Soil Classification by Machine Learning Using a Tunnel Boring Machine’s Operating Parameters

Abstract

This study predicted soil classification using data gathered during the operation of an earth-pressure-balance-type tunnel boring machine (TBM). The prediction methodology used machine learning to find relationships between the TBM’s operating parameters which are monitored continuously during excavation, and the engineering characteristics of the ground which are only available from prior geotechnical investigation. Classification criteria were set using the No. 200 sieve pass rate and N-value and employed classification algorithms that used data for six operating parameters (penetration rate, thrust force, cutterhead torque, screw torque, screw revolution speed, and earth pressure). The results of the ensemble model (i.e., AdaBoost, gradient boosting, XG boosting, and Light GBM), decision tree, and SVM model were examined. As a result, the decision tree and AdaBoost models showed accuracy values of 0.759 to 0.879 in the first and second classification steps, but with poor precision and recall values of around 0.6. In contrast, the gradient boosting, XG boosting, Light GBM, and support vector models all showed excellent performance, with accuracy values over 0.90, and strong precision and recall values. Comparing the performance and the speed of learning using the same PC found Light GBM which showed both excellent learning performance and speed to be a suitable model for predicting soil classification using TBM operating data. The classification model developed here is expected to help guide excavation in sections of ground that lack prior geotechnical information.

Keywords

• TBM
• operating parameters
• soil classification
• machine learning