T11: NATM versus TBMShengfeng HUANG, Misagh ESMAEILPOUR, Saadeldin MOSTAFA, Pooya DASTPAK, Rita SOUSAStevens Institute of Technology, United States of AmericaTBM penetration rate prediction using machine learning models and models’ generalizationMost existing models about penetration rate (PR) prediction have been developed and validated against data from one single project. This poses the question whether these models can perform well when faced with new data. We use two datasets of two tunnels built with the same construction method and in similar geological conditions. Different machine learning (ML) models are trained, validated, and tested with dataset from one tunnel and then generated to the other dataset. Additionally, the effect of several data processing techniques for splitting and scaling on the performance and generalization of the different models is tested. The results demonstrate that random forest (RF) and extreme gradient boosting (XGBoost) exhibit better performance than other models. Regarding generalization, CART and XGBooost model exhibit the best performance. The impact of splitting and scaling techniques on the generalization of the models becomes noticeable than on the performance of models. | Penetration rate, EPBM, Machine learning, Generalization, split and scale technique