Applying Fuzzy Decision-Making and Markov Chain Modelling for Detecting Life Cycle of RC Bridges | ||||
| JES. Journal of Engineering Sciences | ||||
| Article 4, Volume 53, Issue 6, November and December 2025, Page 274-309 PDF (1.86 MB) | ||||
| Document Type: Technical paper | ||||
| DOI: 10.21608/jesaun.2025.390430.1534 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Ahmed Abdelallim1; Yasmin shalaby  2; Gamal Ebrahim3; Mohamed Badawy 4
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| 1Professor of Construction Management, Project Management and Sustainable Construction Program, PMSC Founder, Civil Engineering Department, Faculty of Engineering at Mataria, Helwan University, Cairo, Egypt | ||||
| 2PhD Candidate, Structural Engineering Department, Ain Shams University, Cairo, Egypt | ||||
| 3Professor, Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, Cairo, Egypt | ||||
| 4Associate Professor ,Structural Engineering Department, Ain Shams University, Cairo, Egypt | ||||
| Abstract | ||||
| Bridge inspection become essential for ensuring structural safety and longevity. Recently, Artificial Intelligence (AI) has become significant in improving bridge assessment by supporting different approaches that enhance maintenance planning and minimize associated costs. Objective of this study is to investigate the more accurate and applicable AI-driven technique for assessing reinforced concrete bridges. Therefore, the presented study proposed two different techniques to estimate the current Bridge Condition Rating (BCR) of reinforced concrete (R.C.) bridges: 1) fuzzy decision-making and 2) Markov chain modelling. This paper focused on a corrosion attack as the main defect utilized to assess the bridge condition. The dual methods depend on visual inspection, applying field and laboratory tests, and reviewing the historical data of the inspected bridge to estimate its condition rating. The fuzzy decision model is used to find a correlation between corrosion degree and concrete surface condition to estimate the Bridge Condition Rating (BCR). The Markov chain model is applied to predict the current and the future Bridge Condition Rating (BCR) and when the bridge will reach the critical condition. The service life for each bridge element is evaluated due to the total time required for corrosion based on carbonation and chloride attack. The proposed models are validated through a real case study of R.C. bridge, and the results demonstrate that the fuzzy model is less accurate compared to the Markov chain. The introduced models provide valuable insights to provide proper Maintenance, Repair, and Replacement (MRR) decisions for the bridges. | ||||
| Keywords | ||||
| Reinforced concrete bridges; assessment; AI; Fuzzy logic; Markov Chain | ||||
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