Evaluating Railroad Ballast Degradation Trends Using Machine Vision and Machine Learning Techniques-Reference-Cited by-同舟云学术

Evaluating Railroad Ballast Degradation Trends Using Machine Vision and Machine Learning Techniques

Published:2017-03-30 Issue: Volume: Page:
ISSN:
Container-title:Geotechnical Frontiers 2017
language:
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Author:

Delay Benjamin L.¹²³⁴⁵,Moaveni Maziar¹²³⁴⁵,Hart John M.¹²³⁴⁵,Sharpe Phil¹²³⁴⁵,Tutumluer Erol¹²³⁴⁵

Affiliation:

1. Graduate Research Assistant, Dept. of Electrical and Computer Engineering, Univ. of Illinois at Urbana-Champaign, Urbana, IL 61801.

2. Postdoctoral Research Associates, Dept. of Civil and Environmental Engineering, Univ. of Illinois at Urbana-Champaign, Urbana, IL 61801.

3. Principle Research Engineer, Computer Vision and Robotics Laboratory, Beckman Institute, Univ. of Illinois at Urbana-Champaign, Urbana, IL 61801.

4. Associate, Trackbed Technology, AECOM, Nottingham, Nottinghamshire NG9 6RZ, U.K.

5. Professor, Dept. of Civil and Environmental Engineering, Univ. of Illinois at Urbana-Champaign, Urbana, IL 61801.

Publisher

American Society of Civil Engineers

Link

http://ascelibrary.org/doi/pdf/10.1061/9780784480441.045

Reference19 articles.

1. Tracking Construction Site Resources with Machine Vision;Brilakis I.;Georgia School of Civil & Environmental Engineering,2010

2. Camargo L. F. M. Resendiz E. Hart J. M. Edwards J. R. Ahuja N. Barkan C. P. L. (2011). Machine Vision Inspection of Railroad Track Nextrans USDOT Region V Regional University Transportation Center Urbana.

3. Chiang C.C. (1989). Effects of Water and Fines on Ballast Performance in Box Tests Master of Science Degree Project Report No. AAR89-366P University of Massachusetts USA.

4. Devroye L. (1986). Non-uniform random variable generation Springer-Verlag New York McGill University Montreal Canada.

5. Protocol for Testing Railway Ballast in Large Scale Cyclic Triaxial Equipment;Ebrahimi A.;ASTM Geotechnical Testing Journal,2012

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