Development of Performance Prediction Models for Gravel Roads Using Markov Chains
American Journal of Civil Engineering
Volume 7, Issue 3, May 2019, Pages: 73-81
Received: Apr. 22, 2019; Accepted: May 28, 2019; Published: Jul. 22, 2019
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Authors
Waleed Aleadelat, Department of Civil and Architectural Engineering, University of Wyoming, Laramie, USA
Shaun Wulff, Department of Statistics, University of Wyoming, Laramie, USA
Khaled Ksaibati, Department of Civil and Architectural Engineering, University of Wyoming, Laramie, USA
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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.
Keywords
Gravel Roads, Markov Chains, Performance Models
To cite this article
Waleed Aleadelat, Shaun Wulff, Khaled Ksaibati, Development of Performance Prediction Models for Gravel Roads Using Markov Chains, American Journal of Civil Engineering. Vol. 7, No. 3, 2019, pp. 73-81. doi: 10.11648/j.ajce.20190703.12
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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