Excavation with tunnel boring machines (TBMs) has been extensively used in the construction of tunnelling projects. The geological conditions of the Himalayan region pose significant challenges to the performance of TBM tunnelling. This manuscript proposes a machine learning (ML)-based data-driven approach to predict the TBM net penetration rate (PRnet), integrating both geological and TBM operational parameters. To do so, a total of 8,614 stable-phase real-time TBM cycle data corresponding to mapped geological parameters were collected from a 12 km long tunnel in Nepal. The preprocessed dataset were randomly split into a training and testing set with the 80/20 rule. The TBM PRnet is evaluated using different non-ensemble, ensemble, and artificial neural network (ANN) regression models. The ANN and stacking ensemble models achieved the highest R2 value of 0.94 on unseen test data. Shapley Additive exPlanations (SHAP) method, an explainable artificial intelligence tool, was used to analyze the influence of input features. The output results have revealed that PRnet is significantly influenced by both geological and TBM operational parameters. These parameters were further used to examine TBM jamming in areas where geological challenges associated to faults or weak zones were encountered. The evaluation results revealed that fluctuations in torque and thrust provide valuable information for assessing the risk of operational hazards in challenging geological environments. The manuscript offers a more adaptable prediction framework. Thus, the authors emphasize that the effective application of the ML-based data-driven approach in TBM tunnelling holds substantial potential for accurately predicting the net penetration rate.Highlights<UnorderedList Mark="Bullet"> <ItemContent> Machine learning (ML)-based model developed to predict TBM net penetration rate (PRnet). </ItemContent> <ItemContent> TBM operational parameters and geological conditions are utilized as input parameters. </ItemContent> <ItemContent> Prediction model incorporates data anomalies associated with complex geological environment. </ItemContent> <ItemContent> Impact of input features on TBM net penetration rate evaluated using SHAP method. </ItemContent> <ItemContent> Risk of TBM jamming assessed based on machine parameters and geological conditions. </ItemContent> </UnorderedList>

TBM Penetration Rate Prediction in Himalayan Geology Using Machine Learning (ML) Techniques

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2025年10月28日

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Tek Bahadur Katuwal,
Krishna Kanta Panthi

摘要

Excavation with tunnel boring machines (TBMs) has been extensively used in the construction of tunnelling projects. The geological conditions of the Himalayan region pose significant challenges to the performance of TBM tunnelling. This manuscript proposes a machine learning (ML)-based data-driven approach to predict the TBM net penetration rate (PRnet), integrating both geological and TBM operational parameters. To do so, a total of 8,614 stable-phase real-time TBM cycle data corresponding to mapped geological parameters were collected from a 12 km long tunnel in Nepal. The preprocessed dataset were randomly split into a training and testing set with the 80/20 rule. The TBM PRnet is evaluated using different non-ensemble, ensemble, and artificial neural network (ANN) regression models. The ANN and stacking ensemble models achieved the highest R² value of 0.94 on unseen test data. Shapley Additive exPlanations (SHAP) method, an explainable artificial intelligence tool, was used to analyze the influence of input features. The output results have revealed that PRnet is significantly influenced by both geological and TBM operational parameters. These parameters were further used to examine TBM jamming in areas where geological challenges associated to faults or weak zones were encountered. The evaluation results revealed that fluctuations in torque and thrust provide valuable information for assessing the risk of operational hazards in challenging geological environments. The manuscript offers a more adaptable prediction framework. Thus, the authors emphasize that the effective application of the ML-based data-driven approach in TBM tunnelling holds substantial potential for accurately predicting the net penetration rate.

Highlights

Machine learning (ML)-based model developed to predict TBM net penetration rate (PRnet).

TBM operational parameters and geological conditions are utilized as input parameters.

Prediction model incorporates data anomalies associated with complex geological environment.

Impact of input features on TBM net penetration rate evaluated using SHAP method.

Risk of TBM jamming assessed based on machine parameters and geological conditions.