Modeling of High-Speed Railway Carbody Weighing and Leveling with Optimization of Secondary-Suspension Load Distribution

To address the uneven distribution of secondary-suspension loads, the insufficient prediction accuracy of conventional mechanistic models, and the limited comprehensive optimization capability of existing leveling algorithms in the weighing and leveling process of high-speed railway carbodies, this study proposes a secondary-suspension load distribution optimization method that integrates radial basis function (RBF) neural-network error compensation with a multi-objective improved whale migration optimization algorithm. First, a mechanistic model describing the relationship between secondary-suspension loads and shim thickness is established based on the four-point weighing mechanics, and an RBF neural network is employed to compensate for the model prediction error, thereby improving the characterization accuracy of the actual loading state of the carbody. Second, a multi-objective optimization model for carbody weighing and leveling is formulated by taking the load standard deviation, total shim thickness, and number of shim positions as optimization objectives. Furthermore, the whale migration algorithm is improved according to the requirements of secondary-suspension load optimization, enabling collaborative multi-objective optimization of load deviation and total shim thickness. Finally, simulated-carbody tests and field tests on actual carbodies are carried out using a weighing and leveling test bench. The results show that the proposed method can identify the stress-free state of the carbody more accurately, reduce the error between theoretical calculations and measured data, and effectively improve the nonuniform distribution of secondary-suspension loads, demonstrating good engineering applicability.