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Keywords: ballast degradation, aggregate, fouling, image segmentation, angularity index.

Summary

Ballast fouling, often associated with deteriorating railroad track performance, refers to the condition when the ballast layer changes its composition and becomes much finer in grain size distribution. This paper describes an image analysis approach to characterize different stages of railroad ballast degradation studied using Los Angeles abrasion testing in the laboratory. An aggregate image analysis approach is utilized to investigate ballast particle abrasion and breakage trends at every stage through detailed quantifications of individual ballast particle size and shape properties. Aggregate image processing or segmentation techniques have been also developed and used in this study to analyze the two-dimensional images of ballast aggregate samples captured by a commonly used DSLR camera in the field for extraction and analyses of individual aggregate particle size and shape properties. The segmented individual particle images were fed into the validated University of Illinois Aggregate Image Analyzer (UIAIA) processing algorithms to compute particle size and shape properties using the imaging based indices of flat and elongated ratio (FER), angularity index (AI), and surface texture index (STI). The performance of the field imaging and segmentation methodology was evaluated by means of a case study involving field images of railroad aggregate samples collected from various ballast depths in a mainline freight railroad track. Image analysis results of ballast particles larger than 9.5 mm (3/8 in.) scanned after a different number of turns of the LA abrasion drum showed good correlations between percent changes in aggregate shape properties, i.e., imaging based flatness and elongation, angularity and surface texture indices, and the fouling index (FI). Such relationships to be established between in-service track fouling levels and ballast size and shape properties using similar field imaging techniques would help to better understand field degradation trends and as a result, improve ballast serviceability and life cycle performance.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 104 PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo Paper 123 Investigation of Ballast Degradation and Fouling Trends using Image Analysis M. Moaveni, Y. Qian, H. Boler, D. Mishra and E. Tutumluer Department of Civil and Environmental Engineering, University of Illinois, Urbana-Champaign, USA doi:10.4203/ccp.104.123 purchase the full-text of this paper Full Bibliographic Reference for this paper M. Moaveni, Y. Qian, H. Boler, D. Mishra, E. Tutumluer, "Investigation of Ballast Degradation and Fouling Trends using Image Analysis", in J. Pombo, (Editor), "Proceedings of the Second International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Stirlingshire, UK, Paper 123, 2014. doi:10.4203/ccp.104.123 Keywords: ballast degradation, aggregate, fouling, image segmentation, angularity index. Summary Ballast fouling, often associated with deteriorating railroad track performance, refers to the condition when the ballast layer changes its composition and becomes much finer in grain size distribution. This paper describes an image analysis approach to characterize different stages of railroad ballast degradation studied using Los Angeles abrasion testing in the laboratory. An aggregate image analysis approach is utilized to investigate ballast particle abrasion and breakage trends at every stage through detailed quantifications of individual ballast particle size and shape properties. Aggregate image processing or segmentation techniques have been also developed and used in this study to analyze the two-dimensional images of ballast aggregate samples captured by a commonly used DSLR camera in the field for extraction and analyses of individual aggregate particle size and shape properties. The segmented individual particle images were fed into the validated University of Illinois Aggregate Image Analyzer (UIAIA) processing algorithms to compute particle size and shape properties using the imaging based indices of flat and elongated ratio (FER), angularity index (AI), and surface texture index (STI). The performance of the field imaging and segmentation methodology was evaluated by means of a case study involving field images of railroad aggregate samples collected from various ballast depths in a mainline freight railroad track. Image analysis results of ballast particles larger than 9.5 mm (3/8 in.) scanned after a different number of turns of the LA abrasion drum showed good correlations between percent changes in aggregate shape properties, i.e., imaging based flatness and elongation, angularity and surface texture indices, and the fouling index (FI). Such relationships to be established between in-service track fouling levels and ballast size and shape properties using similar field imaging techniques would help to better understand field degradation trends and as a result, improve ballast serviceability and life cycle performance. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £65 +P&P)
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