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Predicting TBM penetration rate in hard rock condition:A comparative study among six xgb-based metaheuristic techniques

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Geoscience Frontiers 2021年第3期12卷 201-213页

作者： Jian Zhou Yingui Qiu Danial Jahed Armaghani Wengang Zhang Chuanqi Li Shuangli Zhu Reza Tarinejad School of Resources and Safety Engineering Central South UniversityChangsha 410083China Institute of Research and Development Duy Tan UniversityDa Nang 550000Vietnam School of Civil Engineering Chongqing UniversityChongqing 400045China Faculty of Civil Engineering University of Tabriz29 Bahman Blvd51666 TabrizIran

A reliable and accurate prediction of the tunnel boring machine(TBM)performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling *** research aims to develop six hybrid models of extreme gradient boosting(xgb)which are optimized by gray wolf optimization(GWO),particle swarm optimization(PSO),social spider optimization(SSO),sine cosine algorithm(SCA),multi verse optimization(MVO)and moth flame optimization(MFO),for estimation of the TBM penetration rate(PR).To do this,a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation,the rock mass rating,Brazilian tensile strength(BTS),rock mass weathering,the uniaxial compressive strength(UCS),revolution per minute and trust force per cutter(TFC),were set as inputs and TBM PR was selected as model *** with the mentioned six hybrid models,four single models i.e.,artificial neural network,random forest regression,xgb and support vector regression were also built to estimate TBM PR for comparison *** models were designed conducting several parametric studies on their most important parameters and then,their performance capacities were assessed through the use of root mean square error,coefficient of determination,mean absolute percentage error,and *** of this study confirmed that the best predictive model of PR goes to the PSO-xgb technique with system error of(0.1453,and 0.1325),R^(2) of(0.951,and 0.951),mean absolute percentage error(4.0689,and 3.8115),and a10-index of(0.9348,and 0.9496)in training and testing phases,*** developed hybrid PSO-xgb can be introduced as an accurate,powerful and applicable technique in the field of TBM performance *** conducting sensitivity analysis,it was found that UCS,BTS and TFC have the deepest impacts on the TBM PR.

关键词： TBM penetration rate Hard rock xgb-based hybrid model Predictive model Metaheuristic optimization

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