A Novel Improved Dragonfly Algorithm-Based Structural Damage Identification Approach Using Flexibility Assurance Criterion and Trace Lasso

Abstract

Aiming to enhance the accuracy, stability, and noise robustness of swarm intelligence-based algorithms for structural damage identification (SDI), a novel improved dragonfly algorithm (IDA) is proposed. The IDA integrates the dragonfly algorithm (DA) with three key strategies including enhanced Lévy flight, optimal solution bidirectional search, and greedy preservation. These strategies are introduced to enhance the exploration capability of the original DA and improve the IDA’s capacity to obtain global optima. An objective function is defined using frequency change ratio and flexibility assurance criterion (FAC). Additionally, trace sparse regularization is also incorporated into the objective function since most of the damages in structures tend to be sparsely distributed, so that a sparse result is ensured to improve SDI accuracy. To evaluate the performance of the proposed algorithm, a comparison of the original DA and IDA is conducted using four benchmark functions. The results demonstrate that the proposed algorithm achieves improved convergent rate and accuracy. Furthermore, numerical simulations are performed on a 10-element simply-supported beam and a 31-element planar truss to validate the effectiveness and efficiency of the proposed algorithm in SDI. Significantly, the utilization of IDA instead of DA leads to a substantial reduction in the average calculated relative error for the truly damaged element within the considered damage cases of the simply-supported beam, decreasing from 13.05% to 6.15%. Moreover, an experimental simply-supported beam structure with several assumed damage cases is fabricated in the laboratory. The experimental results further confirm the robustness and capability of the proposed method in real-world SDI applications.