# Boosting Model Interpretability for Transparent ML in TBM Tunneling
## 1 Introduction  
Research on predicting TBM performance has leveraged various machine learning (ML) approaches, including multiple linear regression [1], ANNs [2], fuzzy logic [3], and deep learning [4], all yielding promising results. Despite tunneling uncertainties, past knowledge has informed models for both specific cases and generalized scenarios [5]. Advanced AI techniques, such as neuro-fuzzy methods [6], particle swarm optimization [7], and hybrid algorithms [8], continue to enhance accuracy and address limitations.  
Recent advances include the XGBoost algorithm [9], renowned for its regression and classification capabilities. Fine-tuning XGBoost with Bayesian optimization has achieved impressive predictions of the TBM advance rate (AR) [10]. To meet the demands of real-time analysis during construction, BIM-based simulations and integrated \"BIM-to-FEM\" planning systems highlight the field's progress [11].  
ML has revolutionized problem-solving across domains by providing predictive insights and improving decision-making. Ensemble methods, which combine multiple models for greater accuracy, exemplify this transformation [12]. The SHAP library addresses this challenge by explaining each feature's contribution to predictions, enhancing trust and usability. Its versatility and solid theoretical foundation make it a valuable tool across fields. In tunneling, SHAP has helped evaluate factors influencing settlement outcomes in shield tunneling [13], control shield attitude [14], and predicting fire-induced spalling in concrete tunnel linings [15]. Additionally, SHAP has clarified how geological conditions impact TBM penetration rates [15, 16, 17]. While some movement reduces stresses and costs, excessive deformation risks destructive surface settlements.  
Predicting settlements is essential to minimizing deformations and preventing soil volume loss. These include immediate settlements, those from tunnel lining deformation, and long-term effects like primary consolidation and secondary creep [18]. Understanding these movements is critical for evaluating TBM performance and planning successful projects.  
TBM efficiency also depends on factors such as rock type, operational parameters, and site conditions. The accurate prediction of TBM utilization rates is key to developing effective tunneling plans, enabling proactive measures to enhance efficiency and progress. The TBM utilization percentage directly influences excavation rates and project costs ([PERSON], 2012). The relationship between the rate of penetration (ROP), AR, and utilization (U) is captured in Equation (1).  
\[AR=ROP\times U\times T \tag{1}\]  
Increased utilization significantly boosts excavation rates. Even with a high ROP, progress remains slow if TBM usage is inefficient. With typical utilization ranging from less than 10% to around 50%, optimizing machine usage is crucial to ensuring efficient excavation and preventing delays (Figure 1). By managing geotechnical factors and operational parameters, predictive models using ML techniques and SHAP methodology can greatly enhance tunneling project success.