The formulation and application of a gravel loss model in management of gravel roads in Iringa region, Tanzania

Among various gravel roads distress prediction models in existence, a gravel loss prediction model is considered critical in selecting the optimal re-gravelling schedule for effective maintenance management of gravel roads. However, due to the number of variables contributing to deterioration of gravel roads and hence gravel loss, gravel loss prediction models are not readily transferable from one geographical location to another, particularly if the locations in question differ in climatic condition, gravel material characteristics, quality of construction and maintenance, terrain, traffic characteristics and driver behaviours. Addressing the aforementioned local characteristics pose a challenge to existing international gravel loss prediction models when employed locally, resulting in inaccurate prediction of gravel loss. Hence the need for a gravel loss prediction model to be formulated locally so as to address local characteristics influencing gravel roads deterioration. The main objective of this study was to formulate locally, a statistically accurate gravel loss prediction model for marginal gravel materials employed to surface gravel roads in Iringa region. The intention was to address local characteristics influencing gravel roads deterioration in the region. To promote research on gravel roads management, the author has published seven papers and presented ten papers in established journals and conferences respectively, as indicated in the Appendix 13. It is author expectation that, given the right impetus, locally formulated gravel loss prediction models can be incorporated, as one of a tool, in gravel roads management systems (GRMS). The literature review focused on the gravel road condition surveys, modelling exercises, gravel loss, and a review of existing gravel loss prediction models. The literature review also examined the version of GRMS currently practiced in Tanzania by its road organizations. The study used factorial experimental design. Parameters which are deemed to influence the gravel loss were collected and studied. A questionnaire was used to study the status of gravel road MMS in Tanzania. The data obtained from the questionnaire responses were analysed with the aid of Statistical Package for Social Sciences (SPSS) and Microsoft Excel. A detailed gravel road condition survey of each 300 m long test section was carried out during site visits. The measurement of gravel loss through the change in average height loss formed a crucial part of the study. The modelling of a gravel loss prediction model was performed using pavemetric principles, the term coined by this study, which is principally based on econometric principles. From the analysis of questionnaire responses, it was evident that each Tanzania Roads Agency (TANROADS) regional office and district council works department needs to have a unique MMS and GRMS which reflect their operating capacity. The results of the condition survey led to the formulation of a new range of grading coefficient (GC) to suit the local marginal materials. The gravel loss survey results assisted in establishing gravel loss thresholds. The thresholds were based on the rate of gravel loss noted in the study. These thresholds can be employed to enhance the quality control of gravel roads construction and maintenance practices. The study formulated a gravel loss prediction model for Iringa region. The process utilized average daily traffic, climate, and derivatives of sieve analysis and Atterberg limits. The model is statistically significant at 1 % level. The model gives a constant gravel loss of 0.1 mm per annum regardless the state of the six variables in the model. This was attributed to autonomous loss that is the amount of gravel material lost through mechanical and chemical weathering. Recommendations include the need for gravel loss prediction models to reflect local characteristics influencing the deterioration of the gravel roads in question and the modelling capacity of local road agencies. Areas for further studies are highlighted.