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GAPSO-LightGBM-based Intelligent Prediction Method of Surrounding Rock Grade in TBM Tunnelling
(1. State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059; 2. College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059;3. Power China Chengdu Engineering Corporation Limited, Chengdu 610072)
Abstract The tunnel boring machine (TBM) excavation parameters are sensitive to changes in geological condi? tions. Accurately and real-time identifying the quality grade of tunnel surrounding rocks is crucial for efficient tunnelling and geological hazard prevention. To address this, pre-processing is performed on TBM excavation parameter data to obtain a high-quality database. Selected the LightGBM as the basic model, a surrounding rock grade prediction model based on GAPSO-LightGBM is constructed by introducing the GAPSO optimization algorithm for hyperparameter tuning. The model's performance is compared with PSO-LightGBM, GA-LightGBM, LightGBM, XGBoost, and Random Forest models. The results show that the GAPSO-LightGBM-based surrounding rock grade prediction model outperforms the traditional models. For the prediction of grades Ⅱ, Ⅲ, and Ⅳ surrounding rocks, the F1 scores are 0.849, 0.871, and 0.893, with an accuracy of 87.5%. The field validation demonstrate that this method can effectively predict the changes in surrounding rock quality grades and provide reference for practical engineering.
Abstract：
The tunnel boring machine (TBM) excavation parameters are sensitive to changes in geological condi? tions. Accurately and real-time identifying the quality grade of tunnel surrounding rocks is crucial for efficient tunnelling and geological hazard prevention. To address this, pre-processing is performed on TBM excavation parameter data to obtain a high-quality database. Selected the LightGBM as the basic model, a surrounding rock grade prediction model based on GAPSO-LightGBM is constructed by introducing the GAPSO optimization algorithm for hyperparameter tuning. The model's performance is compared with PSO-LightGBM, GA-LightGBM, LightGBM, XGBoost, and Random Forest models. The results show that the GAPSO-LightGBM-based surrounding rock grade prediction model outperforms the traditional models. For the prediction of grades Ⅱ, Ⅲ, and Ⅳ surrounding rocks, the F1 scores are 0.849, 0.871, and 0.893, with an accuracy of 87.5%. The field validation demonstrate that this method can effectively predict the changes in surrounding rock quality grades and provide reference for practical engineering.
ZHANG Huan1,
2 ZHANG Shishu3 LI Tianbin1,
2 YANG Gang1 etc
.GAPSO-LightGBM-based Intelligent Prediction Method of Surrounding Rock Grade in TBM Tunnelling[J] MODERN TUNNELLING TECHNOLOGY, 2025,V62(2): 98-109
2025, Vol. 62 