Sensors optimization strategies for an efficient shape sensing using the inverse Finite Element Method

The inverse Finite Element Method is attracting significant attention as a crucial tool for the Structural Health Monitoring framework. This method allows for the monitoring of the displacements field of a structure from discrete strain sensors. Its accuracy, and consequently its applicability to real structures, strongly depends on the strain sensors configuration. In this work three optimization algorithms, the Genetic Algorythm (GA), the Particle Swarm Optimization (PSO) and the Whale Optimization Algorithm (WOA), are explored to assess their effectiveness as a fast and robust tool for the optimization of the strain sensors for the iFEM. The study proves that the GA is preferable when the considered configuration requires the exploration of a broad search space, whereas the PSO and WOA are preferable when a fast optimization of a reduced set of possible sensors is required.