Vehicle loads identification on beam bridges via a genetic algorithm

Due to the increasing volume of freight traffic and the growing practice of structural monitoring for existing bridges, it has become crucial to exploit modal analysis to identify the magnitudes of axle loads and the frequency content of the structural response induced by vehicle crossings. In this paper, starting from the comparison between a simplified analytical model of the bridge and the relevant experimental responses, a multi-parameter identification method is proposed to identify the magnitude, the number, the axle distance, and the eccentricity of moving loads crossing the bridge. A bending-torsional beam model subjected to travelling loads characterized by non-uniform spacing and magnitudes, has been adopted to describe the bridge dynamics. The identification procedure is based on the Differential Evolution genetic algorithm. The proposed method is tested and validated using numerically simulated dynamic responses including the presence of noise. The main contribution of this work concerns the combined use of a beam model and the DE algorithm to identify heavy vehicle load distributions for skew road bridges. • A multi-parameter identification method is proposed to identify bridge moving loads. • The intensity, number, axle distance, and eccentricity of loads are identified. • The adopted 1-D beam model integrates bending and torsional responses. • Both load eccentricity and deck skewness can be considered. • Governing equations have been solved starting from a Faedo–Galerkin approach. • The differential evolution algorithm was adopted for the inverse problem. • An alternative convergence criterion (called “swarm criterion”) was proposed.