Evaluating railroad ballast degradation trends using machine vision and machine learning techniques

Benjamin L. Delay, Maziar Moaveni, John M. Hart, Phil Sharpe, Erol Tutumluer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recently, automatic ballast sampling (ABS) methods have been introduced to the railroad industry to obtain a sample of ballast and underlying layers. Currently, manual-visual classification methods are used by experts to identify fouling conditions and degradation trends in the collected ballast samples. This paper presents an innovative approach developed for objective classification of ballast degradation using the combination of machine vision and machine learning techniques. Initially, various computer vision algorithms were used to generate features associated with images of ballast cross sections at different degradation levels. Next, the generated features were used alongside a visual classification database provided by experts to develop, train, validate, and test a feed forward artificial neural network (ANN) using a supervised learning method. This work was further extended by implementing convolutional neural networks (CNNs) to serve as automatic feature generators. The findings of this study showed that the proposed CNNs with an optimized topology could successfully classify ballast fouling in an effective and repeatable fashion with reasonable error levels. Further improvement of this technology holds the potential to provide a tool for consistent and automated ballast inspection and life cycle analysis intended to improve the safety and network reliability of US railroad transportation system.

Original languageEnglish (US)
Title of host publicationGeotechnical Special Publication
EditorsThomas L. Brandon, Richard J. Valentine
PublisherAmerican Society of Civil Engineers
Pages432-441
Number of pages10
EditionGSP 277
ISBN (Electronic)9780784480434
DOIs
StatePublished - 2017
EventGeotechnical Frontiers 2017 - Orlando, United States
Duration: Mar 12 2017Mar 15 2017

Publication series

NameGeotechnical Special Publication
NumberGSP 277
Volume0
ISSN (Print)0895-0563

Other

OtherGeotechnical Frontiers 2017
Country/TerritoryUnited States
CityOrlando
Period3/12/173/15/17

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Architecture
  • Building and Construction
  • Geotechnical Engineering and Engineering Geology

Fingerprint

Dive into the research topics of 'Evaluating railroad ballast degradation trends using machine vision and machine learning techniques'. Together they form a unique fingerprint.

Cite this