2023

saeed goodarzi, University of Massachusetts, Amherst
Kevin (Hamed) Kashani, Loram Technologies Inc
Jimi Oke, University of Massachusetts, Amherst
Carlton Ho, University of Massachusetts, Amherst

The Estimation of track degradation rate is a challenging step in scheduling preventive maintenance interventions due to its uncertain nature. To tackle this challenge, a large dataset of geometry, Ground Penetrating Radar (GPR), and Light Detection And Ranging (LiDAR) are investigated. The data are collected from passenger revenue track between 2011 and 2021. Stochastic models are proposed for estimating the expected track degradation rates based on subsurface and drainage conditions of blocks, which are two important factors. A novel framework is proposed for automatically determining drainage conditions of track from LiDAR data. Results indicate that the probability of having high track degradation rates in highly fouled ballast is 10 times more than in clean ballast. Analyzing the effect of drainage conditions revealed that blocks with poor drainage conditions are 1.3 times more likely to have high track degradation rates than blocks with moderate drainage conditions. Thus, building a ditch with a depth of 2.5 ft (0.76 m) is necessary for improving drainage conditions in cut sections. The results of this study enhance scheduling geometry maintenance and provide reliable information for making decisions about other types of maintenance such as cleaning ballast layer or building ditches.

Ivan Pagano Aragona, ENSCO, Inc.

Jacob Fenno, Amtrak Engineering
Tamara Nazhmutdinova, Amtrak Engineering

Wesley Thomas, MegaCORP Ltd.
Edwin Vollebregt, Vtech CMCC

Stephen Wilk, Association of American Railroads