Recent water shortages, particularly evident in the state of California, are calling for better predictive capabilities, and
improved management techniques for existing water distribution infrastructure. One particular example involves large-scale
water distribution systems (on the scale of reservoirs and dams) in the Sierra Nevada, where the majority of the
state's water is obtained from melting snow. Current control strategies at this scale rely on sparse data sets, and are often
based on statistical predictions of snowmelt. Sudden, or unexpected, snowmelt can thus often lead to dam-overtopping,
or downstream flooding.
This paper assesses the feasibility of employing real-time hydrologic data, acquired by large-scale wireless sensor
networks (WSNs), to improve current water management strategies. A sixty node WSN, spanning a square kilometer,
was deployed in the Kings River Experimental Watershed, a research site in the Southern Sierra Nevada, at an elevation
of 1,600-2,000 m. The network provides real time information on a number of hydrologic variables, with a particular
emphasis on parameters pertaining to snowmelt processes. We lay out a system architecture that describes how this real-time
data could be coupled with hydrologic models, estimation-, optimization-, and control-techniques to develop an
automated water management infrastructure. We also investigate how data obtained by such networks could be used to
improve predictions of water quantities at nearby reservoirs.
We use a new design of high-fidelity nanoseismic sensors to detect the stress waves produced at the initiation of sliding
during stick-slip friction. The piezoelectric sensors can detect radiated waves just a few pm in amplitude in the frequency
range of 10 kHz to over 2 MHz. The reported experiments are designed to provide insights that may be applicable to
both fault scales and micro contact junctions. The sensors used are packaged in a hardened steel case to facilitate their
use in the field. The transducer's small size (14 mm threaded body, 30 mm long) permits a dense population of sensors
to be installed on laboratory-sized samples, or surrounding localized centers of damage on structural applications. The
closely spaced sensor array facilitates the localization of individual load releases from tiny asperities on a cm-scale
frictional interface. At the same time, the broadband response of the conical piezoelectric sensors makes possible the
study of source dynamics using theory developed for the study of earthquake source mechanisms via radiated seismic
waves.
Experimental studies were performed using high-fidelity broadband Glaser-NIST conical transducers to
quantify stress waves produced by the elastic collision of a tiny ball and a massive plate. These sensors are sensitive to
surface-normal displacements down to picometers in amplitude, in a frequency range of 20 kHz to over 1 MHz. Both the
collision and the resulting transient elastic waves are modeled with the finite element program ABAQUS and described
theoretically through a marriage of the Hertz theory of contact and a full elastodynamic Green's function found using
generalized ray theory. The calculated displacements were compared to those measured through the Glaser-NIST sensors.
As civil infrastructure ages, the early detection of damage in a structure becomes increasingly important for both life
safety and economic reasons. This paper describes the analysis procedures used for beamforming acoustic emission
techniques as well as the promising results of preliminary experimental tests on a concrete bridge deck. The method of
acoustic emission offers a tool for detecting damage, such as cracking, as it occurs on or in a structure. In order to gain
meaningful information from acoustic emission analyses, the damage must be localized. Current acoustic emission
systems with localization capabilities are very costly and difficult to install. Sensors must be placed throughout the
structure to ensure that the damage is encompassed by the array. Beamforming offers a promising solution to these
problems and permits the use of wireless sensor networks for acoustic emission analyses. Using the beamforming
technique, the azmuthal direction of the location of the damage may be estimated by the stress waves impinging upon a
small diameter array (e.g. 30mm) of acoustic emission sensors. Additional signal discrimination may be gained via array
processing techniques such as the VESPA process. The beamforming approach requires no arrival time information and
is based on very simple delay and sum beamforming algorithms which can be easily implemented on a wireless sensor or
mote.
Array processing of seismic data provides a powerful tool for source location and identification. For this method to work
to its fullest potential, accurate transduction of the unadulterated source mechanism is required. In our tests, controlled
areas of normal-strength concrete specimens were exposed to a low relative humidity at an early age to induce cracking
due to drying shrinkage. The specimens were continuously monitored with an array of broad-band, high-fidelity acoustic
emission sensors contrived in our laboratory in order to study the location and temporal evolution of drying shrinkage
cracking.
The advantage of the broadband sensors (calibration NIST-traceable) compared to more traditional acoustic emission
sensors is that the full frequency content of the signals are preserved. The frequency content of the signals provides
information about the dispersion and scattering inherent to the concrete, and the full unadulterated waveforms provide
insight into the micromechanisms which create acoustic emissions in concrete. We report on experimental and analytical
methods, event location and source mechanisms, and possible physical causes of these microseisms.
KEYWORDS: Sensors, Sensor networks, Microelectromechanical systems, Acoustic emission, Data analysis, Bridges, Data acquisition, Signal detection, Inspection, Algorithm development
The inspection of building structures, especially bridges, is currently made by visual inspection. The few non-visual methodologies make use of wired sensor networks, which are relatively expensive, vulnerable to damage, and time consuming to install. Systems based on wireless sensor networks should be both cost efficient and easy to install, scalable and adaptive to different type of structures. Acoustic emission techniques are an additional monitoring method to investigate the status of a bridge or some of its components. It has the potential to detect defects in terms of cracks propagating during the routine use of structures. However, acoustic emissions recording and analysis techniques need powerful algorithms to handle and reduce the immense amount of data generated. These algorithms are developed on the basis of neural network techniques and - regarding localization of defects - by array techniques. Sensors with low price are essential for such monitoring systems to be accepted. Although the development costs of such a system are relatively high, the target price for the entire monitoring system will be several thousands Euro, depending on the size of the structure and the number of sensors necessary to cover the most important parts of the structure. Micro-Electro-Mechanical-Systems and hybrid sensors form the heart of Motes (network nodes). The network combined multi-hop data transmission techniques with efficient data pre-processing in the nodes. Using this technique, monitoring of large structures in civil engineering becomes very efficient including the sensing of temperature, moisture, strain and other data continuously. In this paper, the basic principles of a wireless monitoring system equipped with MEMS sensors is presented along with a first prototype. The authors work on details of network configuration, power consumption, data acquisition and data aggregation, signal analysis and data reduction is presented.
KEYWORDS: Sensors, Data communications, Amplifiers, Telecommunications, Clocks, Control systems, Analog electronics, Data storage, Data modeling, Prototyping
The Terra-Scope system is an affordable 4-D down-hole seismic monitoring system based on independent, microprocessor-controlled sensor Pods. The pods are nominally 50 mm in diameter, and about 120 mm long. They are expected to cost approximately $6000 each. An internal 16-bit, extremely low power MCU controls all aspects of instrumentation, eight programmable gain amplifiers, and local signal storage. Each pod measures 3-D acceleration, tilt, azimuth, temperature, and other parametric variables such as pore water pressure and pH. The following parameters are independently controllable at each pod: pre-trigger length, post-trigger length, trigger time stamp, sampling rate, trigger level, trigger parameters, non-volatile storage, and calibration and self-evaluation. Each Pod communicates over a standard digital bus (e.g. RS-485) through a complete web-based GUI interface, and has a power consumption of less
than 400mW. Three-dimensional acceleration is measured by pure digital force-balance MEMS-based accelerometers. These accelerometers have a dynamic range of more than 115 dB and a frequency response from DC to 1000 Hz. The accelerometer chip uses a 5th order delta-sigma feedback loop to yield a noise floor of less than 30 ngrms/√Hz. Accelerations above 0.2 g are measured by a second set of MEMS-based accelerometers, giving a full 160 dB dynamic range. The prototype of the device is currently undergoing evaluation. The first array will be installed in the fall of 2005.
KEYWORDS: Sensors, Sensor networks, Earthquakes, Data modeling, System identification, Microcontrollers, Systems modeling, Filtering (signal processing), Computer science, Operating systems
This paper presents two case histories of the use of wireless sensor Mote technologies. These are devices that incorporate communications, processing, sensors, sensor fusion, and power source into a package currently about two cubic inches in size -- networked autonomous sensor nodes. The first case discussed is the November, 2001, instrumentation of a blast-induced liquefaction test in Tokachi Port, Japan. The second case discussed is the dense-pak instrumentation of the seismic shaking test of a full-scale wood-frame building on the UCB Richmond shake table. The utility of dense instumentation is shown, and how it allows location of damage globally unseen. A methodology of interpreting structural seismic respose by Bayesian updating and extended Kalman filtering is presented. It is shown that dense, inexpensive instrumentation is needed to identify structural damage and prognosticate future behavior. The case studies show that the current families of Motes are very useful, but the hardware still has difficulties in terms of reliability and consistancy. It is apparent that the TinyOS is a wonderful tool for computer science education but is not an industrual quality instrumentation system. These are, of course, growing pains of the first incarnations of the Berkeley Smart Dust ideal. We expect the dream of easy to use, inexpensive, smart, wireless, sensor networks to become a reality in the next couple of years.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.