Localizing and quantifying structural damage by means of a beetle swarm optimization algorithm

Abstract

An efficient meta-heuristic algorithm, named beetle swarm optimization (BSO), is proposed to localize and quantify structural damage using limited vibration measurement data. The beetle antennae search (BAS) algorithm that imitats a random walking mechanism in nature was recently developed to solve the optimization problem. However, the ratio of convergence of this algorithm significantly relys on the random direction and deviation for high-dimensional problems. To overcome this shortcoming, the BSO inspired by the swarm intelligence strategy is proposed. In the iterative search process of the BSO, each beetle swarm moves in a random direction like the BAS and the swarm of beetles is cognitive with the optimal one for the searching behavior. Consequently, the optimal one is updated step by step until a better beetle appears. To demonstrate the capability and robustness of the BSO, numerical and experimental studies using limited vibration measurement data of an offshore wind turbine structure are carried out for structural damage identification. An novel objective function is established by combining natural frequencies with mode shapes of the structure. The numerical results show that the BSO can accurately localize and quantify various types of damage even in a noise and temperature variations polluted environment. Moreover, it has higher accuracy and faster convergence speed than the BAS and the particle swarm optimization (PSO) algorithms. These promising performances could contribute to establishing a structural monitoring system for safety assurance of wind turbine structures.