Damage Identification in Bridge Structures Based on a Novel Whale-Sand Cat Swarm Optimization Algorithm and an Improved Objective Function

Abstract

Structural damage identification (SDI) serves as an indirect approach that has the potential to meet real-time monitoring of structures. However, the identification accuracy and efficiency of some methods need to be improved, especially when there are some uncertain interfering factors or noise. This paper presents a new optimization algorithm and an improved objective function for inverse problems of SDI, offering an effective solution for bridge damage identification under uncertain noise interference and incomplete modal data. In this study, by hybridizing the whale optimization algorithm and the sand cat swarm optimization, a novel whale-sand cat swarm optimization (W-SCSO) method is proposed for SDI. The cubic chaotic mapping is introduced for initialization of the W-SCSO method, and then the lens opposition-based learning and the stochastic differential mutation are employed to enhance the search capability and convergence accuracy of the proposed algorithm. Besides, the mode shape curvature, the frequency change ratio, and the L_1/2 sparse regularization are used to improve the objective function. Four other existing state-of-the-art methods are used to verify the performance of the proposed W-SCSO method by the CEC2017 benchmark functions and a simply supported beam finite model. The comparative analysis highlights the feasibility and effectiveness of the proposed method in the considered cases. Moreover, an aluminum alloy simply supported beam was conducted for the SDI experiment to further prove the effectiveness of the improved method in practice. Simulation and experimental results show that the proposed method effectively locates and quantifies stiffness reduction in bridge structures, which maintains high accuracy in damage identification despite potential modal incompleteness and uncertain measurement noise interference.