Prediction of TBM penetration rate from brittleness indexes using multiple regression analysis

Abstract

One of the most important aspects in the excavation of tunnels with a Tunnel Boring Machine (TBM) is the reliable prediction of its penetration rate. This affects the planning and other decision making on the organization of the construction site of the tunneling project, and, therefore, total costs. In this study, raw data obtained from the experimental works of different researchers were used to establish the new statistical models for prediction of rock TBM penetration rate from brittleness indexes, B 1 , B 2 , and B 3 . For this, correlation between the TBM penetration rate with brittleness indexes statistically was investigated using multiple regression analyses. In these analyses, the TBM penetration rate was considered to be the dependent variable, which is dependent on the independent variables of the brittleness indexes. The validity of the predictive models was validated by statistical tests. The results showed that statistical models are in good accuracy for prediction of TBM penetration rate, and thus making a rapid assessment of the TBM performance.

Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate

“…Several studies noted that predicting the penetration rate is a complex and difficult task because of the interaction between the TBM and rock mass. According to Jamshidi [51], TBM penetration rate directly influences the advance rate, which represents the total distance excavated by the machine divided by the total time. While there is a high cost associated with tunneling projects and using the TBM, utilizing prediction methods, especially ML techniques for predicting the penetration rate of TBM can significantly reduce the total time and cost of the tunneling projects.…”