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
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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.
