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.

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Courtney A. Peckens and Camille Fogg, "Bio-inspired iterative learning technique for more effective control of civil infrastructure," Proc. SPIE 10970, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019, 109701T (Presented at SPIE Smart Structures + Nondestructive Evaluation: March 07, 2019; Published: 27 March 2019); https://doi.org/10.1117/12.2514334.