A present challenge in structural health monitoring consists in the detection, localization, and quantification of small damage (e.g., small cracks) within large structures, such as bridges and buildings. Existing sensing solutions have several limitations, the most important being those related to the extent of spatial coverage by sensors and power supply. In this work, we will present proof-of-concept research for sub-millimeter displacement measurement using novel embeddable passive wireless radio frequency (RF) sensors. The novel sensors estimate relative displacement from phase shifts in the transmitted RF signal. The proposed system represents a novel paradigm in wireless sensing in structural health monitoring, as the wireless sensors are battery-less and will be deployed in a form of densely populated 3D network embedded within large volume of material.
KEYWORDS: Data modeling, Buildings, Calibration, Structural health monitoring, Stochastic processes, Safety, Performance modeling, Monte Carlo methods, Earthquakes, Bridges
The long-term behavior of concrete is driven by rheological effects and impacts the safety and serviceability of high-rise buildings. Rheological effects are difficult to predict due to their dependence on environmental conditions and loading history. Recently, data-driven prediction using long-term structural health monitoring data have shown success in prestressed bridges. In this work, calibration of rheology models using long-term monitoring data towards forecasting of long-term behavior of high-rise buildings is investigated. A calibration strategy is identified that enables improved long-term forecast. The method is evaluated on data from two residential high-rise buildings.
Ground penetrating radar (GPR) is a remote geophysical sensing method that has been applied in the localization of underground utilities, bridge deck survey, localization of landmines, mapping of terrain for aid in driverless cars, etc. Multistatic GPR can deliver a faster survey, wider spatial coverage, and multiple viewpoints of the subsurface. However, because of the transmit and receive antennas spatial offset, formation of 3D GPR image by simple stacking of the acquired A-scans is inaccurate. Also, averaging of different receivers data may lead to destructive interference of back-scattered waves due to different time delays implied by the spatial offset, so averaging does not lead to higher SNR in general. Furthermore, the energy back-scattered by scatter points are spread in hyperbolas in the GPR raw data. Migration or imaging algorithms are employed to increase SNR by focusing the hyperbolas. This focusing process also leads to better accuracy in target localization. In this paper, a computationally efficient synthetic aperture radar (SAR) imaging algorithm that properly integrates multistatic GPR data in both ground and air-coupled cases is presented. The algorithm is successfully applied on two synthetic datasets.
Ground penetrating radar (GPR) subsurface sensing is a promising nondestructive evaluation (NDE) technique for inspecting and surveying underground utilities in complex urban environments, as well as for monitoring other key infrastructure such as bridges and railroads. A challenge of such technique lies on image formation from the recorded GPR data. In this work, a fast back projection algorithm (BPA) for three-dimensional GPR image construction is explored. The BPA is a time-domain migration method that has been effectively used in GPR image formation. However, most of the studies in the literature apply a computationally intensive BPA to a two-dimensional dataset under the assumption that an in-plane scattering occurs underneath the GPR antennas. This assumption is not precise for 3D GPR image formation as the GPR radiation scatters in multiple directions as it reaches the ground. In this study, a generalized form for an approximation to determine the scattering point in an air-coupled GPR system is developed which considerably reduces the required computations and can accurately localize the scattering point position. The algorithm is evaluated by applications on GPR data synthesized using GprMax, a finite-difference time domain (FDTD) simulator.
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.