Evaluating Surface Material Loss for Gravel Road

Abstract

Gravel roads require a continuous maintenance and regraveling cycle to maintain the required surface quality and the desired level of service. Surface material loss is a way of deterioration of unpaved road by which the reduction of thickness of gravel wearing course by various factors through time. To keep it in mind the responsible body or roads authority should predict the gravel loss in order to account for maintenance or rehabilitation requirement after a given period. This should be based up on regraveling frequency from predicted gravel loss during construction or after the first maintenance. Here main factors included for study are ADT, mean monthly precipitation of locations, plasticity index of surfacing material and absolute value of gradient of the road as independent variables and gravel loss as dependent variable. The road segments selected are Sodo-Gesuba road(29km), Humbo-Menuka(13km) and Sodo zuriya-Gulgula(11km) road segments. The monthly rainfall data from SNNPR meteorological agency was used as secondary data and all other data was collected from field survey. The data collected for modeling are based up on the basic scientific methods and collected data was analyzed by statistical software IBM SPSS statistics 20 and Microsoft Excel 2019 in order to develop a model. The developed model indicates that gradient of the road is critical factor hence its unit change accelerates loss of gravel by 4.7316, a unit change in ADT lead to 0.1225 change in gravel loss, a unit change in mean monthly precipitation of locations lead to 0.1460 change in gravel loss and a unit change in plasticity index of surfacing material lead to -1.3473 change in gravel loss. From regression output R2 also called coefficient of determination which is the proportion of variance in the dependent variable that can be explained by the independent variables, and is equal to 0.985 for model which means gravel loss contributing factors here in this study explain 98.5% of variability of gravel loss and that statistical package strongly reinforced the correlation.

Keywords: Gravel loss, Regraveling, gravel resurfacing, absolute gradient, multiple linear regression and modeling

DOI: 10.7176/CMR/11-6-02

Publication date: August 31st 2019