Railway infrastructure, such as rails, ballasts, sleepers, etc., condition should be always monitored and analyzed to ensure safety and quality of the ride for both passengers and freight. Railway infrastructure has various components from different materials which make it hard to assess and monitor its condition. The majority of the existing conditions assessment models are limited either in terms of components or techniques, many models focus on the assessment of the track geometry condition depending on only the data collected from the track recording cars and a few condition assessment models to evaluate the structural condition of the railway infrastructure. Other developed models take into consideration one component or focus in utilizing one inspection technique. Therefore, the development of a comprehensive condition assessment tool that covers the numerous railway infrastructure components and the different inspection techniques is needed to ensure the safety and the quality of the service for the public.
The objective of this research is to develop a defect-based condition assessment model of Railway infrastructure. This model aims to cover the structural and geometrical defects that are associated with the different components of railway infrastructure. The railway infrastructure was divided into five main components rails, sleepers (Ties), ballast, track geometry and insulated rail joints, for each component their defects were collected and categorized. Two main inputs have been used to develop the model, firstly the relative importance weights of the components, Defect Categories, and defects, secondly the defects severities. To obtain the relative importance weights the Analytic Network Process (ANP) model was adopted, ANP covers the interdependencies between the components and their defects. Fuzzification technique was used to uniform all the different defects criteria and to translate the linguistic condition assessment grading scale to a numerical score. Furthermore, the Weighted Sum Mean was used to integrate both the weights and severities to define the conditions and to evaluate the overall condition of the railway infrastructure. The data utilized in this research was obtained from railway condition classification manuals, previous research, and questionnaires distributed to professionals in Canada. The fruit of this fusion was also presented in a user-friendly automated tool using excel. The developed model was implemented in two case studies from Ontario, Canada. The model outputs and the decision made for the case studies were compared and the model gave a similar condition. This model helps in minimizing the inaccuracy of railway condition assessment through the application of severity, uncertainty mitigation, and robust aggregation. It also benefits asset managers by providing detailed condition of the Railway infrastructure, the condition of the components, defect categories and an overall condition for maintenance, rehabilitation, and budget allocation purposes
