Model-based damage identification of railway bridges using genetic algorithms

The assessment of structural integrity via numerical model updating has been drawing attention in several areas of engineering over the last years. Basically, it consists in an optimization process based on the minimization of the residuals between measured and estimated numerical responses. In such methodologies, several factors influence the success of both localization and quantification of structural damage, such as: the damage features used in the formulation of the objective function, the optimization algorithm and the adopted updating parameters. Many existing studies using these methods are applied to simple structural systems, e.g., beams, frames and trusses. However, few studies applied to large and complex structures are found in the literature. In this context, this work proposes to assess the performance of a genetic algorithm-based approach applied to two case studies. The first case refers to a two-dimensional model of a hypothetical railway bridge, where the efficiency and robustness of five different indicators are assessed considering three damage scenarios. In the second case, a real railway bridge is considered. The results obtained show that the proposed approach is able to detect, locate and quantify multiple damage with several updating parameters and few target responses.