Review of Condition Rating and Deterioration Modeling Approaches for Concrete Bridges
Concrete bridges are the most prevalent bridge type worldwide, forming critical components of transportation infrastructure. These bridges are subjected to continuous deterioration due to environmental exposure and operational stresses, necessitating ongoing condition monitoring. Despite extensive r...
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MDPI AG
2025-01-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/2/219 |
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| author | Nour Faris Tarek Zayed Ali Fares |
| author_facet | Nour Faris Tarek Zayed Ali Fares |
| author_sort | Nour Faris |
| collection | DOAJ |
| description | Concrete bridges are the most prevalent bridge type worldwide, forming critical components of transportation infrastructure. These bridges are subjected to continuous deterioration due to environmental exposure and operational stresses, necessitating ongoing condition monitoring. Despite extensive research on condition rating and deterioration modeling of concrete bridges, a comprehensive and comparative understanding of these processes remains underexplored. This paper addresses this gap by conducting a critical scientometric and systematic review of condition rating and deterioration modeling approaches for concrete bridges to highlight their strengths and limitations. Accordingly, most of the condition rating methods were found to have a heavy reliance on qualitative visual inspections (VI) and inherent subjective assumptions. Techniques such as fuzzy logic and non-destructive evaluation (NDE) methods were identified as promising tools to mitigate uncertainties and enhance accuracy. Moreover, the performance of most deterioration models was dependent on the quality of the historical condition data. The advancement of hybrid deterioration models, such as integrating artificial intelligence (AI) with stochastic and physics-based approaches, has proven to be a powerful strategy, combining the strengths of each method to deliver enhanced condition predictions. Finally, this study offers key insights and future research directions to assist researchers and policymakers in advancing sustainable concrete bridge management practices. |
| format | Article |
| id | doaj-art-570bf83c2ff043ad96a7dc7eb05c7731 |
| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-570bf83c2ff043ad96a7dc7eb05c77312025-01-24T13:26:14ZengMDPI AGBuildings2075-53092025-01-0115221910.3390/buildings15020219Review of Condition Rating and Deterioration Modeling Approaches for Concrete BridgesNour Faris0Tarek Zayed1Ali Fares2Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong, ChinaDepartment of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong, ChinaDepartment of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong, ChinaConcrete bridges are the most prevalent bridge type worldwide, forming critical components of transportation infrastructure. These bridges are subjected to continuous deterioration due to environmental exposure and operational stresses, necessitating ongoing condition monitoring. Despite extensive research on condition rating and deterioration modeling of concrete bridges, a comprehensive and comparative understanding of these processes remains underexplored. This paper addresses this gap by conducting a critical scientometric and systematic review of condition rating and deterioration modeling approaches for concrete bridges to highlight their strengths and limitations. Accordingly, most of the condition rating methods were found to have a heavy reliance on qualitative visual inspections (VI) and inherent subjective assumptions. Techniques such as fuzzy logic and non-destructive evaluation (NDE) methods were identified as promising tools to mitigate uncertainties and enhance accuracy. Moreover, the performance of most deterioration models was dependent on the quality of the historical condition data. The advancement of hybrid deterioration models, such as integrating artificial intelligence (AI) with stochastic and physics-based approaches, has proven to be a powerful strategy, combining the strengths of each method to deliver enhanced condition predictions. Finally, this study offers key insights and future research directions to assist researchers and policymakers in advancing sustainable concrete bridge management practices.https://www.mdpi.com/2075-5309/15/2/219concrete bridgesvisual inspectionscondition ratingnon-destructive evaluationdeterioration modelscondition prediction |
| spellingShingle | Nour Faris Tarek Zayed Ali Fares Review of Condition Rating and Deterioration Modeling Approaches for Concrete Bridges Buildings concrete bridges visual inspections condition rating non-destructive evaluation deterioration models condition prediction |
| title | Review of Condition Rating and Deterioration Modeling Approaches for Concrete Bridges |
| title_full | Review of Condition Rating and Deterioration Modeling Approaches for Concrete Bridges |
| title_fullStr | Review of Condition Rating and Deterioration Modeling Approaches for Concrete Bridges |
| title_full_unstemmed | Review of Condition Rating and Deterioration Modeling Approaches for Concrete Bridges |
| title_short | Review of Condition Rating and Deterioration Modeling Approaches for Concrete Bridges |
| title_sort | review of condition rating and deterioration modeling approaches for concrete bridges |
| topic | concrete bridges visual inspections condition rating non-destructive evaluation deterioration models condition prediction |
| url | https://www.mdpi.com/2075-5309/15/2/219 |
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