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| Rock mass structural characterization based on machine vision and grout take prediction |
| OUYANG Shaoming1, DING Changdong1, 2*, ZHOU Liming1, DING Xiang3, LUO Rong1, HU Dawei2, 4 |
| (1. Key Laboratory of Geotechnical Mechanics and Engineering of the Ministry of Water Resources, Changjiang River Scientific Research Institute, Wuhan, Hubei 430010, China; 2. State Key Laboratory of Geomechanics and Geotechnical Engineering Safety, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, Hubei 430071, China; 3. School of Civil Engineering and Architecture, Hubei University of Technology, Wuhan, Hubei 430068, China; 4. State Key Laboratory of Deep Geothermal Resources, China University of Geosciences, Wuhan, Hubei 430074, China) |
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Abstract Aiming at “black-box” technical bottleneck characterized by the inherent concealment and evaluation challenges in underground cavern grouting, this study proposes an integrated framework combining deep learning and machine learning for refined characterization of rock mass structures and quantitative assessment of grouting performance. The results indicated: (1) By employing high-resolution image acquisition and 3D point-cloud reconstruction technologies to construct a dataset of the surrounding rock, an efficient fracture identification and segmentation method based on the CBAM-UKAN model was developed. The proposed method achieved IoU and Dice coefficients of 0.76 and 0.86, respectively, on the validation set. (2) Through the integration of morphological closing, skeleton extraction, and a multi-scale geometric quantification algorithm chain, key fracture parameters such as length, width, and area were extracted, providing essential inputs for the rock mass structural characterization. (3) Comparative analysis of four ensemble learning models identified the simulated annealing-optimized Gradient Boosting Regression Tree (SA-GBRT) as the best-performing predictive model, achieving an R² value of 0.79 on the test set. The proposed approach enables reliable prediction of unit ash consumption based on rock mass fracture characteristics and grouting construction parameters, thereby providing a quantitative basis for pre-evaluation of seepage-control performance. These findings offer significant practical value for the intelligent optimization and scientific decision-making of grouting operations in underground engineering.
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2026, Vol. 45 
