Data‐augmented machine learning for risk management of tunnel boring machine jamming considering coupled geological factors

Effective management of tunnel boring machine (TBM) jamming is crucial for ensuring safety and mitigating construction downtime. However, previous studies have primarily focused on predictive modeling based on numerical datasets, with limited consideration of field‐based geological conditions and inadequate investigation of the fundamental mechanisms underlying jamming phenomena. This study utilized two ensemble learning algorithms, Random Forest and Extreme Gradient Boosting, to predict TBM jamming based on a field dataset from 39 tunneling projects. A data augmentation technique was employed to construct an expanded dataset. The predictive model trained on the augmented dataset demonstrated improved detection of TBM jamming compared to the model developed without data augmentation. The jamming mechanism was successfully characterized, revealing the individual effects of geological factors and their complex interactions. A distinct difference in predictive uncertainty between correct and incorrect predictions supports the model's reliability. Finally, a practical risk management system was proposed by incorporating the predictive model with probability thresholds and validated through field application.