Mechanical response and data-driven fatigue model of interlayer soils in track-bed considering multi-factor coupling effect

Abstract

This study delves into the mechanical behavior of interlayer soils in conventional French railway track beds. The focus is on the influence of four key factors: volumetric coarse grains content, stress state, number of cycles, and water content. Comprehensive analysis of experimental results reveals that permanent deformation and resilient modulus are significantly affected by the interplay of these factors. Notably, the relationship between these factors and the permanent deformation of interlayer soils is sophisticated and coupled, and more advanced models may be required to sufficiently reflect their behavior. To better understand the complex relationships among these factors and accurately predict the behavior of interlayer soil, a fatigue model based on Artificial Neural Networks (ANN, i.e. a classical data-driven approach) was developed. The model demonstrates high prediction reliability and accuracy, with a determination coefficient (R2) of 0.9996, a mean absolute error (MAE) of 0.0044, and a root mean square error (RMSE) of 0.006629. Thereafter, the proposed model was compared with the laboratory cyclic test results as well as with the commonly-used empirical fatigue models, and a permanent strain curve of the interlayer soil were also successfully predicted by using test set. Results show that the proposed model could effectively capture the influence of multiple coupled factors on permanent plastic strain, adapting to a wide range of interlayer soil conditions. In conclusion, the data-driven fatigue model provides valuable insights into the combined effects of various factors on interlayer soil behavior, offering an effective tool for evaluating the performance of French traditional track beds under different conditions.