Analysis and Improvement of Fitting Models for Predicting Subsidence Under High-Speed Railway Lines

Abstract

A number of methods for predicting land subsidence and monitoring deformation under high-speed railway tracks exist, and are divided into three categories: layer-wise summation, numerical calculations based on consolidation theory, and curve fitting. One of these, curve fitting, including the hyperbola, expanded hyperbola, three-point fitting and Asaoka methods, is widely used because it is computationally simple and applicable in many situations. In this paper, we analyze the performance of the four classical curve fitting methods using field data and propose a novel approach to estimate land subsidence. The new method integrates three-point fitting, which is computationally simple whilst stringent in terms of correlation restrictions, with the Asaoka method to significantly improve performance in practical applications. Our experimental results indicate the average relative error of the modified method is reduced by 35.3 % than that of three-point fitting, and the mean correlation coefficient remains within acceptable bounds and even was enhanced by 1.48 %, so that this modified method can substantially improve prediction precision.