This study introduces a novel hybrid algorithm, equilibrium optimizer-singular value decomposition (EO-SVD), for structural damage detection in various bridge models. By combining the optimization capabilities of EO with the dimensionality reduction of SVD, the proposed approach achieves superior accuracy and computational efficiency compared to traditional algorithms.
By combining the optimization capabilities of EO with the dimensionality reduction of SVD, the proposed approach achieves superior accuracy and computational efficiency than traditional algorithms. Comprehensive evaluations were performed on multiple bridge models, including a reinforced concrete bridge, a steel truss bridge and a suspension bridge, under scenarios with noise.
The proposed algorithm achieved the highest accuracy in the three mentioned structures, with values of 2.98E−05, 2.97E−05 and 4.32E−04, demonstrating high accuracy as the highest standard deviation was only 7.97E−04. EO-SVD consistently delivered the lowest errors and fastest convergence. Utilizing finite element models and dynamic features, the algorithm effectively pinpointed damage locations and severities, even under complex scenarios.
Although its performance may slightly decrease with highly intricate structures, EO-SVD remains highly robust and reliable, offering a solid foundation for the future development of structural health monitoring systems.
Utilizing finite element models and dynamic features, the algorithm effectively pinpointed damage locations and severities, even under complex scenarios.
