Damage assessment in plate structures using an enhanced Jaya algorithm

Abstract

The article proposes a new method called enhanced Jaya (EJaya) to identify and evaluate damage in plate structures. The method uses a nonlinear optimization task to solve the inverse damage identification problem. The damage magnitudes of the plate elements are taken as design variables, and the objective function to be minimized uses the variations in the plate flexibility matrix and includes a penalization term to avoid false alarm detection. EJaya is developed by incorporating three modifications in the traditional Jaya algorithm: generating an initial population of candidate solutions using prior knowledge of the damage scenario, integrating a component that takes into account the individual distribution through the search space, and implementing a new local search technique. The simulation results show that EJaya is efficient in both solution quality and computational effort and can detect and evaluate damage even in large-sized problems and under noisy data conditions.