13970

12694

كارشناسي ارشد

مكانيك سنگ

اصفهان: دانشگاه صنعتي اصفهان، دانشكده معدن

۱۳۹۷

سيزده، ۹۶ص.: مصور، جدول، نمودار

ابراهيم قاسمي

[سافت ياگيز]

استاد داور (داخلي): راحب باقر پور; داور (خارجي): حسن طباطبايي

1397/07/21

كتابنامه

معدن

مهندسي معدن

ID12694

Evaluation of tunnel boring machine TBM performance using stochastic modeling Peyman Jafar Shirzad Shirzad peyman@yahoo com Date of Submission October 3 2118 Department of Mining Engineering Isfahan University of Technology Isfahan Iran Degree M Sc Language Persian Supervisor E Ghasemi e ghasemi@cc iut ac ir Abstract In recent decades construction of urban and suburban tunnels has become very important in order toreduce urban pollution conserve fossil fuels reducing travel distances and energy costs Among tunnelexcavation methods the application of mechanized excavation techniques is increasing by technologyadvances One of mechanized excavation methods is excavation by tunnel boring machines TBMs Intunnel boring machines the prediction of machine performance is a very important and critical issue because it is affected by mechanical parameters of machines geological parameters and operationalparameters In this research first penetration rate of TBM open type applied in Queens Tunnel of NewYork is predicted by mathematical equation Then the influence of uncertainty on this parameter issimulated using the Monte Carlo stochastic modeling and the mathematical relation obtained from theprevious step For this purpose data such as rock brittleness index distance between plane of weakness angle between plane of weakness and TBM driven direction excavation specific energy thrust force cutterhead power and cutterhead torque were used to predict the measured penetration rate One of theproblems in this study was the high correlation between input data which caused a multicollinearityproblem This problem creates the marginal effect of input data on each other and reduces the accuracyand efficiency of mathematical model In order to solve this problem and reduce the amount of input data SPSS software and principal component s analysis PCA were used Applying the principal component sanalysis on the input data four main components were obtained and by performing a linear regressionbetween the penetration rate The dependent variable and these four components a relationship was foundfor penetration rate prediction In the next step data distribution functions were obtained and entered intothe @Risk software to investigate the effect of uncertainty on the penetration rate index The results showedthat along the tunnel route increasing parameters like brittleness index angle between plane of weaknessand TBM driven direction cutterhead power and cutterhead torque led to increase in penetration rate andwith increasing parameters like distance between plane of weakness excavation specific energy and thrustforce has a negative impact on penetration rates Furthermore the sensitivity analysis of the penetrationrate and impact of input parameters on it were also analyzed it was found that the brittleness index with acorrelation coefficient of 1 528 and the thrust force with 1 1117 value have the most effective and theleast effective role on controlling the penetration rate Keywords Queens Tunnel Mechanized Excavation TBM Performance Penetration Rate StochasticModeling

ابراهيم قاسمي

[سافت ياگيز]

استاد داور (داخلي): راحب باقر پور; داور (خارجي): حسن طباطبايي