Research on intelligent prediction of surface deformation induced by super-large diameter shield tunneling in silty clay ground
Zhong Jianmin1, Zhang Xiaoping2*, Zhang Liangliang3, Wu Ke2, Tang Shaohui4, Lu Jingke2, Chen Peng5, You Shaoqiang5
1. Urban Construction Group Co., Ltd., Jinan 250014, Shandong, China; 2. Key Laboratory of Safety for Geotechnical and Structural Engineering of Hubei Province, School of Civil and Architecture Engineering, Wuhan University, Wuhan 430072, Hubei, China; 3. China Railway Siyuan Survey and Design Group Co., Ltd., Wuhan 430063, Hubei, China; 4. School of Civil Engineering and Architecture, Guangxi University, Nanning 530004, Guangxi, China; 5. Large Diameter Shield Engineering Co., Ltd., China Railway 14th Bureau Group, Nanjing 211899, Jiangsu, China
Abstract: To accurately predict surface deformation induced by super-large-diameter shield tunneling in silty clay, an intelligent prediction model based on machine learning was developed. The physical and mechanical parameters of the soil were corrected by considering stratum thickness and tunnel burial depth. Three surface-deformation prediction models, namely, a back-propagation(BP)neural network, a support vector machine(SVM), and a random forest(RF), were established in MATLAB. The optimum hyperparameter combinations were determined by cross-validation, grid search, and empirical formulas. Using data from the Jiluo Road Yellow River Tunnel in Jinan, the surface deformation induced by shield tunneling was predicted. Model accuracy was evaluated using mean square error(EMS), root mean square error(ERMS), mean absolute error(EMA), and the coefficient of determination(R2). The results showed that the RF model produced the smallest prediction error, followed by the BP neural-network model, whereas the SVM model produced the largest error and had relatively weak generalization ability. The proposed model provided a reference for predicting surface deformation induced by super-large-diameter shield tunneling under similar engineering-geological conditions.
钟建敏,张晓平,张亮亮,吴柯,唐少辉,鲁婧轲,陈鹏,游少强. 粉质黏土地层超大直径盾构掘进诱发地表变形智能预测研究[J]. 隧道与地下工程灾害防治, 2026, 8(3): 115-121.
Zhong Jianmin, Zhang Xiaoping, Zhang Liangliang, Wu Ke, Tang Shaohui, Lu Jingke, Chen Peng, You Shaoqiang. Research on intelligent prediction of surface deformation induced by super-large diameter shield tunneling in silty clay ground. Hazard Control in Tunnelling and Underground Engineering, 2026, 8(3): 115-121.
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2026, Vol. 8 
