The use of gravel loss predicting models for effective management of gravel roads

Abstract

Paper presented at the 31st Annual Southern African Transport Conference 9-12 July 2012 "Getting Southern Africa to Work", CSIR International Convention Centre, Pretoria, South Africa.To conserve the gravel materials borrow pits (B/P) and gravel materials deployed as surfacing layer of unsealed roads, there is a need to reduce regravelling cycles to optimum level. This can be achieved by reducing the rate of gravel loss (GL) through quantifying locally, the annual GL and addressing factors behind it. Understanding the behaviour of local gravel materials to readily lose fines followed by coarser particles under the action of traffic and climate is of paramount importance in achieving the above goal. This paper advocates the use of the gravel loss predicting model (GLPM) as one of the measures of conserving gravel wearing course and gravel B/P and hence towards effective management of gravel roads. The GL information, as captured by GLPMs, formulated through monitoring GL over the passage of time, will assist those responsible in managing gravel roads to address the root causes of GL and hence reduce the grading and regravelling frequencies.