Researcher: Iman Soleimanmeigouni & Alireza Ahmadi
The goal of railway infrastructure managers is to keep the RAMS parameters of railway system within acceptable limits at lowest possible cost. An efficient an effective way of achieving this goal is to employ applicable and effective maintenance strategy. The aim of this project is to develop an integrated data driven methodology to support maintenance decision making. Obviously, prediction of track geometry degradation and effectiveness of tamping recovery are the key inputs for RAMS assessment of track (see Fig. 1). In addition, isolated defects must be considered as they are the driving factors for safety of railway operation (see Fig 2). In this regard, track geometry degradation, isolated defect, and tamping effectiveness are modelled and integrated for long term prediction of track geometry condition over a track line. The developed model will be used to predict and simulate track geometry behaviour and to evaluate RAMS parameters by adopting different maintenance plans. This will enable infrastructure managers to compare different maintenance plans with respect to the RAMS and LCC parameters and to find the optimal maintenance plan (see Fig. 3).