Modeling of Moisture Content of Subgrade Materials in High‐Speed Railway Using a Deep Learning Method

Abstract

Moisture content of subgrade materials is an essential factor affecting frost heave deformation of high-speed railway subgrade in a seasonally frozen region. Modeling and predicting moisture transport play an important role in analyzing the subgrade thermal and hydraulic conditions in cold regions. In this study, a long short-term memory (LSTM) model was proposed based on subgrade material moisture in two sections during one winter and spring cycle from 2015 to 2016. The reliability of the model was verified by comparing the monitoring data with the model results. The results demonstrate that the LSTM model can be effectively used to forecast the dynamic characteristics of the moisture of subgrade materials. The data of simulated moisture content of subgrade materials have a root mean square error ranging from 0.17 to 0.47 in the training phase and from 0.20 to 10.5 in the testing phase. The proposed model provides a novel method for long-term moisture prediction in subgrade materials of high-speed railways in cold regions.

A Railway Roadbed Deformation Monitoring System Using Deep Learning and AI Intelligent Technology

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Deep Q networks-based optimization of emergency resource scheduling for urban public health events

“…Based on the moisture content of two subgrade materials in a winter-spring cycle in recent years, his research proposes a long short-term memory (LSTM) model, and the reliability and practicability of the LSTM model are proved by comparing the model and its detection data through experiments. The model provides a new method for long-term moisture prediction of high-speed railway subgrade materials in cold regions; simulating and predicting moisture transport plays an important role in analyzing the thermal and hydraulic conditions of subgrades in cold regions [ 6 ]. Matthew applies deep learning to the layering of hidden variables, constructs a nonlinear high-dimensional predictor, and develops and trains it for spatiotemporal modeling based on deep learning architectures.…”