为了快速高效地确定深大圆井的变形值，提高设计效率，首先建立表征其空间力学效应的三维有限元数值模型，并结合现场监测数据对数值模型进行验证。随后基于该数值模型，建立典型地层条件和结构尺寸对圆井变形影响的数据库，选取随机森林和梯度提升树2种算法，采用单目标模型组合、链式回归组合和多输出组合3种组合方式，构建6组预测施工过程圆井整体位移的多输出预测模型。结果表明： 1）多目标-梯度提升(MO-GB)模型可以同时考虑多个预测指标，对应的最大位移值均方根误差（ERMS）为0.457，相比其他模型最小，且最大位移值和出现位置的决定系数(R2)均超过0.98，预测效果最佳。2）随着圆井开挖深度的增加，采用MO-GB模型预测得到的最大位移值和出现位置与三维有限元模型数值仿真计算结果基本一致，不同开挖深度预测位移点构成的折线可包络住相应阶段数值仿真变形曲线，预测值可为圆井结构设计提供参考，指导圆井结构选型，为施工图设计提供基础。
Herein, a method characterizing the spatial effects of
deep large-diameter shaft structures in water resource allocation projects is
established. Based on a three-dimensional finite
element model, a database is constructed to capture the influence of typical
geological conditions and structural dimensions on shaft deformation. To
further predict the overall structural deformation during the construction
process, a multi-output prediction model for shaft
displacement is proposed. Two algorithms, gradient boosting trees and random
forest, are selected, and three combinations of single-target
models, multi-output models, and regressor chain models
are employed. This results in the creation of six prediction models: single-target gradient boosting, multi-output
gradient boosting (MO-GB), regressor chain gradient
boosting, single-target random forest, multi-output random forest, and regressor chain random forest. The results
indicate that: (1) The MO-GB model considers multiple
prediction indicators, and its corresponding maximum deformation ERMS is 0.457, smaller than other models; it
performs the best in terms of predictive accuracy, with a determination value R2 for maximum displacement and its
corresponding location exceeds 0.98. (2) The maximum deformation value and its corresponding location
using MO-GB model align well with three-dimensional finite element model results, providing guidance for
design of shaft structure.
