Assessment of contaminants impact on the surface of pavements In the probability of accidents by Neural Network approach

One of the effective factors in the reduction of the accidents is the improvement of the friction of the existing pavement surface structures, which is often measured with a conventional parameter against skid. Resistance to skid is an important feature of road pavement surface, which has a significant role in providing safety and prevention of accidents resulting from skidding. The aim of this study is to investigate the effect of various contaminants on the skid resistance of asphalt mixture.

There are various pollutants that affect the skid resistance of pavements, such as dust, dune sand, oil and diesel, exhaust fumes, etc. Present research has taken in account the interpretive and comprehensible results from the neural network, which is a data mining method used in predictions and statistical modeling.

The results of this research can be used to select the type of asphalt mix according to the weather conditions of each region. As it is evident, in the desert areas, there is a lot of dune sand on the road surface, which reduces the resistance of the skids or in busy transportation areas, smoke due to the exhaust of trucks, as well as the dust in the air settle on the road surface and reduce the skid resistance.

The data obtained was analyzed using artificial neural network method and it was observed that the sort of contaminants had the most impact on the variation of the skid resistance of the samples. Field observation and installation of electrical equipment in high contamination sites and inspections were performed, and for the reduction of accidents it was recommended that accident prone areas should be eliminated before the first rainy season after the dry seasons, in order to avoid the dangers of skidding.