基于BO-XGBoost的盾构掘进地表变形预测  

SURFACE DEFORMATION PREDICTION OF SHIELD TUNNELING BASED ON BAYESIAN OPTIMIZATION XGBOOST

作　　者：李鑫家 丁智[1,2,3] 张霄 罗宇勤 LI Xinjia;DING Zhi;ZHANG Xiao;LUO Yuqin(College of Civil Engineering and Architecture,Zhejiang University,Hangzhou 310058,China;Department of Civil Engineering,Zhejiang University City College,Hangzhou 310015,China;Key Laboratory of Safe Construction and Intelligent Maintenance for Urban Shield Tunnels of Zhejiang Province,Hangzhou 310015,China;Zhejiang Huadong Mapping and Engineering Safety Technology Co.,Ltd.,Hangzhou 310014,China)

机构地区：[1]浙江大学建筑工程学院,杭州310058 [2]浙大城市学院土木工程系,杭州310015 [3]浙江省城市盾构隧道安全建造与智能养护重点实验室,杭州310015 [4]浙江华东测绘与工程安全技术有限公司,杭州311122

出　　处：《低温建筑技术》2022年第11期102-107,共6页Low Temperature Architecture Technology

基　　金：国家自然科学基金资助项目(52178400);浙江省自然科学基金重点项目(LHZ20E080001);浙江省重点研发计划资助项目(2020C01102)。

摘　　要：在城市地铁建设中,土压平衡盾构掘进引起的地表变形大小受到众多因素的影响,因此往往难以准确预测。针对这一问题,提出了贝叶斯优化(BO)的极端梯度提升(XGBoost)预测模型。模型输入考虑了地质参数、施工参数与隧道几何参数,并通过特征选择分析了各特征的重要性,同时降低了输入参数的维度。采用贝叶斯优化算法确定了最优超参数,并将预测结果与手动调参的XGBoost、随机森林(RF)、支持向量机(SVM)模型作对比。结果表明集成算法在预测中比传统SVM表现更好,贝叶斯优化算法提升了XGBoost模型的预测性能;隧道埋深与直径对XGBoost预测模型影响最大,隧道上方淤泥质土层、黏土层、粉砂土层、风化岩层对模型的影响依次降低,刀盘转速和平均土仓压力的相对重要度也较高。In urban subway construction,the surface deformation caused by EPB shield tunneling was affected by many factors,so it was always difficult to predict accurately.To solve this problem,a Xtreme Gradient Boosting(XGBoost)prediction model based on Bayesian optimization was proposed.Geological parameters,construction parameters,and tunnel geometric parameters were considered in the model input.The importance of each feature was analyzed through feature selection,and the dimension of input parameters was reduced.The optimal hyperparameters were determined by Bayesian optimization algorithm,and the prediction results were compared with the manually adjusted XGBoost,Random Forest(RF),and Support Vector Machines(SVM)models.The results show that the integrated algorithm performs better than the traditional SVM in prediction.Bayesian Optimization algorithm improves the prediction performance of XGBoost model.The buried depth and diameter of the tunnel have the greatest impact on the XGBoost prediction model,the influence of muddy soil layer,clay layer,silt soil layer,and weathered rock layer above the tunnel on the model decreases in turn;the relative importance of cutter head speed and average chamber pressure is also high.

关 键 词：地铁隧道 盾构掘进 变形预测 贝叶斯优化 极端梯度提升 

分 类 号：TU478[建筑科学—结构工程]