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<p>Accurate condition assessment of ageing railway bridges is critical for ensuring structural safety and optimising maintenance decisions. Traditional visual inspection methods, such as the IRICEN (Bridge inspection and maintenance, 2014) 5-point rating system, rely heavily on inspector judgement and often lead to overly conservative evaluations due to their deterministic and worst-case-driven nature. To address these limitations, this study proposes a fuzzy logic-based framework for bridge condition rating, incorporating a 10-point scale and component-wise importance weighting. The approach employs triangular fuzzy membership functions to model condition uncertainty, and aggregates ratings using the Fuzzy Weighted Geometric Mean technique. A Python-based expert system “Fuzzy Bridge Rating System (FBRS)” is developed to implement the methodology. The proposed system was applied to multiple railway bridges, with results from three representative cases presented in this paper. Comparative analysis demonstrates that the FBRS offers more nuanced, realistic, and structurally consistent ratings than the traditional method, thereby improving maintenance prioritisation and resource allocation. The framework is designed for integration into bridge management systems and is especially applicable in contexts where visual inspection remains the primary evaluation tool.</p>

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Fuzzy Based Bridge Rating System (FBRS) for Condition Assessment of Existing Railway Bridges

  • 期刊论文

  • 发表日期：

    2025年8月5日

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  • Sudhir Babu Patel,
  • Abhigyan Srivastava,
  • Krishna Kant Pathak,
  • Ishan Jha

摘要

Accurate condition assessment of ageing railway bridges is critical for ensuring structural safety and optimising maintenance decisions. Traditional visual inspection methods, such as the IRICEN (Bridge inspection and maintenance, 2014) 5-point rating system, rely heavily on inspector judgement and often lead to overly conservative evaluations due to their deterministic and worst-case-driven nature. To address these limitations, this study proposes a fuzzy logic-based framework for bridge condition rating, incorporating a 10-point scale and component-wise importance weighting. The approach employs triangular fuzzy membership functions to model condition uncertainty, and aggregates ratings using the Fuzzy Weighted Geometric Mean technique. A Python-based expert system “Fuzzy Bridge Rating System (FBRS)” is developed to implement the methodology. The proposed system was applied to multiple railway bridges, with results from three representative cases presented in this paper. Comparative analysis demonstrates that the FBRS offers more nuanced, realistic, and structurally consistent ratings than the traditional method, thereby improving maintenance prioritisation and resource allocation. The framework is designed for integration into bridge management systems and is especially applicable in contexts where visual inspection remains the primary evaluation tool.