An effective Approach for Damage Detection using Reduction Model Technique and Optimization Algorithms

Abstract

With the development of science and technology in recent decades, numerous optimization algorithms have emerged and been successfully applied in various fields. Particle swarm optimization (PSO) is a well-established evolutionary algorithm commonly used for optimization tasks. However, similar to other evolutionary algorithms, PSO has two main limitations that can hinder its performance. The first limitation is premature convergence, which can result in suboptimal solutions. The second limitation is the high computational time since PSO employs all particles in the swarm for each iteration. To overcome these limitations, in this work, we propose coupling a reduction model technique, specificially, Orthogonal Diagonalization (OD) with a hybrid algorithm combining Genetic Algorithm (GA) and PSO, termed HGAPSO-OD.  GA is employed to address the issue of premature convergence in PSO, whereas OD is employed to rearrange the particle positions and select only the particles with the best solutions for the next iterations, thereby reducing the computational cost significantly. To evaluate the effectiveness of the proposed approach, a large-scale railway bridge, calibrated based on field measurements, is used as a case study. The results demonstrate that HGAPSO-OD not only increases the accuracy but also reduces computational time of GA and traditional PSO.