基于GRU-RF模型的TBM掘进参数预测研究  

Research on Prediction of TBM Tunnelling ParametersBased on GRU-RF Model

作　　者：满轲 曹子祥 刘晓丽[2] 宋志飞 刘汭琳 MAN Ke;CAO Zixiang;LIU Xiaoli;SONG Zhifei;LIU Ruilin(College of Civil Engineering,North China University of Technology,Beijing 100144,China;State Key Laboratory of Hydroscience and Hydraulic Engineering,Tsinghua University,Beijing 100084,China)

机构地区：[1]北方工业大学土木工程学院,北京100144 [2]清华大学,水沙科学与水利水电工程国家重点试验室,北京100084

出　　处：《应用基础与工程科学学报》2023年第6期1519-1539,共21页Journal of Basic Science and Engineering

基　　金：国家重点研发计划项目(2018YFC1504801,2018YFC1504902);国家自然科学基金项目(51522903,51774184);清华大学水沙科学与水利水电工程国家重点实验室项目(2019-KY-03);北方工业大学毓杰项目(216051360020XN199/006)。

摘　　要：在TBM施工过程中,仅依靠主司机主观经验确定掘进参数可能导致施工效率低、卡机、刀盘磨损严重和围岩坍塌等问题.采用最小二乘法将门控循环单元(GRU)和随机森林(RF)进行集成,开发一种TBM掘进参数预测模型(GRU-RF模型),并利用灰色关联度分析方法对模型输入特征进行筛选.GRU-RF模型预测掘进参数推力、转速和贯入度的拟合优度(R2)平均值为0.81、平均绝对百分比误差(MAPE)平均值为8.32%、均方根误差(RMSE)平均值为0.74,相对误差(RE)平均值几乎为0.选择双向长短时记忆(BiLSTM)模型、误差反向传播神经网络(BPNN)模型、GRU-BPNN模型和BPNN-RF模型进行各掘进参数预测结果误差的对比分析.结果表明,GRU-RF模型的预测准确度和泛化性最高,最小二乘法集成传统机器学习模型和深度学习模型可以构建强预测性能的预测模型.最后证明了灰色关联度分析在预测模型输入特征筛选中的必要性.该研究为实际工程掘进参数预测提供了指导,有助于推动TBM智能化施工.During TBM tunnelling,relying solely on the subjective experience of the lead driver to determine tunnelling parameters may cause some problems such as low construction efficiency,jamming,severe cutterhead abrasion,and collapse of the surrounding rock.In this paper,the least squares method was used to integrate the gate recurrent unit(GRU)and random forest(RF)for the development of a TBM tunnelling parameters predictive model(GRU-RF model),and the grey relational analysis method was employed to screen the input features of the model.The average of goodness of fit(R2),mean absolute percentage error(MAPE),and root mean square error(RMSE)of thrust,rotational speed and penetration predicted by the GRU-RF model were 0.81,8.32%,and 0.74,respectively,and the average relative error(RE)was almost zero.The bidirectional long short-term memory(BiLSTM)model,back propagation neural network(BPNN)model,GRU-BPNN model,and BPNN-RF model also were selected to compare and analyze the prediction error of each tunnelling parameter.The analysis results showed that the GRU-RF model had the highest prediction accuracy and generalization ability.And the integration of a traditional machine learning model and a deep learning model using the least squares method can construct a predictive model with strong predictive performance.Finally,the necessity of using the grey relational analysis method to select the input features of the prediction model was proved.This study provides guidance for the prediction of actual engineering tunnelling parameters and contributes to the advancement of intelligent TBM construction.

关 键 词：隧道工程 TBM 岩体参数 掘进参数 GRU-RF模型 最小二乘法 灰色关联度分析 

分 类 号：U455.43[建筑科学—桥梁与隧道工程]