Gravel Roads Surface Performance Modeling

Author:

Huntington George1,Ksaibati Khaled2

Affiliation:

1. Wyoming Technology Transfer (LTAP) Center, University of Wyoming, 1000 East University Avenue, Department 3295, Laramie, WY 82071-3295.

2. Department of Civil and Architectural Engineering, University of Wyoming, 1000 East University Avenue, Department 3295, Laramie, WY 82071-3295.

Abstract

Twenty gravel road study sections at 10 sites in north-central Wyoming were monitored from September 2005 through June 2006. Windshield or mobile, visual survey ratings of the sections and field measurements were taken weekly. Surfacing gravel samples were collected, and their gradations were determined. Traffic speeds and volumes by class were collected with a two-tube system. (A two-tube system counts traffic by receiving signals from two tubes placed 8 ft apart across the road. By a comparison of the times at which signals are received from each tube, the speed of the traffic can be determined, as can the number and spacing of axles.) Statistical analyses generated regression models that allowed the prediction of the service life of an unmaintained gravel road. Traffic speeds, traffic volumes, and surfacing gravel properties were shown to have the greatest influence on gravel roads’ deterioration rates. For these typical Wyoming county roads with good geometry, good drainage, and adequate gravel thicknesses, the typical failure mode was shown to be either potholes or washboards (corrugations). The typical life of gravel roads without maintenance was shown to be in the range of several months to 1 year. Climatic effects were shown to be related to precipitation more than seasonality, at least in Wyoming's dry–freeze climate.

Publisher

SAGE Publications

Subject

Mechanical Engineering,Civil and Structural Engineering

Reference5 articles.

Cited by 13 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Modeling Dust Generation on Low-Volume Roads Based on Vehicle Speed and Surface Fines Content;Transportation Research Record: Journal of the Transportation Research Board;2023-03-18

2. Gravel Loss Prediction Model Using Beta Regression;Transportation Research Record: Journal of the Transportation Research Board;2023-02-03

3. Literature Review on Gravel Road Maintenance: Current State and Directions for Future Research;Transportation Research Record: Journal of the Transportation Research Board;2022-11-14

4. Artificial neural network-based roughness prediction models for gravel roads considering land use;Innovative Infrastructure Solutions;2022-05-06

5. A data-driven approach for gravel road maintenance;2021 International Conference on Maintenance and Intelligent Asset Management (ICMIAM);2021-12-12

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