ORIGINAL RESEARCH article
Front. Built Environ.
Sec. Geotechnical Engineering
Volume 11 - 2025 | doi: 10.3389/fbuil.2025.1702533
Study on Settlement Prediction Methods for High Embankments in Mountainous Areas
Provisionally accepted
- 1China Railway Communications Investment Group Co.,Ltd., Nanning, China
- 2Guangxi China Railway Nanheng Expressway Co., Ltd., Nanning, China
- 3College of Civil Engineering and Architecture, Guangxi University, Nanning, China
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Due to the influence of special geological conditions and economic factors, the construction of highways in the mountainous areas of Guangxi has employed a large number of high embankment structures. Characterized by significant settlement amounts and long settlement cycles, these high embankments are prone to issues such as cracking and uneven settlement. This study utilizes three prediction methods— exponential curve model fitting, grey system theory, and backpropagation neural network (BPNN)—to forecast the settlement of a high embankment section of a highway in Guangxi. The prediction results from these three methods are compared with actual measured values. The findings indicate that among the three methods, the BPNN achieves the best overall fitting performance; the grey system theory meets accuracy requirements while being the least affected by different spatial locations; and the exponential curve fitting method involves lower computational costs but shows greater dependence on parameter selection, yet its accuracy improves significantly as the time interval increases.
Keywords: High embankment, Settlement prediction, Curve prediction method, Grey system theory, BP neural network
Received: 10 Sep 2025; Accepted: 21 Oct 2025.
Copyright: © 2025 Wang, Liao, Shen, Wang, Li and Yan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Beite Li, 2410302028@st.gxu.edu.cn
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