Chapter
Mar 30, 2017
Evaluating Railroad Ballast Degradation Trends Using Machine Vision and Machine Learning Techniques
Authors: Benjamin L. Delay [email protected], Maziar Moaveni, Ph.D., M.ASCE [email protected], John M. Hart [email protected], Phil Sharpe, Ph.D. [email protected], and Erol Tutumluer, Ph.D., M.ASCE [email protected]Author Affiliations
Publication: Geotechnical Frontiers 2017
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 U.S. railroad transportation system.
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© 2017 American Society of Civil Engineers.
History
Published online: Mar 30, 2017
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Graduate Research Assistant, Dept. of Electrical and Computer Engineering, Univ. of Illinois at Urbana-Champaign, Urbana, IL 61801. E-mail: [email protected]
Postdoctoral Research Associates, Dept. of Civil and Environmental Engineering, Univ. of Illinois at Urbana-Champaign, Urbana, IL 61801. E-mail: [email protected]
Principle Research Engineer, Computer Vision and Robotics Laboratory, Beckman Institute, Univ. of Illinois at Urbana-Champaign, Urbana, IL 61801. E-mail: [email protected]
Associate, Trackbed Technology, AECOM, Nottingham, Nottinghamshire NG9 6RZ, U.K. E-mail: [email protected]
Professor, Dept. of Civil and Environmental Engineering, Univ. of Illinois at Urbana-Champaign, Urbana, IL 61801. E-mail: [email protected]
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Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.