Intelligent support system for ground settlement management during TBM tunneling by combining machine learning with statistical analysis

Abstract Ground settlement management is crucial in tunnel boring machine (TBM) tunneling. Previous studies on predicting ground settlement have required substantial assumptions or information, making it challenging to explicitly determine their predictive criteria. This study developed an intelligent TBM operation support system for ground settlement management, by combining machine learning and statistical analysis. Initially, measured settlements were categorized into three classes: heaving, normal, and large settlement. Based on three key features selected through correlation analysis, the predictive criterion for the heaving class was determined using an initial model based on a decision tree algorithm. Subsequently, through statistical analysis, the predictive criteria for the normal and large settlement classes were established. The developed support system, using only three key features, achieved an accuracy of 0.847, F1 score of 0.784, precision of 0.798, and recall of 0.770, outperforming two ensemble machine learning models that used nine features. Moreover, the system can provide explicit predictive criteria, enhancing its practical applicability. Error analysis revealed that among the four instances misclassified by the support system, three pertained to minor settlements within ± 3 mm.