Predictive Modeling and Optimization of TBM Operations: Advanced Techniques Applied to the Jakarta MRT Project

The effectiveness of Earth Pressure Balance (EPB) and Tunnel Boring Machines (TBMs) in urban underground construction relies on understanding and optimizing their performance under variable geotechnical conditions. This study investigates the key parameters impacting TBM efficiency during the construction of the Jakarta Mass Rapid Transit (MRT) Underground Section CP106.

Data from TBM operation were analyzed using statistical and machine learning techniques, including Mutual Information (MI), Partial Dependence Plots (PDP) Analysis of Variance (ANOVA), to identify influential parameters such as tensile strength, uniaxial strength, spacing penetration.

Predictive models, including gradient boosting regressor, random forest regressor linear regression, were evaluated based on error metrics and R-squared values, with gradient boosting regressor showing the highest predictive accuracy. Clustering analyses using K-Means and Principal Component Analysis (PCA) further classified operational states, identifying conditions that optimize energy efficiency and reduce mechanical wear. The findings suggest that TBM configurations with lower specific energy, normal force rolling force contribute to more efficient, less forceintensive tunneling. These insights provide a basis for refining TBM operations and predictive modeling in urban tunneling projects.