subsurface for sample collection. One type of a sampling device used in past missions that is continually being developed is the drill. In these extraterrestrial applications, the drilling systems have mass, volume and energy consumption constraints that limit their depth of penetration. To address the related challenge, a deep drill, called Auto-Gopher II, is currently being developed as a joint effort between JPL’s NDEAA laboratory and Honeybee Robotics Ltd. The Auto-Gopher II is a wireline rotary-hammer drill that combines breaking formations by hammering using a piezoelectric actuator and removing and collecting the cuttings by rotating a fluted bit. The hammering is produced by the Ultrasonic/Sonic Drill/Corer (USDC) mechanism that has been developed by the JPL team as an adaptable tool for many drilling and coring applications. The USDC uses an intermediate free-flying mass to convert high frequency vibrations of a piezoelectric transducer horn tip into lower frequency hammering of the drill bit. The USDC concept was used in a previous task to develop an Ultrasonic/Sonic Ice Gopher and then integrated into a rotary hammer device to develop the Auto-Gopher-I. The lessons learned from these developments were implemented into the development of the Auto-Gopher-II, an autonomous deep wireline drill with integrated cuttings management and drive electronics. Subsystems of this wireline drill were developed in parallel at JPL and Honeybee Robotics Ltd. In this paper, we present the latest developments including the integration of the whole drill, laboratory testing and field test results.

One of the options to improve the aerodynamic performance of wind turbines, airplane wings or trucks is due to active controlling of fluid flow. The most appropriate implementation concept is the use of synthetic jet actuators (SJA). There are different types of these kind of fluid flow generators. However, all they have conjointly the basic elements: membrane with attached piezoelectric disk element and small cavity with an orifice. In the case of symmetric composition of SJA is it possible to develop simple space-saving array module. Each module has two orifice which can be directed from or against each other in an angle. Thus the fluid jets are alternately generated. In a simple case the membrane is equipped on the side with one thin piezoelectric disk. The symmetrical attachment of a second one on the other side brings some advantages in the load performance or act as a sensor for structural health monitoring of the system. The positioning of at least two modules close to each other provides creation of crossing fluid jets, which finally leads to the formation of vortex flow. For design and optimization of symmetric jet actuators an equivalent circuit representation network model was used.

With the advancement of spacecraft payload technologies, multi-functional antennas are increasingly in demand. This paper presents a planar reconfigurable antenna technology that combines reflectarray antenna technology and micro-actuator technology. This antenna can realize wide angle beam scanning by rotating all patches simultaneously. Every patch is rotated by an innovative micro-actuator. This paper is mainly divided into three parts. The first part describes the patch of the antenna. The concentric dual split-rings patch structure is designed and analyzed. The Radio Frequency (RF) phase was found to be almost a linear function of the rotating angle of the patch. To verify the RF performance of this antenna, an antenna with 756 patches was designed and simulated. Simulation results show that the antenna has high gain and aperture efficiency. Finally, a novel rotational actuator was designed, optimized, fabricated, and tested. Unique characteristics of this actuator include small volume, low energy consumption, high controllability, fast response, and high reliability. These characteristics are essential to the successful performance of this antenna. For the architecture of this actuator, bimorphs were used as the driving units and micro-gears were used to form the transmission system. This actuator can rotate clockwise and counterclockwise alternately by altering the applied voltage. The control system is simple and easy to implement; only feedforward control is required. For a positioning application, this actuator does not have any accumulative error. The geometry of this actuator was optimized using the multi-physics coupling finite element method (FEM). Components of the actuator were fabricated by micro-electric-mechanical-system (MEMS) fabrication processes and precise machining technologies. Glass fiber reinforced composite (GFRC) and Lead Zirconium Titanite (PZT) materials were used to fabricate the bimorph. Copper and stainless steel were used to fabricate the housing and the gear, respectively. Several actuators were assembled and tested to investigate their characteristics. The relationship between the rotating angle and the applied voltage was studied both analytically and experimentally. In addition, response time was assessed using a high-speed camera.

The aim of this study is to propose a subsystem that is able to establish correlation between aerodynamics and structure measurements to be used as the input to the existing shape memory alloy actuated adaptive airfoil system. A reconfigurable wing with NACA 4412 airfoil was fabricated using Acrylonitrile Butadiene Styrene (ABS) and actuated using SMA spring. A wind tunnel testing was conducted at low Reynolds number with angle of attack ranging from 0° to 12°. Pressure taps were fitted along the upper and lower surface of the airfoil and connected to the multi-tube manometer in order to obtain pressure measurements and strain gauges were mounted in parallel to the pressure taps on the upper surface to measure the deflection of the structure due to the aerodynamics loading. A system to compute the lift and drag coefficients from pressure measurements, was developed using LabVIEW software. The strain of the airfoil structure on the upper surface of the airfoil was measured using strain gauge via the NI 9237 C Series Strain/Bridge Input Module. This measurement was mapped to the pressure distribution around the airfoil to obtain correlation with aerodynamic forces. A strain feedback system with PID controller was used to control the shape of the airfoil using the NI 9263 C Series Voltage Output Module. The work presented here is an extension of work done previously by the authors, and updates the existing adaptive airfoil system with an aerodynamics and wing structure measurements subsystem.

Nanofiller-modified composites have enormous potential for advanced structural health monitoring (SHM) because they are self-sensing via their piezoresistive properties. However, the current state of the art is largely limited to static damage detection between two distinct states (i.e. comparison between pre-damage and post-damaged states). This is an important limitation because transient responses can provide great insight into damage events. It is therefore desirable to incorporate the transient effects into piezoresistive-based SHM. Unfortunately, the transient response of piezoresistive materials has not been widely investigated. To address this limitation, this study examines the piezoresistive response of a slender carbon nanofiber (CNF)-modified epoxy rod specimen due to impact loading as a function of time. More specifically, the change in voltage along the length of the rod is recorded as an idealized one-dimensional elastic plane wave traverses through the specimen. This allows us to directly compare the rate of voltage change in the CNF/epoxy to the speed of sound of the material. The fundamental insights into transient piezoresistivity herein uncovered has the potential to greatly facilitate future advances in piezoresistive-based SHM.

The research presented in this paper focuses on predicting the electromechanical properties of conductive polymer composite (CPC) materials using stochastic modeling methods and data-driven model adaptation. CPC sensors have recently garnered attention in the field of structural health monitoring (SHM) due to their atomic similarities with composite materials which are an increasingly popular commodity among numerous industries. In this study, a CPC composed of carbon black nanoparticles and phenolic-based resin epoxy is manufactured and characterized both experimentally and via computational methods. The accuracy of the model is investigated, and the physical parameters defined in the model are adjusted based on empirical data. A potential manufacturing method for piezoresistive CPC sensors is presented, and preliminary results of sample builds are discussed. The potential applications for such a sensor are introduced, and the implementation of such sensors in industrial SHM applications is considered.

In this study, autonomous composites (AutoCom) are suggested by embedding multifunctional mechano-luminescenceoptoelectronic (MLO) composites into fiber reinforced polymer (FRP) structural composites. The AutoCom is designed for next generation unmanned aerial vehicles to enhance self-sustainability by benefiting from the self-powered strain sensing damage detection capability. The MLO composites generate direct current (DC) in response to mechanical stimuli, and the generated DC varies with magnitude of strain and strain rate. The DC-based strain sensing capability of MLO composites enables AutoCom to measure strain by itself without any external electrical source. The strain sensing data produced from the AutoCom can be potentially used to detect damage using vibration-based damage detection scheme. First, the MLO composites are fabricated by assembling two functional building blocks, such as mechanooptoelectronic (MO) poly(3-hexylthiophene) (P3HT)-based sensing thin films and mechano-luminescent (ML) copperdoped zinc sulfide (ZnS:Cu)-based elastomeric composites. The MLO composites’ self-powered strain sensing capability is validated by subjecting the MLO composites to cyclic tensile strains. Second, AutoCom specimen is fabricated and tested for validating its self-powered strain sensing capability using four-point bending test. Third, mechanical properties of the AutoCom are assessed through theoretical study by comparing to FRP composites without MLO embedment. Last, strain-based system identification methodology is proposed and used for performing system identification of FRP composites.

We have been developing smart photonic coating for structural materials to visualize strain mapping on steel or aluminum and to detect cracks on concrete. In the technology, strain imaging sheet made of colloidal photonic crystal film coated on a polyethylene terephthalate sheet. The strain imaging sheets change structural color by mechanical deformation. Now we have been testing smart photonic coating for real concreate bridges One of the key issues is the durability of the sheet for long-term use at the outfield site. In outdoor exposure test and in laboratory accelerated exposure test, polystyrene particles in the colloidal photonic crystal film were damaged and lost the structural color. To protect the deteriorating, top coat layer containing ultraviolet absorber was effective to reduce the damage of the colloidal photonic crystal film.

The pencil lead drawing paper sensor (PLDPS) is a flexible and wearable sensing device, a new concept sensor that offers a tremendous potential feasibility for a variety of applications. Highly responsive, sensitive, low cost, easy-tohandle pencil lead graphite and paper made from cellulose pulp fibers extracted from wood, rags or grass, which are inexpensive, nature-friendly materials were used. The sensing effect on 3 different papers (Plane, Hwasun, and Han papers) based on the properties of PLDPS was compared via FT-IR, tensile test, optical observation and initial electrical resistance (ER). The interfacial and mechanical properties of epoxy and GF/epoxy composites were evaluated by damage sensing and ER mapping using PLDPS with impact, flexural, and interlaminar shear strength (ILSS) tests. The optimum type of paper used as the adherend for the pencil sensor was chosen as the plane paper. As glass fiber (GF)/epoxy composites were severely damaged, the large change in ER of PLDPS was observed distinctly.

Surface acoustic wave (SAW) sensor has increasing demand in structural health monitoring due to its passive, reliable life-cycle, high accuracy, and small size. The ongoing demands of sensor’s adaptability with flexible substrates, which is capable of wireless monitoring, is the basis of the research. The SAW device assembly includes a piezoelectric composite substrate that aids in wave transmission and two interdigital transducers (IDTs) capable of actuating and sensing radio frequency (RF) signals. The SAW substrate is fabricated by integrating lead zirconate titanate ceramic nanoparticles into polyvinylidene fluoride polymer matrix using dimethyl sulfoxide as the solvent. Hot-pressing the mixtures produces a thin 0-3 composite substrate that exhibits flexibility and optimum dielectric properties. The substrate material properties are studied by conducting FTIR scanning. Delay-line IDTs are incorporated on the surface of the substrate by a conventional photolithographic technique. With the sensor fabricated, RF signals are passed onto the device through the input transducer generating Rayleigh waves. The transmission and reflection characteristics of the device is determined through the S-parameter reading obtained using a network analyzer. This paper discusses about the development process of a flexible piezocomposite SAW sensor.

This paper presents the realization of antenna sensors on flexible textile substrates for bio-impedance measurements. A T-resonator technique was employed to characterize the dielectric constant of various textile materials and to evaluate how the human tissue, functioning as the superstrate, influence the effective dielectric constant. A single frequency antenna sensor was designed based on the measured dielectric constant and the effect of tissue on the antenna resonant frequency was characterized. Monitoring perspiration during exercise using the antenna sensor as well as wireless interrogation of the antenna sensor, when worn by a volunteer, was demonstrated. A rapid-prototyping technique for fabricating the T-resonator and the antenna sensor from conductive fabric or copper films is also presented.

Piezoresistive nanocomposites hold incredible potential for structural health monitoring (SHM). The electrical conductivity of these materials is influenced by strain, making them self-sensing. Electrical impedance tomography (EIT) is a low-cost, non-invasive method of imaging the internal conductivity distribution of a domain. To date, EIT has most often been used to detect damage. However, localizing incipient damage for failure prediction may situationally be a more useful capability from a SHM perspective. Herein, we explore the potential of EIT to identify stress concentration-induced conductivity changes in piezoresistive nanocomposites. First, a nanocomposite specimen with a circular hole is manufactured. Next, displacements are applied in small increments and boundary voltage data is collected after each increment of displacement such that conductivity images can be produced as the stress concentration intensifies. These results demonstrate that the proposed approach allows for accurate spatial localization of stress concentrations in deformed nanocomposites via EIT-imaged conductivity changes and therefore has potential to enable greatly advanced failure prediction capabilities.

Track geometry is one of the most important health indices in the maintenance of rail tracks. Visual inspection and inspection using a track-geometry car are two common approaches to inspect track geometry. Presently, using accelerations from in-service trains has become a popular track inspection approach, because it is a low-cost way to monitor the rail tracks more frequently. However, due to the noise presented in the collected accelerations, detecting anomalies using manually designed features often results in many false alarms. In this paper, we propose a learning-based anomaly detection approach for monitoring the longitude elevation of track geometry from the dynamic response of an in-service train. We consider the track geometry with a sudden change as an anomaly, measured by the signal energy of slopes of the track geometry. The proposed approach uses a variational autoencoder (VAE) to detect the anomaly. The VAE takes accelerations as input and learns a mapping from the frequency-domain representation of acceleration signals to a low-dimensional latent space that represents the distribution of the observed data. The reconstruction probability, which measures the variability of the distribution of the input data, is used as an anomaly score for indicating how well the input follows the normal pattern. Compared to distance- and density-based anomaly detection methods, such as K-nearest neighbor and clustering, the VAE-based anomaly detection is robust to measurement noise and prevents overfitting because it captures the underlying distribution of the data in a low-dimensional space. Furthermore, the VAE-based method does not require model-specific thresholds for detecting anomalies because it uses a probabilistic measurement instead of reconstruction error as the anomaly score. We validate the proposed VAE-based approach on the vibration dataset from an in-service train. We show that this approach outperforms a baseline model (an autoencoder-based anomaly detection method) in terms of recall, precision, and F1-score. The proposed method also successfully addresses the overfitting problem presenting in recurrent neural network-based methods. The results make the proposed approach a strong candidate for low-cost and frequent track geometry inspection.

Mobile crowd sensing (MCS) enables many smart city applications (e.g., transportation monitoring/management, environmental monitoring, etc.). Recently, MCS systems built on non-dedicated vehicular platforms like taxis have become popular due to their large-scale coverage and low-cost deployment and maintenance. However, the goal of MCS may be inconsistent with the goal of vehicles. For example, MCS expects to get large and balanced sensing coverage over the city, while the taxis gather in busy areas to search for new ride requests. This inconsistency between the goals of MCS and vehicles results in a low sensing coverage and decreases the quality of the collected information. To address this inconsistency and optimize the sensing coverage, this paper presents an incentivizing system to optimize the sensing coverage of the sampled data. Key challenges to resolving this inconsistency include limited budget constraining the ability to incentivize more vehicles and complicate vehicle and trajectory selection problem making it difficult to obtain the incentivizing strategy. To address these challenges, we design a customized incentive by combining monetary incentives and potential ride request at the destination to reduce the cost of incentivizing vehicles and utilize the budget efficiently. Meanwhile, we formulate the problem of incentivizing trajectory planning as a non-linear multiple-choice knapsack problem, and propose a heuristic algorithm to approximate the optimal incentivizing strategy. The experiments based on the real-world data show that our system achieves up to 26.99% improvement in the sensing coverage compared to benchmark methods.

Recently, sleep evaluation has attracted lots of attention as sleep disorders have become a public health problem that causes cumulative effect on physical and mental health. Poor sleep qualities can lead to adverse effects on performance of basic activities in normal life such as memorization, concertation and learning especially among elderlies. Sleep posture is one of the keys factors that evaluates sleep qualities to prevent medical conditions such as pressure ulcer formation. In this paper, a sleep monitoring system is developed based on an infrared array sensor to detect different sleep postures. Motion and presence detections are performed and achieved an accuracy over 90%. And eleven different postures can be successfully identified with an accuracy above 95% in controlled lab environment. As the infrared array sensor has the unique property of non-contact, non-privacy invasion and passive sensing, it provides an unobtrusive, low cost and convenient method of long-term sleep monitoring.

Integrating the interest of dancers in monitoring dance quality with the interest of engineers in monitoring loads and vibrations, this research studies floor vibrations induced by human activity while classifying and characterizing dances simultaneously. This research uses smart sensing capabilities with readily available low-cost Arduino sensors equipped with accelerometers. The paper describes the procedures to extract step features from the signal to categorically classify different dance steps. The major contribution of this work is to demonstrate that structural vibration can be used to classify dance steps and provide meaningful information about the harmony of the dances. The conclusion of this research accomplishes that using this new Cyber-Physical Systems (CPS), dancers’ performance can be objectively classified using floor vibration data.

Osseointegrated prostheses (OIP) are an alternative to traditional socket-based prostheses, since amputees can experience unrestricted ranges of motion and improved sensory feedback. However, the risk of infection at the tissue-OIP interface is very high. Subcutaneous infections, if undetected at an early stage, can result in prosthesis loosening, bone fracture, and OIP mechanical failure. In order to avoid such issues, most of the current OIP monitoring techniques depend on physician inspection or by conventional imaging techniques. Recent studies showed that electrical capacitance tomography (ECT), which is inherently noncontact and does not require radiation, could be potentially used for imaging OIPs. In addition, embedded passive nanocomposites interrogated by ECT could reveal pH changes indicative of infection. However, the ECT images suffer from limited resolution. Thus, this study investigated a rotational ECT (RECT) system in which the ECT electrode array is rotated with respect to the central axis of the cylindrical electrode array. It was found that RECT was capable of producing better quality images as a result of an increased number of independent boundary measurements. Furthermore, a limited region tomography algorithm (LRT) was also developed and implemented to image only the region corresponding to the OIP-tissue interface. By limiting the region of interest, the ill-posed nature of the ECT inverse problem can be significantly reduced, which results in an enhanced resolution of the reconstructed images. The advantages of such a combined RECT-LRT system was first investigated using numerical simulations. Second, to demonstrate proof-of-concept, a RECT was designed a and tested in the laboratory. Several image quality metrics were calculated to critically evaluate the RECT-LRT images as compared to conventional ECT images. The results showed that the RECT-LRT system was able to generate ECT permittivity maps with higher resolution.

Strabismus is an eye movement disorder that the eyes do not properly align with each other when looking at an object. This disorder is usually caused by muscle malfunctions, nerve problems or injuries. Currently, the ophthalmic prism with two nonparallel planes is used to diagnose the strabismus angle. The light into one eye is refracted when passing through the prism, which adjusts both eyes to looking forward. The strabismus angle is then identified after checking the parameter of the prism. However, the whole process is operated depending on the doctors’ experience which shows somewhat low efficiency and low accuracy. In this study, an automated strabismus diagnosis technique using VR device is developed. A specially-designed VR is built to simulate the normal strabismus diagnosis steps, in which screens are controlled to change alternately between on and off. The eye motions are tracked by two IR cameras by an image-processing based pupil tracking technique. After tracking the motion of the pupil, the position information is converted to the strabismus angle by considering the eyeball diameter. With this process, the strabismus angle is accurately and automatically identified using a unique feature recognition technique. To demonstrate the performance of this technique, experiments are carried out on various persons, including strabismus patients. The results are compared to the doctor’s diagnosis. The results show that this technique could identify the strabismus angle with high accuracy and high efficiency.

In this study, an online residual fatigue life estimation (prognosis) technique based on nonlinear ultrasonic modulation is developed for a welded joint. When two ultrasonic input signals are applied to a structure at two distinct frequencies, resulting crack opening-and-closing generates modulation components at the summation and difference of the two input frequencies. For prognosis, a fatigue index is defined and extracted from nonlinear ultrasonic modulation components obtained from a target structure. Based on Nazarov-Sutin and Paris-Erdogan theories, the fatigue index is formulated as a simple power function of the loading cycles applied to the target structure. Next, a residual fatigue life is estimated by fitting a power function to the ultrasonic modulation data obtained up to the current loading cycles and extrapolating the fitted power function to the failure point. Finally, the proposed prognosis technique is validated using test data obtained from aluminum (6061-T6) plate specimens with a welded joint. The uniqueness of this paper lies in (1) definition of a fatigue index based on nonlinear ultrasonic modulation components, (2) theoretical formulation of the fatigue index as a power function of loading cycles, (3) online fatigue crack prognosis based on nonlinear ultrasonic measurements, and (4) experimental validation using welded aluminum plates under cyclic loading.

Structural Health Monitoring (SHM) is generally presented as a powerful tool that allows bridge managers to make decisions on maintenance, reconstruction and repair of their assets. The benefit of SHM can be properly quantified using the concept of Value of Information (VoI), i.e. the difference between the utilities of operating the structure with and without the monitoring system. In calculating the VoI, two understood assumptions are that all decisions concerning system installation and operation are taken by the same rational agent and that “information never hurts”, i.e. VoI is guaranteed to be non-negative. However, in the real world, the individual who decides on buying a monitoring system, the owner, is often not the same individual, the manager, who will use it once the system has been installed, and they may be-have differently because of their different risk aversion. We develop a formulation to properly evaluate the VoI from the owner perspective, when the manager is a different individual. We demonstrate that in a decision-making process where the two individuals involved share exactly the same information, but behave differently, the VoI can be negative and that we can always find a combination of prior probabilities and utility functions which ultimately yields a negative conditional VoI. Indeed, even if the two agents have an agreement a priori, due to their different behaviors, their optimal actions can diverge after the installation of the monitoring system. We apply this formulation on a real-life case study concerning the Streicker Bridge (NJ, USA).

This paper presents our recent application of Brillouin-based optical fiber sensors for geotechnical monitoring. We embed an optical fiber into a prestressing steel strand (hereafter ‘strand’) during the manufacturing process, where the epoxy-coated strand is fully integrated with the optical fiber without sacrificing long-life durability. We install the strand to prestressed ground anchors for the stabilization of steep slopes. The experimental results indicate that the sensorembedded strand can detect changes in the loaded force on the anchor and can determine the cause of such changes. As a field application, we insert the single strand into a drilling hole and grout it to the anchor. After setting a hydraulic jack, we remove the epoxy coating at the end of the strand and pull out the embedded optical fiber. Then, an optical connector is attached using a fusion splicer for connecting an optical measurement instrument. The strain distribution along the anchor is measured during load testing and after anchoring. As a result, the stress distribution along the anchor is accurately measured in situ during construction and thus, these sensors have the potential to monitor soil conditions.

Serious damaged structures can result in catastrophic disasters during earthquakes. However, visual inspection of damage in structures by human is an inefficient and unreliable approach. Alternatively, a more scientific approach should be exploited to rapidly and accurately localize damage of structures. In this study, two damage detection methods based on prediction errors using a bank of Kalman estimators are presented and compared including a) a centralized approach and b) a decentralized approach. In the centralized approach, a representative model of a building is first derived from a frequency-domain system identification method under ambient vibration prior to earthquake events. This model is then converted into a bank of Kalman estimators, and the estimation errors can be calculated and then turned into statistics. The damage location, level, and time of occurrence can be statistically determined and presented by the damage indices. Similarly, in the decentralized approach, the same system identification method is first applied to structural responses. To be more realistic, the monitoring system is decentralized into subsystems with some overlapped sensor measurements. Banks of Kalman estimators can be constructed using the subsystems. By normalizing the damage probability indices from prediction errors of each bank, the damage location, level, and time of occurrence can be identified. A numerical example is given to demonstrate the two damage detection methods. Moreover, the two methods are compared by a scaled twintower building using shake table testing. The results indicate that both methods are quite effective for seismic damage detection.

Nuclear pipe leakage is the major problem in nuclear plants operation¹.

Nondestructive test of nuclear pipes in situ under high level radiation is the one of the challenging projects in nuclear engineering. The high level nuclear radiation is dangerous for human and traditional NDT methods of nuclear pipe monitoring in real time in situ make them practically impossible².

Proposed advanced laser sensor and technology which allow to detect corrosion, mechanical stress and defects which can prevent nuclear fuel failure caused by mechanical and physical-chemical aspects of nuclear reactor components under high level nuclear radiation.

Sensor operated remotely by laser light and does not require electrical wires for connection with electrical sources.

Advantages of proposed technology: NDT–nondestructive test, contactless, wireless, remote, real time operation under high level of nuclear radiation are discussed and demonstrated.

Use of Shape Memory Alloys (SMA) is more and more frequent in engineering because of their unique properties of completely recovering the imposed deformations after heating, or automatically returning to the unloaded configuration via a super-elastic process after very large strains (till around 10%). The process is regulated by a phase change in the material, shifting between martensite and austenite. Along this transformation, some SMA change their elastic properties by a factor three and damping coefficient by a magnitude. Super-elastic materials exhibit stable hysteresis loops under cyclic loading and dissipate energy without residual deformation thus providing in perspective self-centering capability for use in buildings earthquake protection. The present study investigates the performance of SMA-based devices for seismic protection of reinforced concrete structures. In countries with high seismic hazard, vulnerability assessment of existing constructions and seismic retrofit implementation is a major challenge for both scientific community and public administration. This paper illustrates seismic retrofit of an existing school building in Italy, using dissipating steel braces. Both SMA-wire dampers and mixed devices combining SMA elements and classical buckling-restrained axial dampers are considered for seismic upgrading. Adopted technique effectiveness and reliability are investigated by comprehensive nonlinear static and dynamic analyses. Numerical results show that super-elastic SMA dampers are effective for mitigating building response to strong earthquakes and providing systems self-centering capability with negligible residual strains.

A novel theoretical model for a laminate cantilever beam consisting of numerous superelastic shape memory alloy (SMA) layers, based on the ZM model and Timoshenko theory is introduced. The mathematical equations are first developed to predict and describe the internal material structure of laminated beam, according to the solid phase transformation in SMA layers. Then, the theoretical expression of the moment and shear force for a superelastic SMA composite cantilever beam is derived. The proposed model is validated against a 3D finite element analysis model (FEA), giving very good agreement in each case. The moment-curvature response, and distribution of martensite volume fraction and axial stress along the beam length are investigated.

Nanofiller-modified materials have received enormous attention from the structural health monitoring (SHM) research community because they are self-sensing via the piezoresistive effect. To date, considerable effort has been dedicated to understanding the fundamental mechanisms of nanocomposite conductivity and piezoresistivity via microscale percolation models. However, nanocomposites also possess complex, frequency-dependent electrical properties. This has received much less attention with prevailing approaches only modeling the net input-output response at the macroscale as an equivalent resistor-capacitor (and sometimes inductor) or RC(L) circuit. To truly understand the underlying mechanisms of complex impedance in nanocomposites, more sophisticated models capable of accounting for nanofillerto-nanofiller interactions are needed. To address this, a microscale percolation model for complex impedance is herein developed by introducing capacitive coupling at nanofiller-to-nanofiller junctions. This model is then calibrated against experimental data for carbon nanofiber (CNF)-modified epoxy resulting in very accurate model-to-experiment correspondence. An important insight from this work is that experimental data can only be fit by allowing for capacitive coupling beyond the electron tunneling cutoff distance typically associated with piezoresistive nanocomposites.

Positive train control (PTC) is a system that combines sensors, communications, and actuators to stop a train automatically before certain types of accidents occur. The PTC implementation requirements are part of a 2008 congressional mandate. Information about PTC benefits and costs are from reports between 2004 and 2010. Since then, the complexities and rush to comply with the mandated installation deadline of December 2018 has led to a scarcity of updated information about actual costs and anticipated benefits. The purpose of this study is to share a current understanding of the potential benefits and costs of PTC deployments. The finding is that the Class I railroads collectively incurred an estimated average cost of $224,738 per route mile. Analysis of safety data for the five years preceding the 2018 mandate found that accidents due to human factors or failures in signaling and communication systems accounted for 31.6% of the aggregated financial loss. If PTC could have prevented those accidents, then direct benefits to Class I railroads would have averaged $92 million annually. These results indicate that it is unlikely that the savings from accident prevention alone would offset the costs of PTC deployment and its annual maintenance needs. However, the installed infrastructure of sensor interfaces and wireless networks provides opportunities to realize additional benefits from incremental investments in other systems. Those benefits could include rail-grade safety sensors and business enhancements such as improved line capacity, service reliability, equipment utilization, real-time diagnostics, fuel savings, and non-destructive evaluations.

The structures are coupled with smart technologies such as hybrid type damper demands appropriate control and monitoring scheme as generally show quite complex nonlinear behavior. Additionally, the structures are vulnerable due to environmental changes as well as for inherent nonlinearities of the used materials/devices. Therefore, dealing with various uncertainties are unavoidable in order to monitor any dynamical system. To do this end, the unscented Kalman filter (UKF) is employed and the structures are assumed to be accompanied with various uncertainties such as structural properties are corrupted, sensor noise, controller parameters. Further, it is also investigated that the complexity of monitoring of structural systems due to the uncertainty of initial covariance, especially, when the structural properties e.g. damping components are flawed. In order to perform numerical investigations an eight degree of freedom system is adopted and the viscous damping (VD) and negative stiffness (NS) and only with negative stiffness (WNS) schemes are employed to estimate the control force of virtual hybrid type control system. The investigated approach is capable of handling diverse uncertainties in addition to the process and measurement noise. The outcome will guide to evaluate the conditions (e.g. structural health) of erroneous system in a non-destructive manner by analyzing measured signals.

Civil structures, such as buildings and bridges, are constantly at risk of failure due to extensive environmental loads caused by earthquakes or strong winds. In order to minimize this risk, the application of control systems for civil infrastructure stabilization has been proposed. However, implementation challenges including communication latencies, computation inundation at the actuation node, and data loss have been impeding large-scale deployment. In order to overcome many of these challenges, inspiration can be drawn from the signal processing techniques employed by the biological central nervous system. This work uses a bio-inspired wireless sensor node, capable of real-time frequency decomposition, to simplify computations at an actuating node, thus alleviating both communication and computation inundation and enabling real-time control. The simplistic control law becomes 𝐅 = 𝐰𝐍, where 𝐅 is the control force to be applied, 𝐰 is a weighting matrix that is specific to the structure, and 𝐍 is the displacement data from the wireless sensor node. There is no empirical solution for deriving the optimal weighting matrix, 𝐰, and in this study the particle swarm optimization technique was used as a means for determining values for this matrix. Multiple parameters of this optimization method were explored in order to produce the most effective control. This bio-inspired approach was applied in simulation to a five story benchmark structure and using performance metrics it was concluded that this method performed similar to more traditional control method.

Based on the concept of “functional integration”, a novel integrated semi-active shock absorber with a magnetorheological (MR) inerter with tunable inertance, a MR damper with tunable damping and a coil spring, is proposed and investigated in this paper. The MR inerter mainly consists of a MR clutch, a ball-screw set, a flywheel, excitation coils and MR fluids. Inertance adjustment of the MR inerter can be realized through control of the duty cycle of the excitation current by pulse width modulation (PWM). The dynamic models of the semi-active shock absorber and the MR inerter are established based on MATLAB/SimMechanics, and the mechanical characteristics of the two are simulated. Moreover, the effectiveness of the PWM control for the inertance adjustment is verified. The mechanical behaviors of the semi-active shock absorber with different inertances and damping coefficients are obtained and analyzed.

We demonstrate the application of a novel type of distributed fiber optic sensors (DFOSs) to dynamically monitor the effects of wind on solar tracker structures used in photovoltaic power stations. This DFOS is based on the stimulated Brillouin scattering nonlinear optical effect in optical fiber, which can be used to measure the distribution of strain and temperature along a given structure. However, contrary to existing solutions, the sensor provides dynamic real-time measurements with hundreds or even thousands of full simultaneous measurements for all positions in the fiber each second. Moreover, high-precision and high spatial resolution are obtained. This so-called dynamic Brillouin optical time-domain analysis (D-BOTDA) sensor provides real-time monitoring of the bending and torsion of the structure of solar trackers in response to wind load. This helps the solar tracker manufacturer asses and improve the mechanical designs so as to introduce corrective measures and develop cost-effective components that properly withstand the effects of wind at any given location. We experimentally demonstrate the application of a D-BOTDA sensing system to measure distributed bending and, for the first time to our knowledge, also distributed torsion along the stressed beam of the solar tracker. For this purpose, we have developed a procedure to instrument the torsion beam with two optical sensing fibers that are fixed helically wound along the beam in opposite directions, so that any common-mode thermal or bending effects are removed. We initially performed tests in a laboratory facility in which sections of the torsion beam could be subjected to controlled moments. Static and dynamic loads were applied and the measured deformations were compared to those obtained with fiber Bragg gratings, which just provide point measurements of strain. In both cases, full agreement was demonstrated. Finally, the system was installed in an operational solar park.

An embedded distributed optical fiber sensor (DOFS) based on Rayleigh scattering is used to measure the localized thermal response of a carbon fiber/epoxy composite to directed energy. The thermal impulse from a laser strike is detected while the composite is subjected to cyclic mechanical strain. The ultimate goal is rapid detection of directed energy on the surface of the composite. Prior research demonstrated the use of distributed optical fiber sensors embedded in carbon fiber/epoxy composite structures to rapidly detect temperature changes approaching 1000℃ during a high energy laser strike. However, swept wavelength interferometry used to interrogate a DOFS uses shifts in frequency which can be caused by both changes in temperature and strain. Hence, strain in the composite resulting from mechanical loading is also detected by the embedded sensor and can interfere with rapid detection and measurement of a localized thermal response. Initial tests have demonstrated the simultaneous response of the DOFS to both temperature and strain. A sensing network has been designed to mitigate the response of the sensor to mechanically applied strains, and a simple signal processing technique has been used to cancel the response of the sensor to bending strain while enhancing the thermal response when directed energy is incident on the composite surface. Additional testing indicates that the network and processing technique can also be used to isolate localized impacts on the composite surface from bending strain.

Loads applied transversely on the external surface of waveguides change their circular cross-sectional geometry generating birefringence. Due to this effect the reflected spectrum of a Fibre Bragg grating (FBG) undergoes a splitting of the single peak of the Bragg wavelength. In this work, we employed the Transfer Matrix Method (TMM) for x- and y-polarized wave-modes to model the uniform FBG reflection spectra for uniform and non-uniform transverse loads. We also performed experimental measurements for two different transverse load scenarios. The load profiles chosen for these experiments were applied on the FBG sensor through a block of steel and a roll bearing pin. Then, the modelled and experimental results were compared resulting in good agreement of 85% (on average). Finally, during the roll bearing pin loading test, different responses were observed depending how the FBGs were surface mounted. To investigate this, the glue layer influence on the reflected spectrum was further studied experimentally.

Composite constructions are indispensable in current and future society. Fiber Bragg Gratings (FBGs) embedded in composite need to be carefully aligned with the material fibers to reduce inhomogeneous lateral load exerted onto the FBG which occurs due to the inhomogeneous nature of composite materials. Inhomogeneous load causes distortion of the reflection spectrum. We proposed to solve the FBG spectral distortion by incorporating a dedicated design structure inside the optical fiber. This allows the FBG to sense the strain in the axial direction accurately regardless of the optical fiber alignment with respect to the composite matrix. In this paper, the basic design will be discussed and the results of the first prototype of this structured fiber will be presented. Prototype FBGs are embedded in different composite samples of various thicknesses and materials (glass or carbon fiber based). The spectrum before and after curing is measured and direct comparisons are performed with embedded standard commercial FBG to verify the improvement. Effects of depth of the embedding and FBG direction with respect to the composite material fiber are investigated. Bending and tension tests are performed to ensure the special FBG in the structured fiber has the directional sensitivity to the strain applied. During all experiments, the special FBG is found to have a better or comparable spectrum than the standard FBGs. The improvement varies for the different tests. This can be caused by the unknown orientation of the structure inside the fiber. According to the first FEM analysis, this affects the effectiveness depending on the detail design of the structure. Information of the FEM analysis will be used to further optimize the design and for the development of a prototype.

Over the years, many shape sensing methods have been developed with technologies including optical fibre, PZT and fringe projection. Among them, optical fibres have gained a lot of attention due to their unobtrusive nature when either surface mounted or embedded in the structure. Optical fibre Bragg gratings (FBG) are currently employed for structural health monitoring in civil and aerospace systems and their shape sensing capabilities have been previously reported. In this paper, we propose a novel fibre optic based shape sensor of an isotropic cantilever beam based on the principles of interferometry and FBG sensing. The method described in this paper uses a standard single core single-mode optical fibre and the least number of sensors to estimate the shape, making it comparatively an inexpensive sensing method. On displacing the beam with an unknown magnitude and at an unknown location along the beam, we are able to demonstrate that we can measure the shape of the displaced beam and the magnitude and location of the force applied. The analysis involves using a calibration method and an iterative calculation to measure the two unknowns. An analytical model based on the known beam theories was used to assess the accuracy of the measurements. The preliminary analysis yielded an accuracy of ±1 mm and ±50 mm for the displacement and location, respectively.

Electromechanical impedance (EMI) technique coupled with the piezoelectric sensor has been explored as a promising non-destructive testing (NDT) method to determine the quality of cementitious materials. Among the piezoelectric sensors, Lead Zirconate Titanate (PZT) is the most commonly used piezo-ceramic materials for conducting the EMI because of the high sensitivity and high piezoelectric constant. However, the inherent brittleness of PZT limited their potential application as embedded sensors for in-situ monitoring of materials/structure properties. To improve the durability and flexibility of the PZT sensor, we have systematically investigated the effect of different polymer coating methods of PZT on their sensitivity and effectiveness of EMI sensors. Specifically, two polymer coating agents were used to encapsulate the PZT sensor including the flexible organic polymer compound - Polydimethylsiloxane (PDMS) and the rigid polymer compound- Polyepoxides (Epoxy) We have also investigated the feasibility of EMI technique with the surface bonded sensor and embedded sensor to test the compressive strength gain of mortars. The compressive strength test of samples was also conducted using conventional mechanical methods per ASTM C109 as a baseline at the first 5th to 8th hour. The EMI signatures were collected for all the samples at the same time. Our preliminary results have shown a high correlation of EMI methods’ results with the compressive testing results for two different polymers coated PZT sensors, which indicated the polymer-coated PZT sensors can be used as an effective NDT method for in-situ monitoring of concrete compressive strength gain at the very early age.

In concrete pavements, one of the main deteriorations and damages is tensile cracking, which can destroy concrete pavement frame when sub surface cracks propagate to the surface because it induces water penetration in pavement structure and foundation. However, detecting the propagation, especially the crack length, of hidden cracks inside pavements is very challenging. This paper presents the results of an experimental investigation conducted to detect crack length in concrete using point strain sensors in the bottom of the simulated pavements in a bending test with four-point loading. The linear elastic fracture mechanics is used in this study to calculate the crack lengths based on the collected data from point sensors. Results for the crack length detection in experiments showed a measurement accuracy of 88.85%, 87.7%, and 81.08 % for the three specimens tested, respectively. This study provides an alternative technique to detect hidden bottom-up cracks in concrete pavements.

Progressive damage to any structure causing the decrement in material strength when subjected to alternate cyclic loading condition is termed as fatigue. The present work deals with numerically assessing the life of concrete frame under the influence of flexural fatigue load using ANSYS Mechanical computer program. Stress life analysis is considered for analyzing the life and fracture parameters of two broadly classified 3-D solid models (1) without edge notch, and (2) with edge notch at critical locations for the given loads in concrete frame. Further the notch opening width to depth ratio (a/d) has been varied to understand the effect of crack propagation onto the fatigue life and stress intensities build up in the concrete models. Six PZT-5H sensor patches were modelled on the frame surface with glue interface at different symmetrical locations. The PZT output in form of admittance signatures is gathered and analysed for different damaged states to develop a mathematical model that relates these variation with respect to stiffness loss of the concrete frame. Further the results revealed the degrading trend of fatigue life and growth of alternating stresses with increase in magnitude of applied cyclic loads and flexural stiffness losses. Plots for variation of maximum directional deformation and equivalent von-mises stress set up in the model are devised relative to applied sinusoidal cyclic accelerations and are discussed briefly along with describing the advantages of using PZT transducers for structural health monitoring applications in concrete framed structures under the action of low-strain loads causing high cycle fatigue.

MEMS accelerometers are widely employed in the Internet of Things (IoT) era. Among them, capacitive types are commonly used due to their low cost and compatibility with the commercial CMOS fabrication lines. However, piezoelectric MEMS accelerometers have great research popularity attributed to their wide working range, self-generating property and removal of the need for vacuum sealing. This study designs, fabricates and analyzes a piezoelectriccantilever- beam-based accelerometer in meso scale, which is constructed by a tungsten proof mass and a composite beam comprising of PZT and stainless steel layers. Four structures with different geometries/dimensions are designed for comparison, including rectangular and trapezoid beam shapes. All the devices are fabricated by MEMS processes where aerosol deposition is utilized to make high-quality PZT sensing layer. And the implementation of stainless steel substrate makes the fabrication flow simple and cost-effective. Experiments show that the natural frequencies of the four structures range from 572.25 Hz to 769.01 Hz, corresponding to respective working frequency range from 110 Hz to 150 Hz. The low frequency limit of 10 Hz is determined by a tailor-designed charge amplifier, which is used to amplify the output charge signal of the developed sensor. At the working frequency of 95 Hz, charge sensitivities of 23.9 pC/g to 41.4 pC/g are measured for the four structures. Comparison with other studies, the designed devices have high sensitivities.

A MEMS sensor dedicated to SHM applications is presented. The MEMS is made of a Capacitive Micromachined Ultrasonic Transducer (CMUT) chip composed of circular membranes array. The radius of the membranes varies between 50 μm and 250 μm and hence the associated resonance frequencies between 80 kHz and 2 MHz. A wide frequency bandwidth is then available for acoustic measurements. A testing campaign is conducted in order to characterize the MEMS sensor's behavior when subjected to single-frequency and broadband excitation stimuli. The single-frequency excitations are produced with specific piezoelectric transducers from 300 kHz to 800 kHz. The Fast Fourier Transform (FFT) of the measured signal from the CMUT is centered as expected on the excitation frequency. The broadband excitation is obtained with a pencil lead break. In this case, the FFT of the measured signal is centered on the resonance frequency of the membrane. These characterizations point out the DC bias voltage applied to the CMUT as a major parameter for controlling the sensitivity of the sensor. The CMUT sensor proves to be sufficiently sensitive to monitor these sources. This work highlights the relevant prospective capacities of the CMUT sensor to collect data in structural health monitoring applications. This sensor technology could be externally deployed, or even integrated into a composite structure, in order to monitor the structure by the CMUT detection, either by active ultrasound tests or by passive acoustic emission.

There is a great desire for high-performance and low-cost uncooled infrared (IR) detectors that can survive in harsh environments. To address this need, we are investigating the use of MEMS piezoelectric resonator technology using Aluminum Nitride (AlN) films as a resonating detector. A novel design of an AlN IR sensor based on piezoelectric bending resonator was analyzed and showed that the piezoelectric resonators have the potential to be used as a core element for highly sensitive, low-noise, and low-power uncooled IR detectors [1]. A critical feature of the design that determines the sensing resolution is the minimization of the various loss mechanisms of the resonator. A major loss mechanism for a non-vacuum packaged chip is the acoustic radiation loss in Earth’s atmosphere (1 atm) due to the large stroke of the bending resonators. The acoustic radiation loss is the dominant Q limiting mechanism. We propose the use of perforated plates for the bending resonators in order to reduce acoustic radiation loss. The effectiveness of the loss reduction and the performances of the resonant IR sensors are analyzed using finite element simulations. The results are encouraging for the application of these types of IR sensors under the Earth surface atmosphere conditions.

Advanced composite materials have been integrated extensively into aircraft structures and have been emerged in civil infrastructure (e.g. bridges), in recent years. Composite materials are prone to initiation of hidden damage which makes it vital to detect damage at its onset. In this paper, first we introduce a procedure to fabricate structural carbon fiber reinforced polymer (CFRP) sheets at the university laboratory. Then, two structural health monitoring (SHM) systems will be developed to assess and monitor the performance of CFRP materials. The first SHM system is based on installing piezoelectric MFC sensors on CFRP sheets. These sensors are using guided lamb waves to detect possible damage in composites at its onset. The second SHM system will be developed using Fiber Bragg Grating (FBG) technology. This system will acquire signals from a fiber optic thermocouple and a fiber optic strain gauge. The damage will be introduced into CFRP sheets with an impact hammer. The damage detection will be performed by the two SHM systems for different damage severities. The pros and cons of each SHM system in composite damage detection will be investigated and the recommendations will be made for utilizing each system in real world composite applications.

Development of indigenous CO₂ sensing element operated at an extreme operating temperature (≥ 350 °C) is highly desirable. Use of langasite based surface acoustic wave (SAW) gas sensors is extremely advantageous compared to other commercially available sensors as it can operate at a higher temperature (> 300 °C). Only a few literature reports exist which demonstrated a high-temperature CO₂ gas sensor. In the present study, we have demonstrated CO₂ sensing properties using langasite based SAW sensors. The temperature-dependent gas sensing characteristics have been determined by observing frequency shift due to the adsorption of gas molecules.

This paper presents the design and testing of a new CO2 gas sensor that is based on a piezoelectric Langasite (La3Ga5SiO14) crystal resonator with temperature compensation. CO2 gas concentration change can be measured by monitoring frequency shift due to the adsorption of CO2 gas molecules. The sensor is designed and fabricated as a body acoustic wave (BAW) resonator, and coated with Zinc Oxide thin film for sensitivity improvement. Then it is experimentally tested in the laboratory with a self-designed, temperature controlled gas chamber at a wide CO2 concentration range. Moreover, temperature compensation is formulated and tested by applying the dual mode behavior of the Langasite BAW resonator. The sensor shows a good relationship between CO2 gas concentration and its resonant frequency shift. The proposed sensor can be applied at high temperatures particularly in combustion engines, power plant and other high temperature applications.

Recent advances in sensing are empowering the deployment of inexpensive dense sensor networks (DSNs) to conduct structural health monitoring (SHM) on large-scale structural and mechanical systems. There is a need to develop methodologies to facilitate the validation of these DSNs. Such methodologies could yield better designs of DSNs, enabling faster and more accurate monitoring of states for enhancing SHM. This paper investigates a model-assisted approach to validate a DSN of strain gauges under uncertainty. First, an approximate physical representation of the system, termed the physics-driven surrogate, is created based on the sensor network configuration. The representation consists of a state-space model, coupled with an adaptive mechanism based on sliding mode theory, to update the stiffness matrix to best match the measured responses, assuming knowledge of the mass matrix and damping parameters. Second, the physics-driven surrogate model is used to conduct a series of numerical simulations to map damages of interest to relevant features extracted from the synthetic signals that integrate uncertainties propagating through the physical representation. The capacity of the algorithm at detecting and localizing damages is quantified through probability of detection (POD) maps. It follows that such POD maps provide a direct quantification of the DSNs’ capability at conducting its SHM task. The proposed approach is demonstrated using numerical simulations on a cantilevered plate elastically restrained at the root equipped with strain gauges, where the damage of interest is a change in the root’s bending rigidity.

We present the design, fabrication, and testing of stretchable pressure sensing membranes. Two sensing techniques are demonstrated: resistive and capacitive. Both designs are incorporated in 400μm-thick films and are fabricated with thin film application of silicone and stencil/mask deposition of conductive materials. The resistive sensor utilizes room temperature liquid metal while the capacitive sensor utilizes multi-walled carbon nanotubes. Tests are performed with 18mm-diameter samples of each. Point load tests and acoustic response in an impedance tube provide feedback on sensor performance. The resistive sensor demonstrates a sensitivity of 0.045Ω/mm, and the sensor’s response has been characterized for in the 30Hz to 10kHz range with varying degrees of sensitivity. The capacitive sensor has a small point-load-deflection sensitivity ranging from 0.018pF/mm to 0.044pF/mm depending on capacitor diameter. Acoustic response are shown for 5Hz to 40 Hz, limited by external electronics. These devices are progress towards developing sensor networks capable of tracking aqueous turbulence.

The ability to measure, monitor, and prevent catastrophic failure has made structural health monitoring crucial for aerospace, civil, and marine structures. Spatial strain sensing is necessary for quantifying distributed damage in structural systems. Previous studied that coupled electrical impedance tomography (EIT) algorithms with piezoresistive coatings opened up vast opportunities for distributed strain sensing. However, these approaches could not extract strain directionalities from the reconstructed EIT conductivity maps, and sensing resolution remained rather low. Therefore, this study aims to develop next-generation “sensing meshes” capable of resolving both spatial strain magnitudes and directionalities for distributed strain field monitoring. The approach is to design and fabricate piezoresistive graphenebased thin films and then patterning them to form a grid or mesh. The nanocomposite grid lines were designed to be of a high-aspect ratio so that each grid element could sense distributed strain along its length and direction. Various sensing mesh specimens were fabricated, and a load frame was employed to strain them in a controlled manner. Similar to conventional EIT, boundary voltage measurements were acquired when electrical current was applied between two boundary electrodes. The boundary voltage dataset was then used as inputs to the EIT inverse algorithm to reconstruct the conductivity distribution of the sensing mesh. For verification, these experimental results were compared to an elastic finite element model subjected to the same strain states. Good agreement between the experimental tests and numerical simulations were observed, thereby demonstrating the potential of this technology for distributed strain field monitoring.

For the past century, developing an understanding of human locomotion has been instrumental in advancing orthopedic medical knowledge and technology. Historically, the field of human gait analysis has relied on force plates to investigate the forces occurring between feet and contacted surfaces. A new thrust in recent years has been to investigate foot contact forces by using specialized force sensing insoles. The medical community has already benefited from initial force sensing insole designs. Despite this technological advancement, the currently existing force sensing insoles are largely “one size fits all.” This presents a challenge for the medical community as an accurate and ergonomic measurement system is not available for patients with special orthopedic needs such as those with flat feet or diabetic ulcers. Introduced here is the potential solution of using soft 3D printed material, called NinjaFlex, to develop custom, ergonomic insoles which possess embedded force sensors for plantar pressure detection. In this paper, groundwork for developing such a custom force sensing insole is laid by investigating the ability to use force sensors embedded into a geometrically simplistic 3D printed structure to detect forces applied to the overall system. Three different force sensors are investigated and their ability to accurately detect force in this configuration is compared. Additionally, a simple model relating sensed force to force applied to the system is developed. The intentions of this work are to verify the feasibility of a custom force sensing insole which further benefits the medical community.

Damage characterization often requires direct sensing due to the localization of the anomalous behavior near the cracks. Direct sensing, however, is expensive because of the need to deploy a dense array of individual sensors. Sensing sheets based on Large Area Electronics (LAE) and Integrated Circuits (ICs) are a novel solution to this problem. Such sensing sheets could span several square meters, with a dense array of strain sensors embedded on a polyimide substrate along with the relevant electronics allowing for direct sensing while keeping the costs low. Current studies on LAE based sensing sheets are limited to laboratory experiments. This paper explores the question of suitability of the sensing sheets as a viable option for real-life SHM based on LAE and ICs. Results of laboratory experiments on an aluminum beam are provided to demonstrate the performance of sensing sheets in ideal conditions. Then, the sensing sheets are employed on a pedestrian bridge already equipped with fiber-optic sensors. The strain measurements from the sensing sheets and the fiber-optic sensors are compared and sources of differences are discussed.

With the advancement of science and technology, SHM application through smart materials from graphene are becoming more popular than ever before. The physics behind graphene sensor has become an area of suitable interest owing to its Non-destructive application in structural components. Smart material sensors being allotrope form of carbon has been basically used for piezoresistive application purposes in composites, environmental monitoring and also in health applications. Here Gnp (graphene nano platelet) sensor has been used for SHM monitoring in structural components. We employed various Non-destructive methods such as SEM spectroscopy, Raman spectroscopy to find out sensor’s characterization in straining action associated with structural components with the help of GNP sensor.

With the continuous development of Computer Vision and a variety of advanced seam imaging equipment, the information contained in the seam image is very rich. It is of great significance for industry automation system. Single image feature is difficult to fully express seam image content. Multi- feature fusion has become a natural way to extract the seam image features. It can comprehensively utilize the seam image information to gain more rapid and accurate understanding of welding images.

From low to high, information fusion can be divided into three levels. The feature-level fusion not only keeps the most original information, but also overcomes the unstable and large characteristics of original data. Fusion feature can be effectively used in seam image recognition.

This paper designs an automatic laser arc hybrid welding system basing on optical sensors. The system has the function of automatic control, which is convenient for setting and adjusting parameters, and the welding attitude control is stable and accurate. To a certain extent, it solves the problems of high requirements and poor adaptability of laser welding system in practical application, and improves the engineering adaptability of laser-arc hybrid welding.

Railroads use train speed measurements to assess operational efficiency and safety. The recent availability of low-cost GPS receivers presents an opportunity for massive cost reduction in monitoring continuously the speed and position of equipment across the entire network. GPS receivers estimate speed from geospatial position updates. However, low-cost GPS receivers can produce relatively large errors in position updates, thereby producing similar errors in speed estimates. Studies tend to focus on characterizing GPS receiver errors in urban road settings. Subsequently, railroads know very little about the nature of GPS errors along rural train routes. Smartphones nowadays have all the necessary sensor capabilities needed to test and validate a low-cost speed monitoring system. This study characterizes speed errors by using multiple smartphones onboard a hi-rail vehicle. The authors describe the data collected, the data processing algorithm developed to estimate speed, and the error quantification by comparing speed estimates to vehicle speedometer measurements reported by the hi-rail vehicle operators.

Morphing aircraft can change external shape in flight according to different flight environments and tasks, and improve flight performance maximumly. Among them, the morphing wing can improve the aerodynamic performance efficiently and has become one of the hot spots in recent years. One of the key technologies for morphing wing is flexible skin technique. Aiming at the conflict between in-plane deformation and out-of-plane bearing capacity of flexible skin structure design, a zero Poisson's ratio hybrid honeycomb structure was designed. The strips are added to the honeycomb structure to form a hybrid honeycomb, which increases the out-of-plane bending stiffness. Three different shapes of honeycomb grid elements were proposed, which are cruciform, square, and H-shaped. By adjusting the shape and size parameters of the three kinds of honeycomb grid elements and the height and quantity of the laying strips, the in-plane deformation mechanism of each element was analyzed by the representative volume element method, as well as the variation of mechanical properties with the element and strip shape parameters. The mechanical properties of the hybrid honeycomb structure were analyzed by finite element simulation. Considering the requirements of the variable camber trailing edge wing, a flexible skin which has capacity of out-of-plane bending resistance was constituted by covering elastic panel over the surface of zero Poisson's ratio hybrid honeycomb. The flexible skin structure has good airtightness and smooth surface. Also, it meets the requirements of in-plane unidirectional deformation along with out-of-plane bearing capacity.

Several examples of control strategies for the seismic protection of civil structures, ranging from passive to active and semi-active have been presented in the scientific literature, and often applied in the market. However, none of them proved to be absent of high employment costs or burdensome installations. For this reason, the low-cost Active Mass Damper (AMD) shown in this work and capable of automated self-tuning, control and continuous monitoring of the structures represents an attractive solution. The device has been designed and tested on the numerical model of a scaled steel made three story building. In particular its working principle and the ISAAC algorithm for automatic identification are presented and its robustness against modelling and estimation errors is analyzed. The methodology allows to avoid the study of specific solutions for each case, thus making possible the adoption of such systems also for already existing and common structures.

In this study, a new pressure seal, which can adjust the magnitude of the yield stress of the chamber containing a magnetorheological (MR) fluid, is investigated. The proposed seal can maintain the required pressure during the rotation of the shaft which may vary due to the friction. This design is based on the field-dependent special characteristics of MR fluid. Specifically, the inherent property of MR fluid changing from the liquid phase to semi-solid phase by applying the magnitude is utilized to achieve this goal. Owing to the semi-solid property of MR fluid under the magnetic field, MR fluid can replace the role of silicon materials in designing seal structure. Due to the high sealing provision, the proposed seal can be applicable to pressure locking, dust- and water-proof, and mating two different pieces. The maximal pressure which can be handled by the proposed is derived and analyzed in each case. The behaviors of fluid inside the housing is simulated and observed through the commercial software. The optimization of seal dimensions are then calculated without the pressure loss in design process.

In this paper, a new skin tissue which can emulate the stiffness of several organs of human being is proposed and analyzed utilizing a magneto-rheological (MR) fluid. It is called MR skin. The proposed skin can be applied to the robot assisted surgery manipulated by the haptic devices as a controllable tactile sensor. In order to formulate the device, the valve networks are embedded inside the structure of the master actuator. These valves use the flow mode and shear mode of MR fluid for the pressure control. The deformation equation of the MR skin is derived and the external force contacting to the MR skin is also analyzed. After formulating, the proposed tactile display is optimized by using the finite element method. In the optimization process, many different forces are applied to view different deformation of MR skin with different pressures. It is shown via the optimization that the results satisfy the initial requirements of the design. This result directly indicates that the proposed MR skin structure is feasible in the manufacturing sense and applicable to haptic devices for robotic surgery.

In this paper, a new magneto-rheological MR) fluid damper is proposed to achieve lower limb exoskeleton of the rehabilitation device. This is achieved by designing the piston configuration as both a square geometry and a circular geometry. By doing this, controllability of the both vibration and moment in the horizontal axis can be obtained. In the design process, two operation modes of MR fluid including flow mode and shear mode are used and the principal design parameters of the square piston are optimized to have low limb exoskeleton as possible under imposed design constraints such as size. This principle is also applied for the circular piston. In addition, the configuration parameters of the design are obtained by optimization using a commercial software as ANSYS ADPL. It is shown through computer simulations that the requirements of the force associated with the limb exoskeleton are successful achieved.

The precise evaluation and improvement of the bearing capacity of actual existing bridges mainly depend on the accurate baseline finite element model (FEM) of structures. The FEM updating technique is an effective way to obtain the accurate FEM of bridges by minimizing the differences between the measured and analytical features of structure. In this study, the FEM updating technique is implemented to obtain the baseline FEM of a practical bridge oriented to the bearing capacity improvement. Firstly, the detailed damages of this actual bridge were investigated by visual inspections, and the static and dynamic structural characteristics were obtained by carrying out the loading tests. Secondly, the updating parameters were determined by considering the practical damages of this bridge. Finally, the baseline FEM of this bridge, considering the stiffness reduction of structure, was obtained by using the measured structural characteristics. The generated baseline FEM is suitable for the stiffness strengthening of this bridge.

Fiber Bragg Grating (FBG) sensor is widely used for Structural Health Monitoring (SHM) purpose. For dedicated health monitoring of critical composite parts in aircrafts, maritime constructions, wind turbine blades, etc., high speed measurement can provide vital information to assess impact damage. For example, low-energy droplet impact to wind turbine blade will cause in long term material degradation. A measurement system which can detect the impact accurately and with high reproducibility will contribute to the development of SHM strategy of composite structures. Currently, no commercial system has the required high sampling frequency and sensitivity. We developed a fiber interferometer based FBG interrogation system for high-speed impact measurements. In the droplet experiment on a composite sample, the feasibility of using the high-speed (1 MHz) system to measure low energy down to 2.5 mJ impact is demonstrated. The amplitude of the impact induced FBG wavelength shift decreases with larger distance between the FBG and the impact location. For a test with 5.5 mJ impact at 5 mm from the FBG, the amplitude of the FBG wavelength shift is measured to be about 25 pm and the impact pulse width is about 0.1 ms. The RMS wavelength noise of the interrogator is typically < 1 pm. This system can hence be used to detect even lower impact energy or be optimized to measure stronger impacts in other applications by tuning the sensitivity of the fiber interferometer. Great reproducibility of the impact pulse shape is demonstrated.

A large flexible sensor based on electrical impedance tomography (EIT) is limited in detection area and resolution. It can be improved by introducing center electrodes. However, the number, the position and the new drive patterns will have a great impact on the performance of the sensors with large area. Based on the typical 16-electrode flexible sensor, one, two and four center electrodes are introduced in the design to improve the sensors’ resolution, and the new drive patterns for current injection and voltage measurement are proposed. In evaluating the performance of flexible sensors for the different drive patterns, the detecting resolution and position error are obtained for different numbers of center electrodes. Simulation results show that the flexible sensor with two center electrodes has a satisfactory detection accuracy. And one of the center electrodes is used for current injection and the other is used for voltage measurement. Mainly considering the resolution and position error, the particle swarm optimization (PSO) algorithm is used to optimize the position of the center electrodes. The simulation results show that when the center electrodes are positioned at 0.24 from the center, the flexible sensor has a better performance for multi-objective recognition. Compared with the typical 16-electrode flexible sensor, the detection position error can be reduced by 85.1%. This study provides a new method for finding the suitable number and position of the center electrodes for the design of large EIT based flexible sensors.

This paper presents the preliminary study on the fluid-structure interaction (FSI) analysis of dynamic intraocular pressure (IOP) in the human eye. Because Glaucoma, a chronic disease of the optic nerve, can lead to blindness, the monitoring of IOP through tonometry is essential to prevent the increase of IOP. One of the most common tonometry methods to estimate IOP includes measuring corneal deflection by using either a direct contact or non-contact (e.g. air puff) impact force. Then, the dynamic characteristics of IOP should be investigated for improved correlation to IOP monitoring systems. In this paper, we develop a finite element model of a human eye as a spherically shaped structure filled with inviscid pressurized fluid to solve a problem of a fluid-coupled structural interaction of eye. The structural shape effects due to change in IOP are examined, and the proposed model is modified to further examine by including the mechano-luminescence (ML) membrane acting as IOP monitoring element. The effect of biomechanical parameters such as the ML membrane thickness is investigated based on the air puffy type applanation tonometry models.

We propose and experimentally demonstrate a multiplexing methodology for ultrasonic sensors based on fiber Bragg gratings (FBGs) that are included in the laser cavity of a semiconductor optical amplifier (SOA)-based fiber-ring laser system coupled with a fiber Fabry-Pérot (FFP) filter. The fiber ring laser (FRL) consists of an SOA as a gain medium and of FBGs as wavelength selection elements. We experimentally fabricate a dual-wavelength fiber ring laser and confirm stable oscillation outputs of the laser source. And ultrasonic signals generated from the piezoelectric transducers (PZTs) source are successfully detected. Such a multiplexed fiber-optic ultrasonic sensor system may be used for acoustic emission (AE) detection for structural health monitoring (SHM).

While identifying the parameters of IMFs from Empirical Mode Decomposition, by Hilbert-Huang Transform, a piece of approximately linear data segment is necessary for a specific result. The select of the data segment will directly influence accuracy of the parameters. The time for getting the approximately linear data segment is required to be as short as possible. The paper uses Least Square Series-piecewise Linear Fitting method to divide data into pieces, then chooses several pieces with the highest goodness-of-fit, and takes each median as basis to change the length, for higher goodness-of-fit. The needed data segment is achieved in the case that this data segment can still reflect the inherent parameters. This paper brings some examples to verify that the approach is feasible and exact.

Detecting underground/subsurface metallic objects such as landmines and IEDs (improvised explosive devices) using efficient and effective inspection techniques is crucial in demining and mine clearance missions. The use of microwave/radar sensors in remote sensing represents a new approach to reduce demining risks and to improve efficiency. To achieve the goal, electromagnetic signatures of underground metallic objects must be thoroughly studied. The objective of this paper is to use synthetic aperture radar (SAR) imaging to investigate the size effect of an underground metallic object buried in SAR images at three ranges (15 cm, 30 cm, and 60 cm). Three different sizes (diameters = 7.6 cm, 15.2 cm, and 20.3 cm) were used and buried inside a container (sandbox) filled up with dry sand. A 10-GHz SAR imaging radar sensor was applied to generate all SAR images inside an anechoic chamber. It was found that SAR amplitude decreases with the increase of radar range. In addition, reconstructed distribution of SAR amplitudes depended on the size of underground steel disk specimens. SAR amplitudes (maximum and integrated) were related to the range and modeled by empirical equations.

With the continuous development of Computer Vision and a variety of advanced seam imaging equipment, the information contained in the seam image is very rich. It is of great significance for industry automation system. Single image feature is difficult to fully express seam image content. Multi- feature fusion has become a natural way to extract the seam image features. It can comprehensively utilize the seam image information to gain more rapid and accurate understanding of welding images.

From low to high, information fusion can be divided into three levels. The feature-level fusion not only keeps the most original information, but also overcomes the unstable and large characteristics of original data. Fusion feature can be effectively used in seam image recognition.

Firstly, we build the JARI robot system to research the seam tracking from the image identify. Secondly, principal component analysis (PCA) method based on multivariate statistical analysis is used in feature- level fusion. And it is applied in liver B- image recognition. The recognition results are analyzed and compared. Finally, through the gantry robot 9 degree system to verify the logic of the identify V type seam.

The experimental results show that fusion feature can fully and effectively express seam image, which can bring better recognition results. Analyzing and comparing the feature selection results of different sample images, the results show that feature selection is stable and effective. Comparing with the results of direct PCA fusion applications, the recognition effect after feature selection is better, not only improves the average accuracy rate of recognition but also reduces the time complexity of the recognition process. It has better performance, can be more effectively applicated in welding image recognition.

Magnetorheological elastomers (MREs), like MR fluids, exploit magnetic forces between ferromagnetic particles to produce a material with instantaneously adjustable properties of stiffness and damping with external magnetic fields. In MREs, the particles are a part of a structured elastomer matrix, and an external magnetic field is applied to achieve an instantaneous change of stiffness due to magnetic forces between particles. A drawback of conventional MREs is its inability of softening (reduce stiffness) under an external field. Many engineering applications need an instant change of its stiffness in both directions, which requires a magnetic bias embedded in the MRE. One way is the use of a permanent magnet (PM) for pre-straining a base elastomer matrix, but its mechanical design can be bulky due to the size of PM. In this paper, we address a fabrication process of the biased-magnetorheological elastomers (B-MREs) and their mechanical properties. The B-MREs consist of magnetized ferromagnetic particles as fillers and an elastomer as a binder. The magnetization of ferromagnetic particles embedded in the elastomer matrix eliminates a need for the use of the PM and can achieve the desired pre-strain in the B-MRE. The experiment results related with the mechanical properties after magnetization were presented. Also, different MRE thickness and weight ratios of the ferromagnetic particles mixed with the base elastomer were compared in both normal and shear modes.

Technique with the capability of detecting and localizing damage of structures using naturally operating environments can provide a possibility of developing more efficient and simpler structural health monitoring systems. This passive sensing technique would eliminate the need of active actuation which requires power either from battery or ambients to generate controlled excitation source. In a recent study, self-Green’s functions (GF) were reconstructed using auto-correlation (AC), combined with a damage index by comparing the differences in GFs between damaged and pristine metallic panels to locate the damage. In this paper, random decrement (RD) technique is proposed to reconstruct GF with computational efficiency. While the RD has been widely used for damage detection and structure parameter extraction in civil structures, in the frequency usually below 1 kHz; this study explores using RD up to 15 kHz for transient wave reconstruction and then damage localization. The concept is first validated through simulation for a plate structure, and the results show that the reconstructed self-Green’s function match well with the one from the auto-correlation technique after approximately 10,000 averages of the RD signatures.

A novel special waveguide sensor, which is very sensitive to magnetic field, is proposed. This novel special wave guide is combined by two kinds material, YIG polymer and Bi:YIG polymer. The two beam lights counter-propagating in this kind of waveguide can generate large nonreciprocal phase shift. We designed the sensor structure, simulated the propagated characteristic of the structure. Based on the simulations, we concluded that waveguide with the core divided by two half circles can improve the sensitive to the magnetic field which is parallel to the interface of the two semicircles. We design a Sagnac interference demonstration with the light wavelength of 1550 nm, two difference-resonant loops. This sensor with 0.51pTesla-level sensitivity can be used in ultra-low magnetic field detection.

Magnetorheological elastomer (MRE)-based base isolation system has been proposed to compensate for the drawbacks of the existing passive-type base isolation system. MRE is one of smart material with magnetorheological (MR) effect that can change the stiffness and damping ratio in real time when the magnetic field is applied. The conventional MREbased base isolation system has inter-place between MRE and electromagnetic system. This is determined in consideration of the deformation of MRE, resulting in loss of magnetic flux density and excessive volume of MRE-based base isolation system. Since the strain of the MRE should be considered to be 100 % as base isolation system, the size of the base isolation system becomes large and the practical applicability is remarkably reduced. In addition, the loss of the magnetic flux density increases according to the size of the inter-space between MRE and electromagnetic system. Therefore, a new type of electromagnetic system is proposed to address these problems. In this study, an electromagnetic system which can move with MRE by separating it to the laminated type is proposed. MRE-based base isolation system with the laminated electromagnetic system occupies a small volume compared to the conventional MRE-based base isolation system and can form magnetic closed loop which can minimize the loss of the magnetic flux density. The numerical simulation was performed to compare the proposed electromagnetic system and the conventional MRE-based base isolation. From the numerical simulation, the lab-scale experiment was carried out and the proposed method can be utilized to improve the reality of MRE-based base isolation system.

This paper presents the application of a probabilistic method to estimate the complete dynamic response and state of damage of a large-scale structural system subjected to strong base excitations. The structure considered consists of a sevenstory slice of a shear wall building tested at the George E. Brown Jr. Network for Earthquake Engineering Simulation site at the University of California at San Diego. The acceleration response measured at limited locations is combined with a nonlinear model to estimate the complete dynamic response at unmeasured degrees of freedom. To improve the predicting capability of the mathematical model a subset of the parameters is jointly estimated with the response, a strategy known as joint state-parameter estimation or augmented state estimation. The estimated response is used to compute a damage index as a quantitative measure of damage.