文献类型：期刊文献

英文题名：Modeling of Moisture Content of Subgrade Materials in High-Speed Railway Using a Deep Learning Method

作者：Chen, LiLei[1];Chen, Jing[1,2];Wang, Chao[1];Dai, Yanhua[1,2];Guo, Rongyan[1];Huang, Qian[1]

机构：[1]Shaoxing Univ, Coll Civil Engn, Shaoxing 312000, Peoples R China;[2]Key Lab Rock Mech & Geohazards Zhejiang Prov, Shaoxing 312000, Zhejiang, Peoples R China

年份：2021

卷号：2021

外文期刊名：ADVANCES IN MATERIALS SCIENCE AND ENGINEERING

收录：SCI-EXPANDED(收录号：WOS:000675750300002)、、EI(收录号：20213010679647)、Scopus(收录号：2-s2.0-85111058347)、WOS

基金：This work was financially supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant no. XDA19070504), National Natural Science Foundation of China (Grant no. 41801043), and Open Fund of State Key Laboratory of Frozen Soil Engineering (Grant no. SKLFSE201712). The authors thank Prof. Fu Jun Niu, Prof. Zhan Ju Lin, and Dr An Yuan Li for their while support field data.

语种：英文

外文关键词：Deep learning - Forecasting - Learning systems - Mean square error - Moisture - Moisture determination - Railroad transportation - Railroads

外文摘要：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.