DEVELOPMENT OF RELATIONSHIPS BETWEEN PCI, RESILIENT MODULUS AND IRI USING LTPP DATABASE AND THEIR VALIDATION ON M2

Abstract

The pavement performance is the ability of the pavement to serve the traffic over a period of time. And Pavement performance evaluation is determination of performance using parameters like International Roughness Index (IRI), Pavement Condition Index (PCI), and rutting etc. Many researchers have been developing relationships between pavement distresses and indices. They have used regression analysis, Multivariate Adaptive Regression Splines (MARS), and Artificial Neural Network (ANN). Among the variables used by the researchers, IRI is most commonly used and termed as best characteristic reflecting the pavement distresses and corresponding performance. This research aims to examine and develop relationships between PCI, IRI and FWD back calculated Resilient Modulus (Mr) under temperature and climatic constraints using long term pavement performance (LTPP) database. The relationships developed will be validated on Pakistani motorway M2. The various tests, IRI, and FWD are performed by NHA on Motorway M2 in 2014 and their data are retrieved from the design report of 2014 for this research. Data of 193 sections belonging to wet, no freeze and dry, no freeze regions are obtained from LTPP database. These sections are located at the regions where average annual temperature ranges from 20o C to 30o C. Relationships between IRI, PCI, and back calculated resilient modulus (Mr) of three typical layers of the pavement are examined and developed. Multiple Linear Regression technique is used to develop the relationships. The relationships are assessed with the help of p-value and R2 values. P-value of all three equations is less than 0.05 which makes the equations “significant”. PCI is found to be dependent variable whereas IRI and Mr are independent variables while x examining and developing relationships among these three variables. Furthermore, model validation between estimated PCI and actual PCI for three layers; asphalt layer, base course layer and sub grade layer of motorway M2 presented a better R2 of 0.68, 0.61 and 0.68 respectively indicating a good model. It is concluded that a relationship between the back calculated Mr, IRI, and PCI exists. This research may also help decision makers to predict the resilient modulus and/or determine PCI for the highways and motorways on network level and carryout necessary decisions regarding prioritizing highways/motorways for funding, pavement service life, surface treatments, and pavement rehabilitation etc.