Advertisement

Evaluation of Railroad Ballast Field Degradation Using an Image Analysis Approach

  • Maziar Moaveni
  • Erol TutumluerEmail author
  • John M. Hart
  • Mike McHenry
Conference paper
Part of the Sustainable Civil Infrastructures book series (SUCI)

Abstract

Identifying the level of ballast degradation generally involves ballast sampling and mechanical sieve analyses in the laboratory, which can be time consuming, laborious and costly. As an automated alternative, image processing techniques has the potential to directly and objectively assess ballast condition and degradation levels from high resolution images of ballast layers captured in the field or reproduced in the laboratory. This paper presents the development stages and implementation of an innovative image processing method for assessing the degradation levels of ballast using ballast cross section images collected in the field and also reproduced in the lab. Advanced image enhancement methods, including gamma adjustment, histogram equalization, and bi-lateral image filtering, combined with image segmentation techniques such as watershed algorithm and image thresholding, were used to successfully extract size and shape properties of individual ballast particles as a mean to quantify the level of ballast degradation. In order to capture images of the ballast layers in the field, a detailed procedure was developed to ensure the resulting images captured would perform consistently and accurately when processed with the machine vision algorithms. Rapid imaging of a large quantity of ballast samples was needed for producing ground truth data to be used as input into the machine vision algorithms. The results of this study showed that the images captured in the field and the images captured in the lab from the corresponding collected ballast samples looked quite different. This confirmed that a robust image processing algorithm which can be linked to indices based on sieve analysis methods needs to be adjusted/trained from the images and samples collected in the field. The findings of this ballast field and lab imaging study showed promising future potential of the described image processing technique for replacing the tedious and time consuming ballast sampling and sieve analysis processes for evaluating ballast degradation.

Notes

Acknowledgements

The Association of American Railroads (AAR) and TRB Innovations Deserving Exploratory Analysis (IDEA) program have supported the preliminary development of the described machine vision algorithm field imaging of ballast through the research project collaborations of Rail Transportation and Engineering Center (RailTEC) at the University of Illinois and the Transportation Technology Research Inc. (TTCI). The authors of this paper highly appreciate the contributions and technical support from Dave Davis, senior scientist, and Dr. Dingqing Li, Chief of FRA programs, at TTCI. The contents of this paper reflect the views of the authors who are responsible for the facts and the accuracy of the data presented herein. This paper does not constitute a standard, specification, or regulation.

References

  1. Selig, E.T., Waters, J.M.: Track Geotechnology and Substructure Management. Thomas Telford Publications, London (1994)CrossRefGoogle Scholar
  2. Chiang, C.C.: Effects of water and fines on ballast performance in box tests. Master of Science Degree Project Report No. AAR89-366P, University of Massachusetts (1989)Google Scholar
  3. Han, X., Selig, E.T.: Effects of fouling on ballast settlement. In: Proceedings of the 6th International Heavy Haul Railway Conference, Cape Town, South Africa (1997)Google Scholar
  4. Ebrahimi, A., Tinjum, J.M., Edil, T.B.: Protocol for testing railway ballast in large-scale cyclic triaxial equipment. Geotech. Test. J. 35(5), 762–804 (2012)CrossRefGoogle Scholar
  5. Jain, R., Kasturi, R., Schunck, B.G.: Machine Vision. McGraw-Hill Inc, New York (1995)Google Scholar
  6. Descantes, Y., Fosse, Y., Milcent, F.: Automated measurement of railway ballast angularity. J. Mater. Civ. Eng. 18(4), 612–618 (2006)CrossRefGoogle Scholar
  7. Boler, H., Wnek, M., Tutumluer, E.: Establishing linkages between ballast degradation and imaging based aggregate particle shape, texture, and angularity. In: Proceedings of the 2nd International Conference on Transportation Geotechnics, Hokkaido, Japan (2012)Google Scholar
  8. Moaveni, M., Wang, S., Hart, J.M., Tutumluer, E., Ahuja, N.: Evaluation of aggregate size and shape by means of segmentation techniques and aggregate image processing algorithms. Transp. Res. Rec. J. Transp. Res. Board, 2335, 50–59 (2013)Google Scholar
  9. Moaveni, M., Qian, Y., Qamhia, I.I.A., Tutumluer, E., Basye, C., Li, D.: Morphological characterization of railroad ballast degradation trends in the field and laboratory. Transp. Res. Rec. J. Transp. Res. Board, 2545, 89–99 (2016)Google Scholar
  10. Huang, H.: Discrete element modeling of railroad ballast using imaging based aggregate morphology characterization. Ph.D. Dissertation. University of Illinois, Urbana, pp. 1–3 (2009)Google Scholar
  11. Sun, Y., Indraratna, B., Nimbalkar, S.: Three-dimensional characterization of particle size and shape for ballast. Géotechnique Letters 4(3), 197–202 (2014)CrossRefGoogle Scholar
  12. Akhtar, M., Davis, D., LoPresti, J.: Increasing strength and reducing lifecycle cost of concrete tie track. In: Railway Track and Structures, pp. 16–19 (2012)Google Scholar
  13. McHenry, M., LoPresti, J.: Concrete Tie Testing at FAST to Address Improved Track Strength. Technology Digest TD-15-013. AAR/TTCI, Pueblo, Colorado (2015)Google Scholar
  14. Jobson, D., Rahman, Z., Woodell, G.: A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans. Image Process. 6(7), 965–976 (1997)CrossRefGoogle Scholar
  15. Gonzalez, R., Woods, R.: Digital Image Processing, 2nd edn. Prentice Hall, Upper Saddle River (2001)Google Scholar
  16. Pisano, E., Zong, S., Hemminger, M., Deluca, M., Johnston, R., Muller, K., Braeuning, M., Pizer, S.: Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms. J. Digit. Imaging 11(4), 193–200 (1998)CrossRefGoogle Scholar
  17. Paris, S., Kornprobst, P., Tumblin, J., Durand, F.: Bilateral filtering: theory and applications. Found. Trends Comput. Graph. Vis. 4(1), 1–73 (2008)CrossRefGoogle Scholar
  18. Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulation. IEEE Trans. Pattern Anal. Mach. Intell. 13(6), 583–598 (1991)CrossRefGoogle Scholar
  19. Chaudhury, K.N., Sage, D., Unser, M.: Fast bilateral filtering using trigonometric range kernels, IEEE Trans. Image Process. 20(11) (2001)Google Scholar
  20. de Berg, M., van Kreveld, M., Overmars, M., Schwarzkopf, O.: Computational Geometry: Algorithms and Applications, pp. 2–8. Springer, Heidelberg (2000)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Maziar Moaveni
    • 1
  • Erol Tutumluer
    • 1
    Email author
  • John M. Hart
    • 2
  • Mike McHenry
    • 3
  1. 1.Department of Civil and Environmental EngineeringUniversity of Illinois at Urbana-ChampaignUrbanaUSA
  2. 2.Computer Vision and Robotic LaboratoryUniversity of Illinois at Urbana-ChampaignUrbanaUSA
  3. 3.Transportation Technology Center, Inc.PuebloUSA

Personalised recommendations