基于CatBoost-MOEAD的大直径泥水盾构姿态多目标预测与优化

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中文摘要：为避免盾构掘进过程中出现蛇形、轴线偏离等姿态异常问题影响施工安全,提出一种结合类别提升(CatBoost)算法和基于分解的多目标优化算法(MOEAD)的大直径泥水盾构姿态控制方法;构建一个盾构姿态预测模型,该模型包含19个输入参数和6个输出参数,利用CatBoost算法构建输入参数与输出参数之间的非线性映射关系;采用沙普利加性解释法(SHAP)分析输入参数对盾构姿态的影响;结合多目标优化算法构建CatBoost-MOEAD盾构姿态多目标优化模型,将所提模型运用到武汉长江大直径泥水盾构隧道工程中,分析验证所提方法的适用性和有效性.结果表明:CatBoost预测模型能够高效地预测大直径泥水盾构的姿态,其中6个盾构姿态目标的决定系数范围为0.931～0.974,均方根误差范围为0.030～0.880,误差范围为0.039～1.057;对盾构姿态影响较大的施工参数中推进组推力对盾构姿态的影响最为显著;通过研发的CatBoost-MOEAD盾构姿态多目标优化方法,盾构姿态的优化效果显著,优化率可达38.86％.

外文标题：Multi-objective prediction and optimization of large-diameter slurry shield posture based on CatBoost-MOEAD

外文摘要：To avoid abnormal attitude problems such as serpentine and axis deviation during shield tunneling affecting construction safety,a large-diameter slurry shield attitude control method combining CatBoost algorithm and MOEAD.A shield posture prediction model was developed with 19 input parameters and 6 output parameters,and the CatBoost algorithm was used to develop a nonlinear mapping relationship between input and output parameters.The SHAP was used to analyze the effects of input parameters on shield posture.The CatBoost-MOEAD shield posture multi-objective optimization model was coupled with the multi-objective optimization algorithm.Then the proposed model performance was validated against the Wuhan Yangtze River large-diameter slurry shield tunnel project.The results showed that the CatBoost prediction model can efficiently predict the posture of large-diameter mud-water shields.The determination coefficients of the six shield posture objectives ranged from 0.931 to 0.974,the root-mean-square errors ranged from 0.030 to 0.880,and the errors ranged from 0.039 to 1.057.The thrust of the propulsion group has the most significant impact on shield attitude among the major construction parameters.The proposed CatBoost-MOEAD multi-objective optimization method for shield attitude had a great performance in optimization effect with a maximum value of 38.86％.

外文关键词：

categorical boosting (CatBoost)multi-objective evolutionary algorithm based on decomposition (MOEAD)large-diameter slurry shieldshield posturemultiobjective optimizationShapley additive explanations (SHAP)

作者：

吴贤国、刘俊、王静怡、覃亚伟

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作者单位：

华中科技大学土木与水利工程学院,湖北武汉430074

武汉华中科大检测科技有限公司,湖北武汉430074

关键词：

类别提升(CatBoost) 基于分解的多目标优化算法(MOEAD) 大直径泥水盾构盾构姿态多目标优化沙普利加性解释法(SHAP)

出版年：

2024

DOI：

10.16265/j.cnki.issn1003-3033.2024.10.1718