The Application of FAHP in Railway Infrastructure Maintenance: A Systematic Review
DOI:
https://doi.org/10.32832/astonjadro.v15i3.21975Keywords:
fuzzy Analytical Hierarchy Process, railway infrastructure maintenance, MCDM, predictive maintenance.Abstract
Rail transport is a critical backbone for regional mobility and economic development, and relies fundamentally on well-maintained infrastructure. The maintenance decision-making process for this infrastructure is complex, requiring the simultaneous evaluation of multiple technical and non-technical criteria under significant uncertainty. The Fuzzy Analytical Hierarchy Process (FAHP) has emerged as a prominent Multi-Criteria Decision Making (MCDM) method to address these challenges. While the application of FAHP in the broader transportation sector has been reviewed in various studies, a systematic review focusing specifically on its development and application for railway infrastructure maintenance is absent from the literature. This study conducts a Systematic Literature Review (SLR) following the PRISMA protocol to analyze the development of FAHP and its hybrid applications in railway infrastructure maintenance over a decade. The analysis, based on a systematic search of Scopus, Google Scholar, and MDPI databases, identifies and synthesizes the existing literature to define evolving research trends, methodological approaches, and future directions. The results reveal that FAHP and its hybrid variants (such as FAHP-TOPSIS, FAHP-GIS, and FAHP-FCE) have been applied to assess structural conditions, determine maintenance priorities, and manage geotechnical risks. This review confirms that FAHP effectively structures expert judgement and handles subjective data. However, a significant research gap persists in integrating these models with real-time data streams. This leads to the conclusion that future research must prioritize the development of real-time predictive FAHP models. These advanced systems should be designed to incorporate multi-stakeholder perspectives and leverage digital technologies such as the Internet of Things (IoT) and machine learning to enable a paradigm shift towards proactive and data-driven decision-making in railway infrastructure maintenance.
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