A case study of TBM performance prediction using field tunnelling tests in limestone strata

By Liao Jianbin, Yang Chen, Chen Shuai, Zhang Na, Xing-xin Peng. Tunnelling and Underground Space Technology, 2019

85 citations recorded by the source corpus.

Abstract TBM performance prediction models play important guiding roles in equipment selection, project planning, cost forecast, as well as the optimization of TBM operational parameters. In this study, field tunnelling tests were conducted in the limestone strata of Songhua River water supply project through artificially changing two operational parameters, the rotation speed (RPM) and penetration (p) of TBM cutterhead. Compared the field test and normal tunnelling data, it revealed that penetration (p) and the normal force of single cutter ( F n ) had significant linear correlations with consistent rules in the two different types of penetration conditions, whereas the change of cutterhead rotation speed had little effect on the linear relationship. Based on the rules above, TBM performance prediction model for limestone strata was established after analyzing 46 sets of machine and corresponding rock mass parameters obtained during normal tunneling by using stepwise regression method. A linear relationship between the penetration and the normal force of single cutter was established initially by using the initial section data of normal tunneling cycles. Then the final model was obtained based on the analysis of the correlation between rock mass parameters and the undermined coefficients in the previous linear relationship model. The results show that the model was in good agreement with the experimental measurements. Finally, a real time rock mass state perception method based on this model was discussed. This method can be used to optimize the operational parameters and to create a foundation for intelligent control of TBMs.

Subfields: Civil and Structural Engineering, Mechanics of Materials, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/aa504b21f3a50ff077cc1866039864165d799992