A Trapezoidal Fuzzy TOPSIS-CRITIC-Based Decision Support Model for Railway Track System Selection: Ballasted vs. Ballast-less

This study presents an innovative decision-support framework for selecting optimal railway track superstructures (ballasted vs. ballast-less) by integrating technical feasibility assessment with life-cycle cost analysis. In this matter Trapezoidal Fuzzy TOPSIS-CRITIC (TraF TOPSIS-CRITIC) methodology was developed that addresses two critical gaps in existing approaches: (1) systematic handling of data uncertainty through fuzzy logic and confidence intervals, and (2) objective criteria weighting to minimize selection bias. The framework evaluates six key technical parameters (speed, traffic load, tunnel/bridge length, settlement, and groundwater) alongside 100-year life-cycle costs. Applied to a 24 km Iranian high-speed rail case study, the model demonstrates strong preference for ballast-less tracks in bridge/tunnel-intensive sections, and ballasted solutions favorable in limiting geotechnical conditions (groundwater <1.5 m, differential settlement >1 mm). Sensitivity analyses reveal economic selection thresholds dependent on inflation-discount rate relationships, with validation against IRS 70727 benchmarks confirming <5% deviation. Key innovations include the first successful implementation of TraF TOPSIS-CRITIC in rail infrastructure. For infrastructure managers, this research provides a quantifiable, bias-free decision tool particularly valuable for networks transitioning to higher operational speeds, while establishing new best practices for holistic track system evaluation that balance technical constraints with long-term economic performance.