Development of Performance Models for a Typical Flexible Road Pavement in Nigeria

Abstract

The results of a study conducted to facilitate the development of road pavement performance models that are appropriate for Nigeria and similar developing countries and could predict the rate of deterioration over their lifespan have been presented. Comprehensive investigations were carried out on the expressway linking Lagos (the economic nerve centre 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, roughness index, rainfall and temperature, were collected. Models were developed to determine Pavement Condition Score (PCS) and International Roughness Index (IRI). Stepwise Regression was used to analyse the data and quantify the impact of key input parameters on the PCS and IRI. Parameters such as depth of ruts and area of pot holes were found to be statistically significant in predicting PCS while number of patches, length of longitudinal cracks and depth of ruts were statistically significant in predicting IRI. The models can be used for planning road maintenance programs, thus minimizing the need for comprehensive data collection on pavement condition before the maintenance exercise, which is costly and time consuming.

Development of Pavement Roughness Prediction Model for National Highways in Sri Lanka

“…The climate characteristics of temperature and air humidity may increase pavement deterioration since temperature increases will increase the oxidation rate of bituminous binder in asphalt layers by decreasing the rigidness [15]. Further precipitation, moisture deficit, evapotranspiration and soil water storage influence roughness [13,16]. The subgrade soil type has a direct relationship to the pavement deterioration rate.…”

Geotechnical Assessment of Pavement Failure along Lagos-Ibadan Expressway

“…The existing road is paved double carriageway, comprising 7.3 m paved portion and 0.8 -1.20 m hard (simple superficial treatment) shoulders on both sides of the way with an average median width which varies from of 2.7 m to 11.0 m (Odunfa, 2014). The pavement structure which consists of 150 mm stone base course and 50 mm asphaltic concrete wearing course at inception was strengthened with additional overlays of asphaltic concrete overtime thereby raising the carriageway up to 125 mm above the shoulder (Owolabi and Abiola, 2011).…”

“…Road is one of the infrastructural facilities that is in total collapse (Kekere et al, 2012). Most of these roads fail shortly after construction and are characterized by cracks, ruts, depressions and potholes and many of them have become sources of economic drain (Abiola et al, 2010;Owolabi and Abiola, 2011) and affected many daily activities and other interactions. Commercial activities are suffering and in disorder due to insufficient supply of goods and services which increase price of common consumer items (Dawodu, 2006).…”

Pavement surface condition estimation based on geospatial modelling

“…Several studies (e.g. Lou et al 2001;Terzi 2007;Owolabi, Sadiq, and Abiola 2012) applied modelling techniques (neural network model, linear regression model, fuzzy system model, artificial intelligence neural network model and cluster-based model) to predict roadway pavement surface distress condition. However, most of these studies concentrate on predicting pavement surface distress condition without considering geospatial data.…”