基于CatBoost-NSGA-Ⅲ的盾构隧道施工参数分析及优化控制

Analysis and Optimal Control of Shield Tunnel Construction Parameters Using Categorical Boosting-Nondominated Sorting Genetic Algorithm-Ⅲ

由于盾构在施工过程中受环境、设备和作业等不确定因素的影响,导致隧道开挖的安全性、效率和成本难以协调。针对这种情况,以武汉轨道交通某标段施工为依托,采用基于梯度增强(CatBoost)和非支配排序遗传算法(NSGA-Ⅲ)的混合算法,在全面考虑掘进效率、成本、安全风险等因素的基础上,选择以推进速度、掘进比能、刀具磨损量为目标,构建施工参数智能控制决策系统。首先,通过CatBoost回归模型预测盾构隧道推进速度、掘进比能和刀具磨损量,得到控制目标的适应度函数;然后,基于CatBoost预测模型构建的适应度函数,利用CatBoost-NSGA-Ⅲ进行施工参数的多目标优化;最后,通过模糊决策法从多个Pareto最优解集中选出最佳的施工参数组合,为隧道盾构掘进参数智能预测与优化提供参考。结果表明:1)Catboost可以进行模型精准预测,拟合优度R2大于0.9,均方根误差RMSE和平均绝对误差MAE较小;2)Catboost-NSGA-Ⅲ多目标优化,模糊决策法确定最优方案。经过优化,相较于实测数据的平均值,掘进比能和刀具磨损量分别降低5.3%和13.5%、掘进速度提升6.3%,为盾构隧道的智能化掘进控制和管理决策提供依据。

Due to the challenges posed by varying factors such as environmental conditions,equipment performance,and operational procedures,achieving an optimal balance between safety,efficiency,and cost in tunnel excavation is complex.To address this problem,a case study of a section of the Wuhan rail transit is conducted,proposing a hybrid intelligent framework that integrates categorical boosting(CatBoost)and nondominated sorting genetic algorithm(NSGA)-Ⅲ.This framework aims to develop an intelligent control decision-making system for construction parameters,considering factors such as advance speed,tunneling specific energy,and tool wear.The approach begins with the use of the CatBoost model to predict advance speed,specific energy,and tool wear,which then informs the fitness function for control targets.Following this,the CatBoost-NSGA-Ⅲ algorithm is employed for multi-objective optimization of construction parameters based on the fitness function derived from the CatBoost model.Optimal parameter combinations are subsequently selected from the multiple Pareto optimal solutions using a fuzzy decision method.This approach supports intelligent prediction and optimization of construction parameters for shield tunneling.Key findings include:(1)The CatBoost model provides accurate predictions with a goodness of fit R2 exceeding 0.9.(2)Multi-objective optimization using the CatBoost-NSGA-Ⅲ algorithm,combined with fuzzy decision-making,determines the optimal scheme.Compared to the average measured data,this scheme reduces specific driving energy and tool wear by 5.3% and 13.5%,respectively,while increasing advance speed by 6.3%,thereby enhancing intelligent management and decision-making for shield tunneling.