Incremental Nonlinear Model for Predicting Pavement
Serviceability
Jorge A. Prozzi
1
and Samer M. Madanat
2
Abstract: A recursive nonlinear model was developed for the prediction of pavement performance as a function of traffic characteristics,
pavement structural properties, and environmental conditions. The model highlights some of the advantages of relaxing the linear
restriction that is usually placed on the specification form of pavement performance models. First, a functional form that better represents
the physical deterioration process can be used. Second, the estimated parameters are unbiased, owing to a proper specification and the use
of sound statistical techniques. Finally, the standard error of the prediction is reduced by half that of the equivalent existing linear model.
This improved accuracy has important economic implications in the context of pavement management. The model developed as part of
this research enables the determination of an unbiased exponent of the so-called power law and of the equivalent loads for different axle
configurations. The estimated exponent confirms the value of 4.2 traditionally used. However, it should be noted that this exponent is only
to be used for determining damage in terms of serviceability. On the other hand, equivalent loads estimated for different axle configura-
tions tend to differ from traditionally used values.
DOI: 10.1061/ASCE0733-947X2003129:6635
CE Database subject headings: Pavements; Serviceability; Models; Cost allocations.
Introduction
The objective of this research was the development of sound flex-
ible pavement performance models to be used primarily for the
management of the road infrastructure because accurate predic-
tion of pavement performance is important for effective manage-
ment of the infrastructure.
Owing to the great complexity of the road deterioration pro-
cess, performance models are, at best, only approximate predic-
tors of expected conditions. Models that only produce a determin-
istic prediction of performance without any quantification of the
accuracy of the prediction are unrealistic; hence, an estimation of
the prediction error is essential. The absence of such an estimate
imposes important limitations on the applicability of the perfor-
mance prediction model.
The Present Serviceability Index PSI was developed in the
early 1960s and constituted the first comprehensive effort to es-
tablish performance standards based upon considerations of riding
quality Carey and Irick 1960; Highway Research Board HRB
1962. A panel of highway users from different backgrounds
evaluated seventy-four flexible pavement sections and rated them
on a discrete five-point scale 0 for poor, 5 for excellent. This
experiment gave origin to the Present Serviceability Rating
PSR. The PSR was found to correlate highly with surface
roughness and, to a lesser extent, with rutting, cracking, and
patching.
Models for the prediction of riding quality are very important
to highway agencies for the purpose of managing their road net-
work. The prediction of riding quality is also important for road
pricing and regulation studies. Both the rate of riding quality de-
terioration over time and the contribution of the various factors to
such deterioration are important inputs to these studies. Useful
models are those that establish the contribution of pavement
structure, traffic, environment and any other factors that are rel-
evant for cost allocation.
Data Considerations
There are a number of possible data sources that have been used
for the development of pavement performance models. Some of
these sources are: 1 randomly selected in-service pavement sec-
tions; 2 in-service pavement sections selected according to an
experimental design; 3 purposely built pavement test sections
subjected to the action of actual traffic and the environment; and
4 purposely built pavement test sections subjected to the accel-
erated action of traffic and environmental conditions.
Data originating from in-service pavement sections subjected
to the combined actions of highway traffic and environmental
conditions are those that most closely represent the actual dete-
rioration process of pavements in the field. All other data sources
would produce models that would suffer from some kind of bias
or restrictions unless special considerations are taken into account
during the parameter estimation process. Some of these consider-
ations are briefly described in the following paragraphs.
The most common problems encountered in models developed
from randomly selected in-service pavement sections are caused
by unobserved events typical of such data sets, and by the prob-
1
Assistant Professor, Dept. of Civil Engineering, The Univ. of Texas,
ECJ 6.8, Austin, TX 78712. corresponding author. E-mail:
prozzi@mail.utexas.edu
2
Professor, Dept. of Civil and Environmental Engineering, Univ. of
California, 114 McLaughlin, Berkeley, CA 94720. E-mail:
madanat@ce.berkeley.edu
Note. Discussion open until April 1, 2004. Separate discussions must
be submitted for individual papers. To extend the closing date by one
month, a written request must be filed with the ASCE Managing Editor.
The manuscript for this paper was submitted for review and possible
publication on November 26, 2001; approved on December 20, 2002.
This paper is part of the Journal of Transportation Engineering, Vol.
129, No. 6, November 1, 2003. ©ASCE, ISSN 0733-947X/2003/6-
635– 641/$18.00.
JOURNAL OF TRANSPORTATION ENGINEERING © ASCE / NOVEMBER/DECEMBER 2003 / 635