PREDICTION OF PAVEMENT CONDITION USING Micro PAVER

Abstract

Pavements represent an important infrastructure to all countries. In Sudan, little investments have been made in constructing of the new national road. This road network requires great care through conducting periodic evaluation and timely maintenance to keep the network operating under acceptable level of service.

Pavement condition prediction models can greatly enhance the capabilities of a pavement management system. These models allow Pavement authorities to know and predict the condition of the pavements and consequently determine the maintenance needs and activities, predicting the timing of maintenance or rehabilitation, and estimating the long range funding requirements for preserving the performance of the network.

In this study, historical data of pavement distress and pavement condition on the national road network of northern Kordofan state, Sudan were collected. These data were categorized, processed, and analyzed using Micro PAVER software which is successfully applied for calculating and predicting the pavement condition for the road under study. These data have been used to generate prediction of pavement condition models for Sudan National Road -Northern Kordofan state sector. This network composed of six pavement section with an overall length of 626 km.

According to micro PAVER classification method, The most distress observed on the pavement sections were due to other reasons are constitute 69 % (such as bleeding, patching, and slippage cracking), furthermore load related causes (such as rutting and alligator cracking) were constitute 22 % and environmental effects (such as weathering, longitudinal and transverse cracking, and block cracking) are constitute 19%.

IV

The result of pavement condition index in 2005 shows that one section total out of six, its pavement condition is “good” rating with average PCI

=90, and three sections have pavement condition “satisfactory” rating with an average PCI of 78, and one section in fair condition with an average PCI of 64, and finally poor condition occurs in one section with an average PCI of 53. In the overall, the network had an average weighted PCI of 71, which was considered “satisfactory” rating.

The prediction condition for November 2014 shows that the overall predicted condition of the network had an average weighted PCI of 43, which was considered “poor” rating. In details four road section having 77% from the pavement area are lies in poor condition with an average PCI of 49. And one section having 14% the pavement area is in very poor condition with an average PCI of 36. The remaining 9% is in serious condition with an average PCI of 25 which is simulating one section from network under study.

The prediction equation generated by the software for the family road network is PCI=100-3.08428x (x=age sine last construction or rehabilitation). The equation is having coefficient of correlation =0.885 which acceptable.