Time series prediction of tunnel boring machine (TBM) performance during excavation using causal explainable artificial intelligence (CX-AI)
Since early warning is significant to ensure high-quality tunneling boring machine (TBM) construction, a real-time prediction method based on TBM data is proposed. To solve the “black box” problem of prediction by artificial intelligence (AI) methods, the causal explainable gated recurrent unit (CX-...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/172880 |
| _version_ | 1811677119365775360 |
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| author | Wang, Kunyu Zhang, Limao Fu, Xianlei |
| author2 | School of Civil and Environmental Engineering |
| author_facet | School of Civil and Environmental Engineering Wang, Kunyu Zhang, Limao Fu, Xianlei |
| author_sort | Wang, Kunyu |
| collection | NTU |
| description | Since early warning is significant to ensure high-quality tunneling boring machine (TBM) construction, a real-time prediction method based on TBM data is proposed. To solve the “black box” problem of prediction by artificial intelligence (AI) methods, the causal explainable gated recurrent unit (CX-GRU) is developed to achieve real-time prediction for TBM parameters. The approach is implemented in a tunnel construction project in Singapore and the results indicate that CX-GRU performs well with the R square score are 0.9140 and 0.9184 in real-time prediction for thrust force and soil pressure. The causal discovery component can increase the computational efficiency of model training by 8.8% on average. According to the SHAP analysis of prediction results, the thrust force is more sensitive to the input TBM features, while the soil pressure is more sensitive to historical data. The CX-GRU is more reliable and efficient when applied to TBM projects than traditional methods. |
| first_indexed | 2024-10-01T02:32:18Z |
| format | Journal Article |
| id | ntu-10356/172880 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:32:18Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1728802023-12-27T07:37:53Z Time series prediction of tunnel boring machine (TBM) performance during excavation using causal explainable artificial intelligence (CX-AI) Wang, Kunyu Zhang, Limao Fu, Xianlei School of Civil and Environmental Engineering Engineering::Civil engineering TBM Parameters Real-Time Prediction Since early warning is significant to ensure high-quality tunneling boring machine (TBM) construction, a real-time prediction method based on TBM data is proposed. To solve the “black box” problem of prediction by artificial intelligence (AI) methods, the causal explainable gated recurrent unit (CX-GRU) is developed to achieve real-time prediction for TBM parameters. The approach is implemented in a tunnel construction project in Singapore and the results indicate that CX-GRU performs well with the R square score are 0.9140 and 0.9184 in real-time prediction for thrust force and soil pressure. The causal discovery component can increase the computational efficiency of model training by 8.8% on average. According to the SHAP analysis of prediction results, the thrust force is more sensitive to the input TBM features, while the soil pressure is more sensitive to historical data. The CX-GRU is more reliable and efficient when applied to TBM projects than traditional methods. This work is supported in part by the National Key R&D Program of China (No. 2022YFC3802200), the National Natural Science Foundation of China (No. 72271101), the Outstanding Youth Fund of Hubei Province (No. 2022CFA062), and the Start-Up Grant at Huazhong University of Science and Technology (No. 3004242122). 2023-12-27T07:37:53Z 2023-12-27T07:37:53Z 2023 Journal Article Wang, K., Zhang, L. & Fu, X. (2023). Time series prediction of tunnel boring machine (TBM) performance during excavation using causal explainable artificial intelligence (CX-AI). Automation in Construction, 147, 104730-. https://dx.doi.org/10.1016/j.autcon.2022.104730 0926-5805 https://hdl.handle.net/10356/172880 10.1016/j.autcon.2022.104730 2-s2.0-85146048562 147 104730 en Automation in Construction © 2022 Elsevier B.V. All rights reserved. |
| spellingShingle | Engineering::Civil engineering TBM Parameters Real-Time Prediction Wang, Kunyu Zhang, Limao Fu, Xianlei Time series prediction of tunnel boring machine (TBM) performance during excavation using causal explainable artificial intelligence (CX-AI) |
| title | Time series prediction of tunnel boring machine (TBM) performance during excavation using causal explainable artificial intelligence (CX-AI) |
| title_full | Time series prediction of tunnel boring machine (TBM) performance during excavation using causal explainable artificial intelligence (CX-AI) |
| title_fullStr | Time series prediction of tunnel boring machine (TBM) performance during excavation using causal explainable artificial intelligence (CX-AI) |
| title_full_unstemmed | Time series prediction of tunnel boring machine (TBM) performance during excavation using causal explainable artificial intelligence (CX-AI) |
| title_short | Time series prediction of tunnel boring machine (TBM) performance during excavation using causal explainable artificial intelligence (CX-AI) |
| title_sort | time series prediction of tunnel boring machine tbm performance during excavation using causal explainable artificial intelligence cx ai |
| topic | Engineering::Civil engineering TBM Parameters Real-Time Prediction |
| url | https://hdl.handle.net/10356/172880 |
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