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| Intelligent control of curtain grouting in limestone strata based on big data and machine learning |
| CHEN Zuyu1, 2*, BI Yifan1, XIAO Haohan2, JIANG Zhi?an3, WANG Jing4, CHE Yexi5, CAO Ruilang2 |
(1. State Key Laboratory of Water Engineering Ecology and Environment in Arid Area, Xi?an University of Technology, Xi?an, Shaanxi 710048, China; 2. State Key Laboratory of Basin Water Cycle Simulation and Regulation, China Institute of Water
Resources and Hydropower Research, Beijing 100048, China; 3. Sinohydro Foundation Engineering Co., Ltd., Tianjin
301700, China; 4. Yunnan Institute of Water and Hydropower Engineering Investigation, Design and Research, Kunming,
Yunnan 650051, China; 5. Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China) |
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Abstract To meet the intelligent control requirements for curtain grouting in limestone dam foundations, this study proposes a dynamic prediction method that integrates Grouting Energy Intensity (GEI) with machine learning, aiming to enhance process precision and adaptability in complex karst strata. Utilizing field data from the Dehou Reservoir and the Dongzhuang Water Control Project, a grouting database was established, incorporating key parameters such as grouting pressure (P), flow rate (Q), and Lugeon value (q). A P-Q time-series prediction model was developed using machine learning algorithms to forecast future grouting parameters dynamically. The results indicate that the incorporation of GEI significantly enhances the model?s coefficient of determination (R2). Additionally, a four-level rock mass permeability classification system based on Lugeon values and unit cement consumption was constructed, facilitating intelligent parameter matching and adaptive process control. This research offers a practical technical framework for intelligent construction and quality assurance in curtain grouting within karst regions.
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2026, Vol. 45 
