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While wireless sensor networks have been successfully deployed on a variety of civil infrastructure systems for structural monitoring, past studies have shown that there is room for improvement in terms of network robustness and overall resource consumption efficiency. The mechanisms employed by biological nervous systems (e.g. signal modulation, communication, and integration) can be used as inspiration for overcoming the performance bottlenecks seen in existing wireless sensor nodes and networks. The mammalian auditory system is of particular interest due to its unique signal decomposition techniques (performed by the cochlea) that enable real-time processing of complex sound signals. In this article, a novel wireless sensor architecture based on the operational principles of cochlea is described. The performance of the proposed sensor is validated on a single-degree-of-freedom structure that is excited by seismic ground motion signals, thus demonstrating its real-time monitoring capabilities while maintaining high data compression rates.

This study performs a series of numerical simulations and shake-table experiments to design and assess the performance of a nonlinear clipped feedback control algorithm based on optimal polynomial control (OPC) to mitigate the response of a two-span bridge equipped with a magnetorheological (MR) damper. As an extended conventional linear quadratic regulator, OPC provides more flexibility in the control design and further enhances system performance. The challenges encountered in this case are (1) the linearization of the nonlinear behavior of various components and (2) the selection of the weighting matrices in the objective function of OPC. The first challenge is addressed by using stochastic linearization which replaces the nonlinear portion of the system behavior with an equivalent linear time-invariant model considering the stochasticity in the excitation. Furthermore, a genetic algorithm is employed to find optimal weighting matrices for the control design. The input current to the MR damper installed between adjacent spans is determined using a clipped stochastic optimal polynomial control algorithm. The performance of the controlled system is assessed through a set of shake-table experiments for far-field and near-field ground motions. The proposed method showed considerable improvements over passive cases especially for the far-field ground motion.

A structural dynamic based health monitoring system for large structural systems has been proposed in this article. The proposed method has been verified numerically and experimentally by implementing the scheme on a model of a long span bridge. In the implementation process, the following steps have been identified as being important: (1) finite element modeling for the purpose of establishing the base line, (2) optimal sensor placement to make a scheme economical and (3) damage identification. In the process the authors have identified and discussed the various difficulties that have been encountered and have made suitable recommendations for circumventing the problems.