Comparison and correlation between the laboratory, semi-theoretical and empirical methods in predicting the field excavation performance of tunnel boring machine (TBM)

Many methods have been proposed to predict the field excavation performance of tunnel boring machine (TBM). They can usually predict the general trends of the field rock cutting force and efficiency when cutter penetration depth increases. However, in many cases, the predicted values are not so close (sometimes even very different) to the field values. This study is to check, compare and then correlate the prediction results of three widely used methods in predicting the field TBM excavation performance. These three methods are the laboratory LCM (linear cutting machine) method, the semi-theoretical CSM (Colorado School of Mines) method and the empirical NTNU (Norwegian University of Science and Technology) method. First, the basic procedures of these three methods are reviewed, and their prediction results are obtained concerning the TBM excavation performance in Chongqing Yangtze River Tunnel Project. Then, the quantitative comparison and correlation between the field measured result with the laboratory, semi-theoretical and empirical prediction results are conducted. After that, combined with the previous studies, some empirical formulas are proposed to predict the field TBM excavation performance at the optimum rock cutting condition more reliably and accurately. These empirical formulas can take the laboratory, semi-theoretical and empirical prediction results into comprehensive consideration. Finally, the south lot of the Qinling Tunnel in Yinhanjiwei Water Diversion Project is set as the case study, and the machine parameters and TBM excavation performance in this project are predicted and compared to the field values to verify the applicability and accuracy of the new proposed empirical formulas. Results show that the use of these new proposed empirical formulas can offer more acceptable prediction results for TBM cutterhead design and operation optimization.