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Application of several optimization techniques for estimating TBM advance rate in granitic rocks 预览

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摘要 This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine(TBM)in different weathered zones of granite.For this purpose,extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang-Selangor raw water transfer tunnel in Malaysia.Rock properties consisting of uniaxial compressive strength(UCS),Brazilian tensile strength(BTS),rock mass rating(RMR),rock quality designation(RQD),quartz content(q)and weathered zone as well as machine specifications including thrust force and revolution per minute(RPM)were measured to establish comprehensive datasets for optimization.Accordingly,to estimate the advance rate of TBM,two new hybrid optimization techniques,i.e.an artificial neural network(ANN)combined with both imperialist competitive algorithm(ICA)and particle swarm optimization(PSO),were developed for mechanical tunneling in granitic rocks.Further,the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice.To evaluate the accuracy of the proposed models for both testing and training datasets,various statistical indices including coefficient of determination(R~2),root mean square error(RMSE)and variance account for(VAF)were utilized herein.The values of R~2,RMSE,and VAF ranged in 0.939-0.961,0.022-0.036,and 93.899-96.145,respectively,with the PSO-ANN hybrid technique demonstrating the best performance.It is concluded that both the optimization techniques,i.e.PSO-ANN and ICA-ANN,could be utilized for predicting the advance rate of TBMs;however,the PSO-ANN technique is superior.
出处 《岩石力学与岩土工程学报:英文版》 CSCD 2019年第4期779-789,共11页 Journal of Rock Mechanics and Geotechnical Engineering
作者简介 Corresponding author:Dr.Saffet Yagiz,obtained his BSc degree from Ankara University in Turkey,MSc degree from Missouri University of Science & Technology,and PhD degree from Colorado School of Mines (CSM) in USA.His main research interests are geotechnical engineering,rock mechanics,mechanized excavation,tunneling and scheduling.He has authored over 100 publications including peer-reviewed scientific journals,proceedings,industrial project reports and books.He has over twenty years of experience in management,research and teaching in the field of geotechnical engineering and institution.He is recognized reviewer of numerous mining,rock mechanics and geotechnical journals and also a member of the editorial board of Tunneling and Underground Space Technology.He is known for the studies of developing the modified CSM model for predicting TBM performance,TBM performance prediction formulae,and also proposing rock brittleness index and classification for excavation and construction in rock mass.He is a member of ISRM.E-mail address:saffet.yagiz@nu.edu.kz.
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