Development of Performance Prediction Models for Gravel Roads Using Markov Chains

Waleed Aleadelat; Shaun Wulff; Khaled Ksaibati

doi:doi:10.11648/j.ajce.20190703.12

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Development of Performance Prediction Models for Gravel Roads Using Markov Chains

Waleed Aleadelat, Shaun Wulff, Khaled Ksaibati

Published in American Journal of Civil Engineering (Volume 7, Issue 3)

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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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

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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Plain Text BibTeX RIS

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}
}

Copy | Download

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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