TBM掘进速度预测模型研究  

Prediction Models of the TBM Penetration Rate

作　　者：吕根根 张晓平 刘泉声[1,2] 潘少林[1,2] LYU Gengen;ZHANG Xiaoping;LIU Quansheng;PAN Shaolin(School of Civil Engineering,Wuhan University,Wuhan 430072,China;Key Laboratory of Safety for Geotechnical and Structural Engineering of Hubei Province,Wuhan University,Wuhan 430072,China)

机构地区：[1]武汉大学土木建筑工程学院,武汉430072 [2]武汉大学岩土与结构工程安全湖北省重点实验室,武汉430072

出　　处：《河南科学》2019年第8期1289-1295,共7页Henan Science

基　　金：国家重点基础研究发展计划（973）（2015CB058102）

摘　　要：随着机械制造技术的进步,全断面岩石隧道掘进机被广泛应用于深、长、大隧洞的开挖,鉴于全断面岩石隧道掘进机(TBM)对地质条件十分敏感,且其前期投入巨大,采用合适的方法、准确地预测TBM掘进速度对TBM施工的进度安排和成本估计十分重要.基于纽约皇后NO.3隧道153组实测岩体参数(UCS、PSI、DWP、BTS、α)和TBM掘进速度(PR),分别采用BP神经网络和CART算法建立TBM掘进速度预测模型,与已有预测模型对比发现,CART预测模型预测精度更高更易于不同工程相互借鉴,且在部分岩体参数缺失的情况下也能对TBM掘进速度进行有效预测.With the development of manufacturing technology,the TBM has been widely used in tunneling. Since the performance of TBM is sensitive to the geological condition,an accurately prediction about TBM penetration rate is very important for the construction schedule and cost estimation. In the study,the BP neural network and CART algorithm were used for the prediction of penetration rate based on the measured data of the Queens Water Tunnel # 3. According to the comparison with the existing models,it is found that the CART model is more accurate and easier to be applied in different project. Furthermore,the CART model can be used for the prediction of PR in the case that one or several rock mass parameters are deficient.

关 键 词：TBM掘进速度 CART BP神经网络 预测 

分 类 号：U45[建筑科学—桥梁与隧道工程]