چکیده مقاله
With widespread increasing applications of mechanized tunneling in almost all ground conditions, prediction of tunnel boring machine TBM performance is required for time planning, cost control and choice of excavation method in order to make tunneling economical Penetration rate is a principal measure of full face TBM performance and is used to evaluate the feasibility of the machine and predict advance rate of excavation In this study, a database of actual machine performance from two hard rock tunneling projects in Iran including Zagros lot 1B and 2 with 14 3 km available data has been compiled To clarify the effective parameters on penetration rate, first principal component analysis PCA was performed Furthermore, well known Artificial Intelligence AI based methods, including artificial neural networks ANN , adaptive neuro fuzzy inference system ANFIS and support vector regression SVR have been employed As statistical indices, root mean square error RMSE , correlation coefficient R2 , variance account for VAF , and mean absolute percentage error MAPE were used to evaluate the efficiency of the developed AI models for TBM performance According to the obtained results, it was observed that AI based methods can effectively be implemented for prediction of TBM performance Moreover, it was concluded that performance of the SVR model is better than the ANFIS and ANN models A high conformity was observed between predicted and measured TBM performance for the SVR model
کلیدواژهها
نویسندگان
شیوه ارجاع
Salimi, Alireza and Moormann, Christian and Singh, T.N and Jain, Prasnna,1396,TBM Performance Prediction in Rock Tunneling Using Various Artificial Intelligence Algorithms,12th Iranian & 3rd Regional Tunnelling Conference,Tehran
ارائهشده در
مجموعه مقالات سومین کنفرانس منطقه ای و دوازدهمین کنفرانس تونل ایران6 آذر 1396 · تهران