Structural damage localization and quantification in plane truss structures using optimization techniques

Abstract

Civil infrastructures are susceptible to threats from both nature and human activity; as they are built and used, they deteriorate, potentially resulting in structural damage or even collapse. The detection of structural damage is an important field of study that aims to identify and quantify any possible damage to structures such as bridges, buildings, and other infrastructure. Early detection of structural deterioration benefits the identification of cracks, flaws, and other possible safety issues in civil infrastructure. Identifying and quantifying structural damage with methods based on dynamic analysis data of structures is the main objective of the present study. The damage identification problem is approached as an optimization problem, which is solved using two optimization techniques: Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Three objective functions based on dynamic analysis data of the structures such as modal flexibilities, natural frequencies, and mode shapes are used in the optimization process. This data was gathered by developing a program that performs the dynamic analysis of structures using the Finite Element Method (FEM). The effectiveness of each objective function is assessed through evaluations conducted on three damage scenarios involving a 10-bar truss structure. The impacts of noise and damage levels on damage detection are investigated.