基于随机森林与粒子群算法的隧道掘进机操作参数地质类型自适应决策  

Geological adaptive TBM operation parameter decision based on random forest and particle swarm optimization

作　　者：刘明阳 陶建峰[1] 覃程锦[1] 余宏淦 刘成良[1] LIU Mingyang;TAO Jianfeng;QIN Chengjin;YU Honggan;LIU Chengliang(School of Mechanical Engineering,Shanghai Jiaotong University,Shanghai 200240,China)

机构地区：[1]上海交通大学机械与动力工程学院,上海200240

出　　处：《中南大学学报（自然科学版）》2023年第4期1311-1324,共14页Journal of Central South University:Science and Technology

基　　金：国家重点研发计划项目(2018YFB1702503)。

摘　　要：考虑到隧道掘进机的性能对地质条件比较敏感且其操作依赖于司机经验,提出基于随机森林和粒子群算法的隧道掘进机操作参数地质条件自适应决策方法。利用随机森林(RF)分别建立地质类型、操作参数与推进速度、刀盘转矩的映射关系模型;结合映射关系模型,构建以盾构机推进速度最大为目标,以刀盘转速、螺旋输送机转速、总推力、土仓压力4个操作参数为控制变量的优化方程;利用粒子群算法(PSO)求解各地质类型地层中的最优操作参数决策结果。通过新加坡某地铁工程施工数据验证所提方法的有效性和优越性。研究结果表明:建立的随机森林模型中推进速度和刀盘转矩预测的决定系数R^(2)分别达到0.936和0.961,均大于adaboost、多元线性回归、岭回归、支持向量回归和深度神经网络模型中相应的R2;基于粒子群算法的操作参数决策方法能够准确求解操作参数最优解,寻优用时均比遗传算法、蚁群算法和穷举法的短。本文所提决策方法使隧道掘进机在该施工段的福康宁卵石地层、句容地层IV、句容地层V、海洋黏土地层中的推进速度分别提升了67.2%、41.8%、53.6%和15.0%。Considering that the performance of TBM is affected by geological condition and driver experience,a geological adaptive TBM operation parameter decision based on random forest(RF)and particle swarm optimization algorithm(PSO)was proposed.RF was used to establish the mapping relation model between geological types,operating parameters and thrust speed,cutter head torque.An optimization equation was established using the mapping relationship model in which the maximum TBM thrust speed was taken as the target,and cutterhead speed,screw conveyor speed,total thrust and earth pressure were taken as control variables.PSO was used to solve the optimal combination of operating parameters for each geological type.The validity and superiority of the proposed method were verified by the construction data of a subway project in Singapore.The results show that the R ^(2) of the driving speed and cutter head torque predicted by random forest model reaches 0.936 and 0.961,which are greater than those of adaboost,multiple linear regression,ridge regression,SVR and DNN.PSO can accurately solve the optimal solution of operating parameters,and the time consumption is shorter than that of genetic algorithm,ant colony algorithm and exhaustive algorithm.By using the proposed method,the TBM thrust speed increases by 67.2%,41.8%,53.6%,15.0%in the strata of Fokonnen Pebble Formation,Jurong Formation IV,Jurong Formation V and Marine Clay Formation in this construction section,respectively.

关 键 词：隧道掘进机 操作参数决策 随机森林 粒子群优化 

分 类 号：TH17[机械工程—机械制造及自动化] TU62[建筑科学—建筑技术科学]