Enhancing Railway Track Structural Condition Assessment Using Multi-Criteria Decision-Making Method
| dc.contributor.advisor | Fiseha Nega (PhD) | |
| dc.contributor.author | Kirubel Firew | |
| dc.date.accessioned | 2025-05-14T09:33:34Z | |
| dc.date.available | 2025-05-14T09:33:34Z | |
| dc.date.issued | 2024-10 | |
| dc.description.abstract | The safety and reliability of railway infrastructure are dependent on diligent maintenance and inspection practices. A well-organized maintenance approach is imperative in ensuring the safety and reliability of a railway system. One of the most important aspects of railway maintenance is the detection and monitoring of track defects, which can lead to catastrophic failures if left unaddressed. While existing techniques can be beneficial in certain respects, they are incapable to provide a comprehensive view of the structural conditions of railway tracks, compromising ride safety and quality. This research addresses this gap by developing a model for railway track structural condition assessment using Multi-Criteria Decision Making (MCDM). Leveraging an extensive literature review the model assigns weights to key track components and defect categories through the Best-Worst method. Mamdani Fuzzy logic integrates diverse defect criteria and translates qualitative severity evaluations into numerical scores. The analysis reveals rails (48.16%) as the most critical component, followed by sleepers (21.05%). Within rail defects, transverse fissures hold the highest weight (43.75%). Validated through a case study, the model demonstrates strong agreement with actual results. A user-friendly application built on FlutterFlow facilitates detailed track condition assessments, encompassing individual defect categories, component health, and overall track structure status. This model empowers data-driven decision-making for railway authorities, enabling them to prioritize and address track issues effectively. The MCDM approach provides a structured framework for maintenance planning. Ultimately, this research aims to refine railway track infrastructure management through a structured and data-driven decision-making process. | |
| dc.identifier.uri | https://etd.aau.edu.et/handle/123456789/5471 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | Best-Worst method | |
| dc.subject | Fuzzy logic | |
| dc.subject | Multi-Criteria Decision-Making Method | |
| dc.subject | Railway Track Structural Conditions | |
| dc.title | Enhancing Railway Track Structural Condition Assessment Using Multi-Criteria Decision-Making Method | |
| dc.type | Thesis |