Identification of the bridge moving loads based on fractional conjugate gradient method

This paper proposes a bridge moving load identification method based on the fractional conjugate gradient (FCG) method to address the low identification accuracy of traditional conjugate gradient methods. Firstly, the mathematical framework for detecting the moving load in the vehicle-bridge system is established by utilizing both the time-domain deconvolution technique and modal superposition approach. Secondly, the derivation of the discrete moving load identification system matrix equation enables its formulation as an unconstrained optimization problem. Finally, the load information is obtained iteratively by the FCG method. Experimental results demonstrate that, compared with the Hestenes–Stiefel conjugate gradient (HSCG) method, the Flether–Reeves conjugate gradient (FRCG) method, and the Polak–Ribire–Polyak conjugate gradient (PRPCG) method, the FCG method has faster identification speed, smaller identification error, and higher identification accuracy and noise resistance in identifying bridge moving loads at different noise levels.