Integrating multi-source data for life-cycle risk assessment of bridge networks: a system digital twin framework
Abstract Bridges are critical infrastructure assets that face a variety of stressors throughout their service life, requiring a life-cycle approach to assess their risk profile. Recent advancements in sensing and monitoring technologies provide a powerful data foundation to improve the accuracy of l...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-02-01
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| Series: | Journal of Infrastructure Preservation and Resilience |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s43065-025-00121-7 |
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| author | Ziheng Geng Chao Zhang Yishuo Jiang Dora Pugliese Minghui Cheng |
| author_facet | Ziheng Geng Chao Zhang Yishuo Jiang Dora Pugliese Minghui Cheng |
| author_sort | Ziheng Geng |
| collection | DOAJ |
| description | Abstract Bridges are critical infrastructure assets that face a variety of stressors throughout their service life, requiring a life-cycle approach to assess their risk profile. Recent advancements in sensing and monitoring technologies provide a powerful data foundation to improve the accuracy of life-cycle risk assessment (LCRA). However, existing works that incorporate data for probabilistic risk assessment typically focus on individual bridges and rely on single-source data, limiting their scope and applicability. To this end, a system digital twin (SDT) framework based on Bayesian network (BN) is proposed to integrate multi-source data for LCRA of bridge networks. Specifically, the SDT can capture correlations and interdependencies across various scales, including within individual components (e.g., multiple failure modes), between components within a system (e.g., bridges along a route), and across interconnected systems (e.g., bridge and hydraulic systems). It integrates data from various sources including bridge inspections, traffic monitoring facilities, and water watch stations. A coastal bridge network in Miami-Dade County, FL, is used as an illustrative example to demonstrate how the SDT integrates multi-source data for risk assessment. Additionally, several future scenarios are hypothesized to showcase the applicability and flexibility of the proposed framework in supporting risk management for infrastructure systems. |
| format | Article |
| id | doaj-art-820d24f7b28345a8a1d2b518074a16cf |
| institution | OA Journals |
| issn | 2662-2521 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Infrastructure Preservation and Resilience |
| spelling | doaj-art-820d24f7b28345a8a1d2b518074a16cf2025-08-20T02:01:35ZengSpringerOpenJournal of Infrastructure Preservation and Resilience2662-25212025-02-016112010.1186/s43065-025-00121-7Integrating multi-source data for life-cycle risk assessment of bridge networks: a system digital twin frameworkZiheng Geng0Chao Zhang1Yishuo Jiang2Dora Pugliese3Minghui Cheng4Department of Civil & Architectural Engineering, University of MiamiCollege of Civil Engineering, Hunan UniversitySystems Engineering, Cornell UniversityDepartment of Civil & Architectural Engineering, University of MiamiDepartment of Civil & Architectural Engineering, University of MiamiAbstract Bridges are critical infrastructure assets that face a variety of stressors throughout their service life, requiring a life-cycle approach to assess their risk profile. Recent advancements in sensing and monitoring technologies provide a powerful data foundation to improve the accuracy of life-cycle risk assessment (LCRA). However, existing works that incorporate data for probabilistic risk assessment typically focus on individual bridges and rely on single-source data, limiting their scope and applicability. To this end, a system digital twin (SDT) framework based on Bayesian network (BN) is proposed to integrate multi-source data for LCRA of bridge networks. Specifically, the SDT can capture correlations and interdependencies across various scales, including within individual components (e.g., multiple failure modes), between components within a system (e.g., bridges along a route), and across interconnected systems (e.g., bridge and hydraulic systems). It integrates data from various sources including bridge inspections, traffic monitoring facilities, and water watch stations. A coastal bridge network in Miami-Dade County, FL, is used as an illustrative example to demonstrate how the SDT integrates multi-source data for risk assessment. Additionally, several future scenarios are hypothesized to showcase the applicability and flexibility of the proposed framework in supporting risk management for infrastructure systems.https://doi.org/10.1186/s43065-025-00121-7System digital twinLife-cycle risk assessmentInfrastructure managementSystem reliabilityBayesian network |
| spellingShingle | Ziheng Geng Chao Zhang Yishuo Jiang Dora Pugliese Minghui Cheng Integrating multi-source data for life-cycle risk assessment of bridge networks: a system digital twin framework Journal of Infrastructure Preservation and Resilience System digital twin Life-cycle risk assessment Infrastructure management System reliability Bayesian network |
| title | Integrating multi-source data for life-cycle risk assessment of bridge networks: a system digital twin framework |
| title_full | Integrating multi-source data for life-cycle risk assessment of bridge networks: a system digital twin framework |
| title_fullStr | Integrating multi-source data for life-cycle risk assessment of bridge networks: a system digital twin framework |
| title_full_unstemmed | Integrating multi-source data for life-cycle risk assessment of bridge networks: a system digital twin framework |
| title_short | Integrating multi-source data for life-cycle risk assessment of bridge networks: a system digital twin framework |
| title_sort | integrating multi source data for life cycle risk assessment of bridge networks a system digital twin framework |
| topic | System digital twin Life-cycle risk assessment Infrastructure management System reliability Bayesian network |
| url | https://doi.org/10.1186/s43065-025-00121-7 |
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