Sensor optimization using an evolutionary strategy for structural health monitoring in high temperature environments

In a high temperature environment, it is challenging to perform structural health monitoring (SHM), which has become a required task for many important civil structures in harsh environments. A SHM system in high temperature environments requires a large number of sensors for different data resource measurements, for example, strain and temperature. The accuracy of the measurement is highly dependent on the trade-off between the number of sensors of each type and the associated cost of the system. This paper introduces a sensor optimization approach based on an evolutionary strategy for the multi-objective sensor placement of structural health monitoring in high temperature environments. A single-bay steel frame with localized high temperature environment validates the multi-objective function of the evolutionary strategy. The variance between the theoretical and the experimental analysis was within 5 %, indicating an effective sensor placement optimization using the developed genetic algorithm, which can be further applied to general sensor optimization for SHM system applications in high temperature environments.