Decentralized structural damage detection and model updating with mobile and wireless sensors
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Recent years have seen increasing research interest in structural health monitoring (SHM). Among the many advances in SHM research, “smart” wireless sensors capable of embedded computing and wireless communication have been highly attractive. Wireless communication in SHM systems was originally proposed to significantly reduce the monetary and time cost for installing lengthy cables in an SHM system. Besides wireless sensing, the next revolution in sensor networks has been predicted to be mobile sensor networks that implant mobility into traditional wireless sensor networks. This research explores decentralized structural model updating and damage detection using mobile and wireless sensors. In the first stage of this research, mobile sensing nodes (MSNs) are developed for SHM purposes. The MSNs can maneuver upon structures built with ferromagnetic/steel materials, conduct measurement, and communicate with pears or remote servers wirelessly. The performance of the MSNs is validated through laboratory and field experiments. To further investigate the mobile sensing strategy, a decentralized structural damage detection procedure is proposed herein for the MSNs using transmissibility functions. Laboratory experiments are conducted on a steel portal frame where various structure damage scenarios are emulated. Besides experiments with MSNs, this study also investigates the nature of transmissibility functions for damage detection in an analytical manner based on a general multi-DOF spring-mass-damper system. Finally, this research also explores substructure model updating through minimization of modal dynamic residuals, which can best benefit from dense mobile or wireless sensor data concentrated in one area. Craig-Bampton transform is adopted to condense the structural model, and minimization of the modal dynamic residuals is determined as the optimization objective. An iterative linearization procedure is adopted for efficiently solving the optimization problem. The presented substructure updating method is validated through a few numerical examples. For comparison, a conventional approach minimizing modal property differences is also applied, and shows worse updating accuracy than the proposed approach. The performance of the proposed substructure model updating approach is further investigated on the effects of substructure location and size.