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Development of Performance Prediction Models for Gravel Roads Using Markov Chains

Received: 22 April 2019     Accepted: 28 May 2019     Published: 22 July 2019
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Abstract

The Wyoming technology Transfer Center (WYT2/ LTAP) is currently in the process of developing a Gravel Roads Management System (GRMS) in Wyoming. One of the major components of this new GRMS is developing a comprehensive optimization methodology for Maintenance and Rehabilitant (M&R) activities. To support the new optimization methodology, this research study established multiple performance models to predict the deterioration patterns of gravel roads in Wyoming. Condition data, in addition to the average deterioration rates, for approximately 1931km (1200 miles) of gravel road segments were used to develop these models. A probabilistic modeling approach using Markov Chains (MC) was adopted in this study to establish these prediction models. The developed prediction equations obtained from fitting these models include all the possible deterioration modes of gravel roads such as potholes, washboards, loose aggregate, and rutting. Generally, it was found that the average service life of a gravel road is around 12 months without any maintenance intervention. In addition, potholes, rutting, and washboards are the main failure modes for these types of roads.

Published in American Journal of Civil Engineering (Volume 7, Issue 3)
DOI 10.11648/j.ajce.20190703.12
Page(s) 73-81
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Gravel Roads, Markov Chains, Performance Models

References
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[2] Huntington, G., and K. Ksaibati. 2007. Gravel Roads Surface Performance Modeling. Transportation Research Record: Journal of the Transportation Research Board, No. 2016, pp. 56-64. https://doi.org/10.3141/2016-07.
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[9] Huntington, G., and Ksaibati K., 2011a. Implementation guide for the management of unsealed gravel roads. Transportation Research Record: Journal of the Transportation Research Board, No. 2205, 189-197.
[10] Chamorro, A., and Tighe, S. L. 2015. Optimized maintenance standards for unpaved road networks based on cost-effectiveness analysis. Transportation Research Record: Journal of the Transportation Research Board, No. 2473, 56-65.
[11] Walker, D. 1989. Gravel PASER Manual: Pavement Surface Evaluation and Rating. Wisconsin Transportation Information Center, Madison.
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[17] Abaza, K. A. 2016. Simplified staged-homogenous Markov model for flexible pavement performance prediction. Road Materials and Pavement Design, 17 (2), 365-381.
[18] Hassan, R., Lin, O., and Thananjeyan, A., 2017. Probabilistic modelling of flexible pavement distresses for network management. International Journal of Pavement Engineering, 18 (3), 216-227.
[19] Osorio-Lird, A., Chamorro, A., Videla, C., Tighe, S., and Torres-Machi, C. 2017. Application of Markov chains and Monte Carlo simulations for developing pavement performance models for urban network management. Structure and Infrastructure Engineering, 1-13.
[20] Huntington, G., and Ksaibati, K., 2009. Annualized Road Works Cost Estimates for Unpaved Roads. Journal of Transportation Engineering 135 (10), 702-710.
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Cite This Article
  • APA Style

    Waleed Aleadelat, Shaun Wulff, Khaled Ksaibati. (2019). Development of Performance Prediction Models for Gravel Roads Using Markov Chains. American Journal of Civil Engineering, 7(3), 73-81. https://doi.org/10.11648/j.ajce.20190703.12

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    ACS Style

    Waleed Aleadelat; Shaun Wulff; Khaled Ksaibati. Development of Performance Prediction Models for Gravel Roads Using Markov Chains. Am. J. Civ. Eng. 2019, 7(3), 73-81. doi: 10.11648/j.ajce.20190703.12

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    AMA Style

    Waleed Aleadelat, Shaun Wulff, Khaled Ksaibati. Development of Performance Prediction Models for Gravel Roads Using Markov Chains. Am J Civ Eng. 2019;7(3):73-81. doi: 10.11648/j.ajce.20190703.12

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  • @article{10.11648/j.ajce.20190703.12,
      author = {Waleed Aleadelat and Shaun Wulff and Khaled Ksaibati},
      title = {Development of Performance Prediction Models for Gravel Roads Using Markov Chains},
      journal = {American Journal of Civil Engineering},
      volume = {7},
      number = {3},
      pages = {73-81},
      doi = {10.11648/j.ajce.20190703.12},
      url = {https://doi.org/10.11648/j.ajce.20190703.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajce.20190703.12},
      abstract = {The Wyoming technology Transfer Center (WYT2/ LTAP) is currently in the process of developing a Gravel Roads Management System (GRMS) in Wyoming. One of the major components of this new GRMS is developing a comprehensive optimization methodology for Maintenance and Rehabilitant (M&R) activities. To support the new optimization methodology, this research study established multiple performance models to predict the deterioration patterns of gravel roads in Wyoming. Condition data, in addition to the average deterioration rates, for approximately 1931km (1200 miles) of gravel road segments were used to develop these models. A probabilistic modeling approach using Markov Chains (MC) was adopted in this study to establish these prediction models. The developed prediction equations obtained from fitting these models include all the possible deterioration modes of gravel roads such as potholes, washboards, loose aggregate, and rutting. Generally, it was found that the average service life of a gravel road is around 12 months without any maintenance intervention. In addition, potholes, rutting, and washboards are the main failure modes for these types of roads.},
     year = {2019}
    }
    

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  • TY  - JOUR
    T1  - Development of Performance Prediction Models for Gravel Roads Using Markov Chains
    AU  - Waleed Aleadelat
    AU  - Shaun Wulff
    AU  - Khaled Ksaibati
    Y1  - 2019/07/22
    PY  - 2019
    N1  - https://doi.org/10.11648/j.ajce.20190703.12
    DO  - 10.11648/j.ajce.20190703.12
    T2  - American Journal of Civil Engineering
    JF  - American Journal of Civil Engineering
    JO  - American Journal of Civil Engineering
    SP  - 73
    EP  - 81
    PB  - Science Publishing Group
    SN  - 2330-8737
    UR  - https://doi.org/10.11648/j.ajce.20190703.12
    AB  - The Wyoming technology Transfer Center (WYT2/ LTAP) is currently in the process of developing a Gravel Roads Management System (GRMS) in Wyoming. One of the major components of this new GRMS is developing a comprehensive optimization methodology for Maintenance and Rehabilitant (M&R) activities. To support the new optimization methodology, this research study established multiple performance models to predict the deterioration patterns of gravel roads in Wyoming. Condition data, in addition to the average deterioration rates, for approximately 1931km (1200 miles) of gravel road segments were used to develop these models. A probabilistic modeling approach using Markov Chains (MC) was adopted in this study to establish these prediction models. The developed prediction equations obtained from fitting these models include all the possible deterioration modes of gravel roads such as potholes, washboards, loose aggregate, and rutting. Generally, it was found that the average service life of a gravel road is around 12 months without any maintenance intervention. In addition, potholes, rutting, and washboards are the main failure modes for these types of roads.
    VL  - 7
    IS  - 3
    ER  - 

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Author Information
  • Department of Civil and Architectural Engineering, University of Wyoming, Laramie, USA

  • Department of Statistics, University of Wyoming, Laramie, USA

  • Department of Civil and Architectural Engineering, University of Wyoming, Laramie, USA

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