Application of particle swarm optimization and genetic algorithms to multiobjective damage identification inverse problems with modelling errors

Structural health monitoring has become an important research topic in conjunction with the damage assessment of structures. The use of system identification approaches for damage detection using inverse methods has become more widespread in recent years and their formulation in a multiobjective framework has become more usual. Inverse problems require the use of an initial baseline model of the undamaged structure. Modelling errors in the baseline model whose effects exceed the modal sensitivity to damage are critical and make an accurate estimation of damage impossible. Artificial intelligence techniques based on genetic algorithms are used increasingly as an alternative to more classical techniques to solve this kind of problem especially due to their feasibility for managing multiobjective problems. This paper outlines an understanding of how particle swarm optimization methods operate in damage identification problems based on multiobjective FE updating procedures and takes modelling errors into account. One experimental example is used to show their performance in comparison with genetic algorithms.