A new structural damage detection strategy of hybrid PSO with Monte Carlo simulations and experimental verifications

Structural damage detection (SDD) is originally an optimization problem by minimizing discrepancy between measured and calculated data of structures. In most cases, natural frequencies and mode shapes are selected to define objective functions for SDD. In this study, a new SDD strategy of hybrid particle swarm optimization (HPSO) is proposed and its availability solution to SDD is studied via Monte Carlo simulations. First of all, PSO algorithms with different parameters are tested via Monte Carlo simulations to decide which combination of parameters is more beneficial for SDD. After that, a powerful local searching Nelder-Mead algorithm is embedded into the PSO with a new strategy, which confirms helpful to enhance the PSO global searching ability numerically and experimentally. Simply-supported beams with 10 and 20 finite elements are simulated respectively which prove our proposed method to be effective in SDD. Further, a series of experiments of a box-section steel beam are designed and fabricated in laboratory. Structural frequencies and mode shapes are measured under different crack damage patterns. The experimental verifications confirm the applicability of the proposed new SDD strategy. The SDD results of the experimental beam also show the outperformance of the proposed new SDD strategy. Some relative discussions are also described in detail.