Structural Damage Identification of an Unsymmetrical Frame Based on Variational Bayesian Model Updating with an Improved PSO Algorithm

This chapter explores the identification of structural damage in unsymmetrical frames using a sophisticated approach that combines variational Bayesian model updating with an improved Particle Swarm Optimization (PSO) algorithm. The study addresses the challenges posed by uncertainties in modeling and measurement noise, which can lead to inaccurate damage detection. By employing variational Bayesian inference (VBI), the authors avoid complex multidimensional integrations and provide a robust probabilistic treatment of these uncertainties. The enhanced PSO algorithm, incorporating opposition-based learning and boundary rebound strategies, optimizes the model updating process, making it more efficient and accurate. Experimental validation was conducted on a laboratory-built unsymmetrical frame, demonstrating the method's effectiveness in identifying damage with high precision and significantly reduced computational time compared to conventional Bayesian inference. The results highlight the method's applicability to both standard and non-standard posterior distributions, making it a versatile tool for structural health monitoring.