Wheel-rail force identification for high-speed railway based on a modified weighted l1-norm regularization with optimal strain sensors

Abstract

Wheel-rail force identification is an important inverse problem for structural health monitoring of high-speed railway train-track-bridge system. It is widely used for dynamic response reconstruction of bridges and early safety warning of dynamic reduction rate of wheel weight for the train. It can also serve as a reference for the calibration of wheel-rail force model. This paper proposes a wheel-rail force identification method for high-speed railway based on a modified weighted l1-norm regularization. Based on numerical simulations, it is found that variations of dynamic component of wheel-rail forces are very small. Therefore, the problem is transformed into the identification of three categories of static wheel weights and two categories of average dynamic components, which greatly simplifies the degree of freedom of the solution compared to traditional methods, thereby improving the solution accuracy and saving the number of sensors needed. The wheel-rail forces are decomposed into the product of a series of redundant basis functions and corresponding coefficients. The fast iterative shrinkage threshold algorithm (FISTA) and the Bayesian information criterion (BIC) are used to seek the solution of l1-norm regularization and to select the optimal regularization coefficients, respectively. Since the number of sensors and their locations have a significant impact on the accuracy of reconstructed wheel-rail forces, they are optimized using a genetic algorithm (GA), aiming to accurately reconstruct the wheel-rail forces with the minimum number of sensors at corresponding optimal positions. Numerical simulation of a 32 m box girder standard beam for high-speed railway shows that the reconstructed wheel-rail forces are in good agreement with the simulated results, which verifies the accuracy of the proposed method. In addition, the results of dynamic load test indirectly demonstrate the effectiveness of the proposed method.