A Hybrid Multi-Criteria Decision Making Model for Defect-Based Condition Assessment of Railway Infrastructure

Abstract

The condition of railway infrastructure, such as rails, ballasts and sleepers, should always be monitored and analyzed to ensure ride safety and quality for both passengers and freight. It is hard to assess the condition of railway infrastructure due to the existence of various components. The existing condition assessment models are mostly limited to only assess track geometry conditions and structural condition of the railway infrastructure. Therefore, the present research develops a defect-based structural and geometrical condition model of railway infrastructure. The defects of each component are identified and examined through literature and experts in the field. Two main inputs are used to develop the model: (1) the relative weight of importance for components, defects and their categories and (2) defects severities. To obtain the relative weights, the analytic network process (ANP) technique is adopted. Fuzzy logic is used to unify all the different defect criteria and to interpret the linguistic condition assessment grading scale to a numerical score. Hence, the technique for order preference by similarity to ideal Solution (TOPSIS) is used to integrate both weights and severities to determine the railway infrastructure condition. The developed model gives a detailed condition of the railway infrastructure by representing a three-level condition state, for defect categories, components and an overall railway infrastructure. The developed model is implemented to five case studies from Ontario, Canada. The developed model is validated by comparing its results with the real case studies results, which shows similar results, indicating the robustness of the developed model. This model helps in minimizing the inaccuracy of railway condition assessment through the application of severity, uncertainty mitigation and robust aggregation

Developments in 3D Visualisation of the Rail Tunnel Subsurface for Inspection and Monitoring

“…AFE, namely defect severity ranking, proves less researched. Contrary to our initial expectations of exclusively dictionary-based schemes, of studies found, most now adopt contextual evaluation via fuzzy logic devices [221,222] and probabilistic analysis [33,223]. We infer more complex evaluation grounds are being considered in parallel when grading repair urgency, if not yet for RTSSI.…”

Effect of the Metro Train on the Smoke Back-Layering Length under Different Tunnel Cross-Sections

“…The first is the train blockage area, with high ventilation velocity; the second is the At different longitudinal ventilation velocities, the smoke back-layering length will be different for the same heat release rate. When the longitudinal ventilation velocity is lower than the critical ventilation velocity, smoke back-layering will occur, which can be divided into two cases due to the influence of the long metro train [24,25]: (1) smoke is restricted to the length of the metro train; (2) smoke spread exceeds the length of the metro train, as shown in Figure 2.…”

“…As part of city infrastructure, a rail transit system play a pivotal role in transportation [1]. The development of the metro has greatly facilitated the travel of people, but it has also brought many safety problems.…”

An Integrated Infrastructure Prioritization Model: Case Study of Tripoli, Lebanon

“…Some studies aimed at exploiting the use of multi-criteria decision making algorithms for assessing the condition of infrastructure systems. (El-khateeb et al, 2021) analyzed the performance condition of railway infrastructure using a defect-based model. The overall condition was obtained based on rails, sleeper, track geometry, ballast and insulated rail joints.…”