Ding Zhi, Tu Wenrong, Li Xinjia
Department of Civil Engineering, Hangzhou City University, Huzhou Street 51#, Hangzhou, 310015, Zhejiang, China.
Key Laboratory of Safe Construction and Intelligent Maintenance for Urban Shield Tunnels of Zhejiang Province, Hangzhou, 310015, Zhejiang, China.
Sci Rep. 2024 Sep 20;14(1):21935. doi: 10.1038/s41598-024-73025-3.
This study investigates the prediction of ground loss rate during soft-soil shield tunneling using Peck's back analysis method and XGBoost model. Bayesian optimization is employed to determine optimal hyperparameters, ensuring comprehensive and efficient model tuning. The XGBoost model is compared with Random Forest (RF) and Support Vector Machine (SVM) models to benchmark its performance. The results demonstrate the superior accuracy and robustness of the XGBoost model. Also, the results show that the soil properties and the grouting factors of the excavation face affect the duration of the instantaneous settlement of the ground surface. There is a specific correlation between the depth-to-diameter ratio, the coefficient of variation in the advancing speed of the shield machine, the maximum surface subsidence, and the ground loss rate. The prediction model of the ground loss rate based on the combined approach of Peck back analysis and eXtreme Gradient Boosting and Bayesian optimization has high reliability in soft-soil layers, and this method can provide a specific reference for predicting construction risk in related projects.
本研究采用Peck反分析方法和XGBoost模型对软土地层盾构隧道施工中的地表损失率进行预测。采用贝叶斯优化方法确定最优超参数，确保模型调整全面且高效。将XGBoost模型与随机森林（RF）模型和支持向量机（SVM）模型进行比较，以评估其性能。结果表明XGBoost模型具有更高的精度和鲁棒性。此外，结果还表明，土体性质和开挖面注浆因素会影响地表瞬时沉降的持续时间。盾构机的径深比、推进速度变异系数、最大地表沉降与地表损失率之间存在特定的相关性。基于Peck反分析、极端梯度提升和贝叶斯优化相结合的地表损失率预测模型在软土地层中具有较高的可靠性，该方法可为相关工程施工风险预测提供具体参考。
