Digital Twin-Based and Knowledge Graph-Enhanced Emergency Response in Urban Infrastructure Construction
Urban infrastructure construction poses significant risks to surrounding the infrastructure due to ground settlement, structural disturbances, and underground utility disruptions. Traditional risk assessment methods often rely on static models and experience-based decision-making, limiting their abi...
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MDPI AG
2025-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/11/6009 |
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| author | Chao Chen Yanyun Lu Bo Wu Linhai Lu |
| author_facet | Chao Chen Yanyun Lu Bo Wu Linhai Lu |
| author_sort | Chao Chen |
| collection | DOAJ |
| description | Urban infrastructure construction poses significant risks to surrounding the infrastructure due to ground settlement, structural disturbances, and underground utility disruptions. Traditional risk assessment methods often rely on static models and experience-based decision-making, limiting their ability to adapt to dynamic construction conditions. This study proposes an integrated framework combining digital twin and knowledge graph technologies to enhance real-time risk assessment and emergency response in tunnel construction. The digital twin continuously integrates real-time monitoring data, including settlement measurements, TBM operational parameters, and structural responses, creating a virtual representation of the tunneling environment. Meanwhile, the knowledge graph structures domain knowledge and applies rule-based reasoning to infer potential hazards, detect abnormal conditions, and suggest mitigation strategies. The proposed approach has been successfully applied to a practical tunnel project in China, where it played a crucial role in emergency response and risk mitigation. By integrating real-time monitoring data with the knowledge-driven reasoning system, the developed framework enabled the early identification of anomalies, rapid risk assessment, and the formulation of effective mitigation strategies, preventing further structural impact. This bidirectional interaction between the digital twin and the knowledge graph ensured that the real-world data informed the automated reasoning, while the inference results were visualized within the digital twin for intuitive decision support. The proposed framework not only enhances current risk management practices but also serves as a foundation for future innovations in smart infrastructure and automated emergency response systems. |
| format | Article |
| id | doaj-art-34553b5fb0bb46b7975bdede9c043858 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-34553b5fb0bb46b7975bdede9c0438582025-08-20T02:23:44ZengMDPI AGApplied Sciences2076-34172025-05-011511600910.3390/app15116009Digital Twin-Based and Knowledge Graph-Enhanced Emergency Response in Urban Infrastructure ConstructionChao Chen0Yanyun Lu1Bo Wu2Linhai Lu3Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, ChinaDepartment of Geotechnical Engineering, College of Civil Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, ChinaDepartment of Geotechnical Engineering, College of Civil Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, ChinaJinan Transportation Development Investment Co., Ltd., Jinan 250000, ChinaUrban infrastructure construction poses significant risks to surrounding the infrastructure due to ground settlement, structural disturbances, and underground utility disruptions. Traditional risk assessment methods often rely on static models and experience-based decision-making, limiting their ability to adapt to dynamic construction conditions. This study proposes an integrated framework combining digital twin and knowledge graph technologies to enhance real-time risk assessment and emergency response in tunnel construction. The digital twin continuously integrates real-time monitoring data, including settlement measurements, TBM operational parameters, and structural responses, creating a virtual representation of the tunneling environment. Meanwhile, the knowledge graph structures domain knowledge and applies rule-based reasoning to infer potential hazards, detect abnormal conditions, and suggest mitigation strategies. The proposed approach has been successfully applied to a practical tunnel project in China, where it played a crucial role in emergency response and risk mitigation. By integrating real-time monitoring data with the knowledge-driven reasoning system, the developed framework enabled the early identification of anomalies, rapid risk assessment, and the formulation of effective mitigation strategies, preventing further structural impact. This bidirectional interaction between the digital twin and the knowledge graph ensured that the real-world data informed the automated reasoning, while the inference results were visualized within the digital twin for intuitive decision support. The proposed framework not only enhances current risk management practices but also serves as a foundation for future innovations in smart infrastructure and automated emergency response systems.https://www.mdpi.com/2076-3417/15/11/6009urban infrastructure constructionemergency responsedigital twinknowledge graph |
| spellingShingle | Chao Chen Yanyun Lu Bo Wu Linhai Lu Digital Twin-Based and Knowledge Graph-Enhanced Emergency Response in Urban Infrastructure Construction Applied Sciences urban infrastructure construction emergency response digital twin knowledge graph |
| title | Digital Twin-Based and Knowledge Graph-Enhanced Emergency Response in Urban Infrastructure Construction |
| title_full | Digital Twin-Based and Knowledge Graph-Enhanced Emergency Response in Urban Infrastructure Construction |
| title_fullStr | Digital Twin-Based and Knowledge Graph-Enhanced Emergency Response in Urban Infrastructure Construction |
| title_full_unstemmed | Digital Twin-Based and Knowledge Graph-Enhanced Emergency Response in Urban Infrastructure Construction |
| title_short | Digital Twin-Based and Knowledge Graph-Enhanced Emergency Response in Urban Infrastructure Construction |
| title_sort | digital twin based and knowledge graph enhanced emergency response in urban infrastructure construction |
| topic | urban infrastructure construction emergency response digital twin knowledge graph |
| url | https://www.mdpi.com/2076-3417/15/11/6009 |
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