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Control of existing tunnel deformation caused by shield adjacent undercrossing construction using interpretable machine learning and multiobjective optimization
Automation in Construction ( IF 9.6 ) Pub Date : 2024-12-24 , DOI: 10.1016/j.autcon.2024.105943
Hongyu Chen, Jun Liu, Geoffrey Qiping Shen, Zongbao Feng
Automation in Construction ( IF 9.6 ) Pub Date : 2024-12-24 , DOI: 10.1016/j.autcon.2024.105943
Hongyu Chen, Jun Liu, Geoffrey Qiping Shen, Zongbao Feng
A hybrid intelligent framework is proposed in this paper to reduce the existing tunnel deformation caused by shield adjacent undercrossing construction (SAUC). A Bayesian optimization natural gradient boosting (BO-NGBoost) model for existing tunnel deformation prediction is developed, and the Shapley additive explanations (SHAP) approach is used to analyze the interpretability of the prediction model. The multiobjective evolutionary algorithm based on decomposition (MOEA/D) is used to optimize the construction parameters. The applicability and validity of the proposed method are tested in a case study from the Wuhan Metro. The results indicate that (1) the established BO-NGBoost existing tunnel deformation prediction model shows high accuracy. (2) Through SHAP analysis, the importance of each input parameter to the existing tunnel deformation is identified, and the key shield optimization parameters are defined. (3) By using the developed BO-NGBoost-MOEA/D algorithm to optimize the key parameters, the existing tunnel deformation is effectively controlled.
中文翻译:
使用可解释机器学习和多目标优化控制盾构相邻下交叉施工引起的现有隧道变形
该文提出了一种混合智能框架,以减少盾构相邻下交施工 (SAUC) 引起的现有隧道变形。建立了用于现有隧道变形预测的贝叶斯优化自然梯度提升 (BO-NGBoost) 模型,并采用 Shapley 加法解释 (SHAP) 方法分析预测模型的可解释性。采用基于分解的多目标进化算法 (MOEA/D) 对构造参数进行优化。在武汉地铁的案例研究中检验了所提方法的适用性和有效性。结果表明:(1)建立的BO-NGBoost现有隧道变形预测模型具有较高的精度。(2) 通过 SHAP 分析,确定每个输入参数对现有隧道变形的重要性,并定义关键的盾构优化参数。(3)利用开发的 BO-NGBoost-MOEA/D 算法优化关键参数,有效控制现有隧道变形。
更新日期:2024-12-24
中文翻译:
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使用可解释机器学习和多目标优化控制盾构相邻下交叉施工引起的现有隧道变形
该文提出了一种混合智能框架,以减少盾构相邻下交施工 (SAUC) 引起的现有隧道变形。建立了用于现有隧道变形预测的贝叶斯优化自然梯度提升 (BO-NGBoost) 模型,并采用 Shapley 加法解释 (SHAP) 方法分析预测模型的可解释性。采用基于分解的多目标进化算法 (MOEA/D) 对构造参数进行优化。在武汉地铁的案例研究中检验了所提方法的适用性和有效性。结果表明:(1)建立的BO-NGBoost现有隧道变形预测模型具有较高的精度。(2) 通过 SHAP 分析,确定每个输入参数对现有隧道变形的重要性,并定义关键的盾构优化参数。(3)利用开发的 BO-NGBoost-MOEA/D 算法优化关键参数,有效控制现有隧道变形。