Damage localization in irregular shape structures using intelligent FE model updating approach with a new hybrid objective function and social swarm algorithm - Alkayem, Nizar Faisal; Cao, Maosen; Ragulskis, Minvydas (Applied soft computing, 2019)
Health monitoring of structures and damage diagnosis are important research disciplines under investigation worldwide. Soft computing techniques are usually used to solve the uncertain complex inverse problem of revealing structural damage. In the current research, FE model updating (FEMU) paradigm is embraced for solving the damage tracking problem in three dimensional irregular shape structures. By taking into account the complexity of problem, the pivotal point is to efficiently educe damage through well-evolved objective function. Therefore, a novel objective function merging the modal characteristics of modal strain energy (MSTEN) and mode shape curvature (MSC) is established. Posteriorly, to solve the FEMU problem, a hybrid algorithm combining the particle swarm optimization with a new social version of the sine–cosine optimization algorithm (SPSOSCA) is proposed. The SPSOSCA is considered to take advantage of two enhanced search mechanisms to overcome the overall problem complexity. The proposed paradigm is evaluated using many damage scenarios even under noise conditions and the total outcome reveals outstanding performance with fair computational time. •A hybrid algorithm is evolved to solve structural damage identification problems.•The hybrid algorithm is verified using well-known test functions.•A new hybrid objective function is utilized.•The developed method was tested on three-dimensional irregular-shape frames.•The proposed method succeeded to detect damage until the level of 10% noise.