Damage identification in steel plate using FRF and inverse analysis

Metaheuristic algorithms have known vast development in recent years. And their applicability in engineering projects is constantly growing; however, their deferent exploration and exploitation techniques cause the engineering problems to favor some algorithms over others. This paper studies damage identification in steel plates using Frequency Response Function (FRF) damage indicator to detect and localize the healthy and damaged structure. The study is formulated as an inverse analysis, investigating the performance of three new metaheuristic algorithms of Wild Horse Optimizer (WHO), Harris Hawks Optimization (HHO), and Arithmetic Optimization Algorithm (AOA). The objective function is based on measured and calculated FRF damage indicators. The results showed that the case of four damages with different damage severity levels presented a good challenge where the HWO algorithm was shown to have the best performance. Both in convergence speed and CPU time.

Automated summary

Metaheuristic algorithms have seen significant development in recent years, with increasing applicability in engineering projects. However, differences in exploration and exploitation techniques can lead to certain algorithms being favored over others. This study focused on damage identification in steel plates using Frequency Response Function (FRF) indicators, comparing the performance of three new metaheuristic algorithms, with the HWO algorithm demonstrating superior performance in terms of convergence speed and CPU time.