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Machine learning–informed soil conditioning for mechanized shield tunneling
AbstractEffective soil conditioning is critical for mechanized shield tunneling, yet the selection of conditioning parameters remains experience‐oriented. This study presents a machine learning–informed soil conditioning strategy, aiming at enabling automatic decision‐making for soil conditioning during tunneling. The proposed procedure includes feature engineering to process raw data, the selection of an optimal model, and a committee‐based uncertainty quantification strategy to evaluate the reliability of models. High‐dimensional data visualization techniques are leveraged to examine the influence of data distribution on the model's performance. Results demonstrate that feature engineering and the selection of models lay a foundation for machine learning–enabled automatic soil conditioning, while historical soil conditioning parameters should be incorporated in models to account for time‐dependent features during tunneling. The committee‐based uncertainty quantification strategy can effectively identify the reliability of both interpolated and extrapolated predictions. The proposed data‐driven soil conditioning framework can promote the application of machine learning toward automating earth pressure balance (EPB) shield tunneling.
Machine learning–informed soil conditioning for mechanized shield tunneling
AbstractEffective soil conditioning is critical for mechanized shield tunneling, yet the selection of conditioning parameters remains experience‐oriented. This study presents a machine learning–informed soil conditioning strategy, aiming at enabling automatic decision‐making for soil conditioning during tunneling. The proposed procedure includes feature engineering to process raw data, the selection of an optimal model, and a committee‐based uncertainty quantification strategy to evaluate the reliability of models. High‐dimensional data visualization techniques are leveraged to examine the influence of data distribution on the model's performance. Results demonstrate that feature engineering and the selection of models lay a foundation for machine learning–enabled automatic soil conditioning, while historical soil conditioning parameters should be incorporated in models to account for time‐dependent features during tunneling. The committee‐based uncertainty quantification strategy can effectively identify the reliability of both interpolated and extrapolated predictions. The proposed data‐driven soil conditioning framework can promote the application of machine learning toward automating earth pressure balance (EPB) shield tunneling.
Machine learning–informed soil conditioning for mechanized shield tunneling
Computer aided Civil Eng
Wang, Shuying (author) / Yuan, Xiao (author) / Qu, Tongming (author)
Computer-Aided Civil and Infrastructure Engineering ; 39 ; 2662-2682
2024-09-01
Article (Journal)
Electronic Resource
English
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