Damage Detection in Bridges by Hybrid Particle Swarm Optimization–Simulated Annealing (PSO–SA) Approach

Structural health monitoring (SHM) is critical for ensuring the safety and longevity of bridges. This research presents a novel hybrid approach for structure damage detection, combining Particle Swarm Optimization (PSO) with Simulated Annealing (SA). The PSO algorithm provides an efficient global search mechanism, while SA enhances local search capabilities, improving convergence towards the optimal solution. By integrating these two methods, the proposed PSO-SA approach enhances accuracy and robustness in detecting structural damage. The performance of the proposed method is investigated through numerical studies on two real bridge models under single- and multiple-damage scenarios. For the considered cases and under the adopted parameter settings, PSO–SA demonstrates improved damage localization and quantification accuracy compared with PSO, Genetic Algorithm (GA), and SA. In single-damage scenarios, PSO–SA reduces damage-severity estimation errors to 0.16–0.33%, whereas PSO, GA, and SA yield errors ranging from 1.16% to 11.3%. In the investigated multi-damage cases, PSO–SA successfully identifies all damaged elements and drives the objective function close to zero, while the benchmark algorithms exhibit convergence difficulties. The results indicate that the proposed PSO–SA approach is a robust and promising tool for bridge damage detection when appropriately tuned. The study highlights the potential of hybrid metaheuristic strategies for enhancing SHM performance and provides a basis for further investigations on broader structural systems and parameter sensitivities.