浅埋地铁车站施工期地表变形风险预警

doi:10.6040/j.issn.1672-3961.0.2022.295

摘要/Abstract

摘要： 为研究浅埋地铁车站主体结构风险预报预警,建立基于实测数据与贝叶斯优化的随机森林模型(random forest model based on Bayesian optimization, BA-RF)机器学习的趋势分析方法,通过研究主体结构上方地表与建筑物沉降、竖井关键点位移、拱顶沉降等实时数据的趋势,预报未来关键位移短时期达到的最大值,并与控制值进行对比,当前序实测数据的时间δ为5、15、25 d时,平均绝对误差(y_MAE)0.377、0.303和0.270,均方根误差(yRMSE)分别为0.853、0.463和0.509,实现了对结构风险的短期预报预警。研究结果表明:预报方法可以在前序实测数据宽度较小的情况下实现极小的偏移比与绝对偏移比,具备良好的预报效果,综合预报方法在实际监测过程中取得良好工程应用效果。

关键词: 隧道工程, 车站结构, 机器学习, 风险预报, 地表变形

中图分类号:

TU47

李鸿钊,张庆松,刘人太,陈新,辛勤,石乐乐. 浅埋地铁车站施工期地表变形风险预警[J]. 山东大学学报 (工学版), 2023, 53(6): 82-91.

LI Hongzhao, ZHANG Qingsong, LIU Rentai, CHEN Xin, XIN Qin, SHI Lele. Risk state early warning of the surface deformation of shallow buried subway station[J]. Journal of Shandong University(Engineering Science), 2023, 53(6): 82-91.

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