Causality-Guided Explainable Deep-Learning Model for Managing Tunnel-Induced Ground Settlement Risks
The use of deep learning (DL) has been growing in tunnel-induced ground settlement risk modeling, eliminating the necessity for extensive prior risk management knowledge. Despite the success of deploying a DL model to predict risk, challenges prevail. (1) DL requires high-quality data, which is expe...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
2025
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| Online Access: | http://hdl.handle.net/20.500.11937/97516 |
| _version_ | 1848766288534437888 |
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| author | Liu, W. Liu, F. Love, Peter Fang, W. |
| author_facet | Liu, W. Liu, F. Love, Peter Fang, W. |
| author_sort | Liu, W. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The use of deep learning (DL) has been growing in tunnel-induced ground settlement risk modeling, eliminating the necessity for extensive prior risk management knowledge. Despite the success of deploying a DL model to predict risk, challenges prevail. (1) DL requires high-quality data, which is expensive and time-consuming to prepare, and (2) DL is a “black-box,” which is difficult to understand and interpret. In this instance, we address the following question in this paper: How can we accurately predict ground settlement with limited monitoring data using DL and concurrently provide effective explanations for the generated results? We propose a new DL approach combining explainable techniques to improve ground settlement risk modeling accuracy and explainability. Our approach comprises the following: (1) an interval type-2 fuzzy system to process limited data and improve its usability; (2) a novel causal-based feature selection to determine input parameters that have strong causal effects with ground settlement risk; and (3) a soft-type attention module to evaluate and allocate feature importance to inputs, guiding neural networks to concentrate on learning features in a more targeted manner. A case study is used to validate the feasibility and effectiveness of our approach, demonstrating its superiority in predicting ground settlement risk with a high degree of robustness. We suggest that our approach can help decision makers better understand the “how” and “what” of DL-produced outputs, improving decision making associated with managing safety in construction. |
| first_indexed | 2025-11-14T11:48:46Z |
| format | Journal Article |
| id | curtin-20.500.11937-97516 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:48:46Z |
| publishDate | 2025 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-975162025-04-16T04:49:50Z Causality-Guided Explainable Deep-Learning Model for Managing Tunnel-Induced Ground Settlement Risks Liu, W. Liu, F. Love, Peter Fang, W. The use of deep learning (DL) has been growing in tunnel-induced ground settlement risk modeling, eliminating the necessity for extensive prior risk management knowledge. Despite the success of deploying a DL model to predict risk, challenges prevail. (1) DL requires high-quality data, which is expensive and time-consuming to prepare, and (2) DL is a “black-box,” which is difficult to understand and interpret. In this instance, we address the following question in this paper: How can we accurately predict ground settlement with limited monitoring data using DL and concurrently provide effective explanations for the generated results? We propose a new DL approach combining explainable techniques to improve ground settlement risk modeling accuracy and explainability. Our approach comprises the following: (1) an interval type-2 fuzzy system to process limited data and improve its usability; (2) a novel causal-based feature selection to determine input parameters that have strong causal effects with ground settlement risk; and (3) a soft-type attention module to evaluate and allocate feature importance to inputs, guiding neural networks to concentrate on learning features in a more targeted manner. A case study is used to validate the feasibility and effectiveness of our approach, demonstrating its superiority in predicting ground settlement risk with a high degree of robustness. We suggest that our approach can help decision makers better understand the “how” and “what” of DL-produced outputs, improving decision making associated with managing safety in construction. 2025 Journal Article http://hdl.handle.net/20.500.11937/97516 10.1061/JCCEE5.CPENG-6209 unknown |
| spellingShingle | Liu, W. Liu, F. Love, Peter Fang, W. Causality-Guided Explainable Deep-Learning Model for Managing Tunnel-Induced Ground Settlement Risks |
| title | Causality-Guided Explainable Deep-Learning Model for Managing Tunnel-Induced Ground Settlement Risks |
| title_full | Causality-Guided Explainable Deep-Learning Model for Managing Tunnel-Induced Ground Settlement Risks |
| title_fullStr | Causality-Guided Explainable Deep-Learning Model for Managing Tunnel-Induced Ground Settlement Risks |
| title_full_unstemmed | Causality-Guided Explainable Deep-Learning Model for Managing Tunnel-Induced Ground Settlement Risks |
| title_short | Causality-Guided Explainable Deep-Learning Model for Managing Tunnel-Induced Ground Settlement Risks |
| title_sort | causality-guided explainable deep-learning model for managing tunnel-induced ground settlement risks |
| url | http://hdl.handle.net/20.500.11937/97516 |