Health Monitoring of Civil Engineering Structures Using Simulated Annealing Genetic Algorithm

Abstract

The key to structural health monitoring in civil engineering is to optimize the configuration of sensors in the monitoring system to improve the diagnostic accuracy and reduce the consumption of computing resources. In this study, the genetic algorithm and the simulated annealing algorithm were improved, and an adaptive simulated annealing genetic algorithm was formed, and the strain mode criterion was integrated to achieve more accurate sensor optimal configuration. The finite element model of the bridge structure was constructed by ANSYS software and analyzed to obtain the strain mode matrix and displacement mode matrix. The simulation results showed that the simulated annealing genetic algorithm’s iteration in the process of obtaining the minimum MAC index value was only 132 times, which was significantly lower than that of the target detection algorithm (279 times) and the negative selection algorithm (284 times). At the same time, the average detection error rate of the simulated annealing genetic algorithm was reduced to 0.52, which was better than the 0.66 of the target detection algorithms and the 0.61 of the negative selection algorithm. The proposed algorithm not only shows obvious advantages in convergence speed, but also has higher accuracy than displacement mode in sensor optimization arrangement and has application potential in structural health monitoring of civil engineering.

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