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Open Access

ARTICLE

AI-Enabled Prediction and Control of Settlement during Construction of Concrete-Faced Rockfill Dams: Method and Engineering Application

Zeyu Wang1,#,*, Ying Yu1,#, Xin Huang1, Guangwen Guo1, Degao Zou2, Kaiyan Tan1, Jiayu Long3, Song Gao3
1 Science and Technology Digital Equipment Division, China Gezhouba Group Co., Ltd., Wuhan, China
2 State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, China
3 Department of Civil and Environmental Engineering, Imperial College London, London, UK
* Corresponding Author: Zeyu Wang. Email: email
# These authors contributed equally to this work
(This article belongs to the Special Issue: Selected Papers from the 10th International Conference on Civil Construction and Structural Engineering (I3CSE 2026))

Structural Durability & Health Monitoring https://doi.org/10.32604/sdhm.2026.084515

Received 23 April 2026; Accepted 08 June 2026; Published online 07 September 2026

Abstract

(1) Background: During the construction of the concrete-faced sand-gravel dam with a height of 247 m at the Dashixia Water Conservancy Project in Xinjiang, China, pronounced stage-related changes in dam settlement and marked settlement differences among dam zones were observed. Under these conditions, conventional settlement monitoring and prediction methods were insufficient to support the real-time decision-making required for on-site construction. (2) Methods: To address this practical engineering problem, this study developed an AI-enabled method for settlement prediction and control during construction. Settlement monitoring data collected throughout construction were combined with key dam filling parameters to establish a multi-source dataset for the construction period. On this basis, a settlement prediction model with rolling updates based on random forest regression was established. The model can predict the settlement development trend, support the identification of potential nonuniform settlement zones, and provide references for construction control based on actual construction conditions. (3) Results: The results of field application and quantitative error evaluation show that the predicted settlement is generally consistent with the measured settlement trend. The model effectively captures settlement responses during construction stoppage and the subsequent resumption of filling. (4) Conclusions: The proposed method provides practical quantitative support for optimizing construction organization and improving the precision of settlement risk control.

Keywords

Concrete-faced rockfill dam; settlement during construction; settlement prediction; monitoring data; rolling prediction; construction control

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