An effective Approach for Damage Detection using Reduction Model Technique and Optimization Algorithms
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. 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.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License [CC BY] that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).