Open AccessModeling of Moisture Content of Subgrade Materials in High-Speed Railway Using a Deep Learning Method
Author(s) -
LiLei Chen,
Jing Chen,
Chao Wang,
Yanhua Dai,
Ruixiang Guo,
Qian Huang
Publication year - 2021
Publication title -
advances in materials science and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.356
H-Index - 42
eISSN - 1687-8442
pISSN - 1687-8434
DOI - 10.1155/2021/6166489
Subject(s) - subgrade , water content , moisture , geotechnical engineering , materials science , frost (temperature) , environmental science , reliability (semiconductor) , rut , geology , composite material , power (physics) , physics , asphalt , quantum mechanics
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.