Development of a rating model for assessing the condition of steel railway bridges

Abstract

Steel railway bridges play a crucial role in global transportation networks, yet their operational lifespan is often compromised by factors such as corrosion, aging, exposure conditions, and environmental challenges. Structural health monitoring (SHM) systems are widely employed to extend the longevity of these bridges. However, existing SHM models typically have limitations, as they tend to focus on a limited set of parameters, and their reliance on qualitative definitions introduces subjectivity into the evaluation process. To address these shortcomings, this research aims to develop an innovative priority weight-based condition rating model. Casual factors (CF), laboratory and non-destructive tests (NDT), visual inspection (VI), environmental conditions (EC), and type of element (TOE), were identified as main parameters to the condition assessment. Then, the research gathered opinions from 100 experts to establish the importance of these parameters. By averaging expert opinions and ensuring a high level of consistency (i.e. below 10%), the study aimed to minimize subjectivity. Numerical definitions were introduced to the model to enhance objectivity. The fuzzy analytical hierarchy process (FAHP) was employed to calculate appropriate weightings for the identified parameters, resulting in a comprehensive equation that encapsulates the main factors influencing the condition of steel railway bridges. To validate the developed rating model, a case study was conducted, analyzing seventeen railway bridges located in various regions of Sri Lanka. Notably, the model takes into account physical, geographical, and environmental conditions, making it applicable to diverse locations worldwide. This innovative approach contributes to the enhancement of steel railway bridge maintenance strategies, promoting their sustained and reliable operation on a global scale.