Paper ICMPA093 8th International Conference on Managing Pavement Assets DEVELOPMENT OF PRIORITY INDEX ASSESSMENT MODEL FOR ROAD

Against the background that regular maintenance is necessary to preserve highway investments, reduce users ' cost and cost of goods and services, this paper presents the results of a research aimed at developing a priority assessment index for pavement management system (PMS) in Nigeria using artificial neural network (ANN) and linear regression. As a key component of PMS, priority assessment index plays a crucial role where forecast results provide a basis for prioritizing highway pavement maintenance. Comprehensive investigations were carried out on the expressway linking Lagos (the economic nerve of Nigeria) with Ibadan (the largest city in West Africa)- apparently one of the most heavily trafficked roads in the country. Data relating to traffic characteristics, pavement condition ratings, distress types, pavement thickness and roughness index, were collected. Of these variables, only three were found to be statistically significant namely: traffic volume, pavement condition score and roughness index. These variables were combined into a convenient scale to get a single priority assessment index. The R 2 values of 0.95 and 0.98 show that both ANN model and linear regression can be used satisfactorily to prioritize highway pavement maintenance. The results of this research will provide a method of effectively managing road pavements in Nigeria and other developing countries of the world with similar climatic, soil and traffic conditions