Roughness is good evaluator of performance of road. This paper presents a case study of IRI (International Roughness Index) estimation at NH 67 during four laning of Trichy - Tanjavur section. An attempt has been made to evaluate the IRI of construction work zones using Levenberg- Marquardt back-propagation training algorithm. A MATLAB based model is developed, and the data from the case study are used to train and test the developed model to predict IRI. The models’ performances are evaluated through Correlation coefficient (R2) and Mean Square Error (MSE).

1. Full Paper
ACEE Int. J. on Transportation and Urban Development, Vol. 3, No. 1, October 2013
Estimation of IRI from PCI in Construction Work
Zones
R.Vidya1, Dr. S. Moses Santhakumar2, Dr.Samson Mathew3
National Institute of Technology/ Department of Civil Engineering, Tiruchirappalli, India
Email: 1 vidyarajesh123@gmail.com, 2 moses@nitt.edu, 3 sams@nitt.edu
Abstract— Roughness is good evaluator of performance of road.
This paper presents a case study of IRI (International
Roughness Index) estimation at NH 67 during four laning of
Trichy - Tanjavur section. An attempt has been made to
evaluate the IRI of construction work zones using LevenbergMarquardt back-propagation training algorithm. A MATLAB
based model is developed, and the data from the case study are
used to train and test the developed model to predict IRI. The
models’ performances are evaluated through Correlation
coefficient (R2) and Mean Square Error (MSE).
Index Terms—Roughness, International Roughness Index
Construction Work Zones.
I. INTRODUCTION
Roughness is defined as the deviation of a surface from a
true planar surface with characteristics dimensions that affect
vehicle dynamics and ride quality (ASTM Specification E8672A). Many indices are developed for quantification of road
roughness. Some widely used indices include International
Roughness Index (IRI), Ride Number (RN), Profile Index (PI)
etc. The International Roughness Index (IRI) was established
in 1986 by the World Bank. It was first introduced in the
International Road Roughness Experiment (IRRE) that was
held in Brazil. The IRI is internationally accepted standard
for calibration of roughness measuring instruments. The IRI
is based on simulation of the roughness response of a car
travelling at 80 km/h which expresses a ratio of the
accumulated suspension motion of a vehicle, divided by the
distance travelled during the test IRI and RN are commonly
used because of their stability and reproducibility [2].
Artificial neural networks, Genetic programming and Fuzzy
techniques have great variety of applications in
Transportation engineering and are capable of modeling
uncertain relationships. Numerous researches have been
conducted to evaluate pavement condition. Rada [1]
proposed a life cycle cost model and a cost effectiveness
method for project level pavement management.
Mactutis [4] et al had done investigations on the
relationship between IRI, rutting and cracking using large
database. Dewan and Smith [5] had derived a linear
relationship between IRI and pavement condition based on
39 observations. Lin et al [6] had analyzed the relationships
between IRI and pavement distress based on a backpropagation neural network methodology. Yousefzadeh et al
[8] had discussed the capability of using neural networks for
road profile estimation using neural networks.
The objective of this study is to estimate IRI from PCI
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© 2013 ACEE
DOI: 01.IJTUD.3.1. 22
(Pavement Condition Index) for construction work zones
using neural network modeling. The predicted values are
compared with actual IRI values measured using MERLIN
(Machine for Evaluating Roughness using Low-cost
INstrumentation) along the construction work zones. Since
poor pavement condition increases vehicle operating costs,
accident costs and delay costs of the users, it is necessary to
have certain guidelines for contracting work zones to calculate
the cost incurred during reconstruction. Under the existing
method of reconstruction, the traffic is invariably diverted
over detoriated pavement segments and shoulders which
increases the vehicle operating cost and reduces safety of
the road users. The Management strategies of the
construction work zones can be strengthened to ensure safety
and comfort for which pay index can be formulated with help
of IRI to assess the detoriated Pavement condition.
II. STUDY AREA AND DATA COLLECTION
The Thanjavur (Ch 80+000) to Trichy (Ch 136+490) section
of the National Highway 67 is of 56.490 Km length and
connects the two districts Tiruchirappalli and Thanjavur. The
highway serves the people of industrial area, educational
institution located thereon. This section contains two railway
crossings, one major by pass at Vallam and around 30 other
small bridges and culverts. Various test sites for the survey
location with the bad pavement conditions were selected
and data were collected on those sites (Fig. 1). Measurements
of longitudinal profiles were conducted along different
sections in the four laning of Trichy – Tanjavur section of
the NH – 67 using MERLIN, IRI were determined from the
collected data.
Insight into pavement roughness definition gives the idea
that longitudinal profile measurement in the wheel path yields
better roughness evaluation but it is possible to take many
profiles along a line, hence well-defined path encountered
by most of the vehicles should be traced, but the issue is that
how best sample can be taken, therefore roughness is
calculated by taking average of all single lane paths, with IRI
obtained separately in each wheel path at intervals of 0.5 m
(Fig. 2).
The road roughness can be determined using the equation: IRI = 0.593+0.0471D (2.4 <IRI< 15.9) where IRI is the
roughness in terms of the International Roughness Index (m/
km) and D is measured from the Merlin chart. Indian Road
Congress, SP 16 gives the norms for Roughness for Indian
conditions. Cracking, Rutting and potholing are the three

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ACEE Int. J. on Transportation and Urban Development, Vol. 3, No. 1, October 2013
III. METHODOLOGY
A. Model Development
Input variables of the model include cracking, potholes
and rutting and the output is IRI. The Input and Output
values were normalized. Levenberg-Marquardt backpropagation training algorithm was used. The methodology
of the model development is as shown in Fig.3
Fig. 1 Images depicting detoriated pavement conditions
of construction work zones along NH – 67 of Trichy – Tanjavur
section
Fig. 3 Methodology Flowchart
IV. NEURAL NETWORK MODEL FOR ESTIMATION OF IRI
The schematic representation of the network with three
inputs, 1 hidden layer with 4 neurons and 1 output layer is
shown in Fig. 4. The Sigmoid transfer function is chosen.
Various network combinations were tried and numbers of trial
runs were performed. Different number of hidden layer
neurons and learning rate 0.001, 0.01 and 0.05 are examined
and it is trained by Levenberg-Marquardt back-propagation
training algorithm, of which 0.001 was found to be optimum
for 1000 epochs. The output layer has only one node
representing the IRI. The performance of the networks 3-4-1
is reasonably good. The correlation coefficient, mean square
error and mean absolute error are determined. The model were
developed using MATLAB software (The Math works Inc,
2008).
Fig. 2 IRI determination along construction work zones of NH 67
stretch using MERLIN
parameters included in the study of pavement distress. The
distress survey for the pavement condition was done by visual rating as proposed by Amarnath et al. [7]. Deduct value
is defined as the value that represents the condition of the
existing pavement. A series of curves have been developed
for correcting the Total Deduct Value (TDV) .The Pavement
Condition Index (PCI) for each section of road stretch is calculated as PCI = 100 – CDV where, CDV = Corrected or
Normalised Deduct Value (Not exceeding 100). The curves
for determination of corrected deduct values depending on
the number and combination of distress parameters.Pavement
Condition details along NH 67 Stretch of Trichy – Tanjavur
Section is given in Table I.
Fig. 4 Schematic Representation of neural network
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ACEE Int. J. on Transportation and Urban Development, Vol. 3, No. 1, October 2013
TABLE I. PAVEMENT CONDITION D ETAILS ALONG NH 67 STRETCH OF TRICHY TANJAVUR SECTION
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ACEE Int. J. on Transportation and Urban Development, Vol. 3, No. 1, October 2013
V. RESULTS AND DISCUSSION
IRI were found manually for 43 test sections. PCI of these
sections were also calculated using the variables namely
cracking, rutting and potholes by visual rating. The samples
of IRI collected in construction work zones were divided into
two parts, one for training and another for testing. Out of 43
samples, 70% of the samples were taken as training set and
the remaining 30% were used for testing. The performance
during training and testing is evaluated by performance
indices such as Root Mean Square Error (RMSE), Mean
Absolute Error (MAE).They are computed as shown in (1)
and (2)
(1)
Fig. 6 Scatter plot of predicted and Actual IRI
TABLE III. RESULTS O F STATISTICAL -T EST FOR PREDICTED AND ACTUAL I RI
(2)
Method
Predicted IRI
Actual IRI
where N is the number of observations, and Xi are the IRI
Values.
The performance of the model with their indices is shown
in Table II with RMSE, MAE and R2 statistics. Coefficient of
correlation for the training data of 0.94 and testing data of
0.88 was obtained as shown in Fig. 5. From the performance
indices, it is evident that the model developed is reasonably
good and it shows a good correlation between Predicted and
actual IRI as shown in Fig. 6.
Value
RMSE
MAE
R2
Variance
1.37
1.38
tstat
1.31
tcritical
1.81
not significantly different from the actual one found by field
surveys.
The Model estimates IRI which is difficult to obtain from
the field measurements in construction work sites These road
segments during construction incur additional vehicle
operating costs, due to pavement distress thereby increasing
the road users costs. The predicted IRI from the model created
exclusively for work zones can be used to find the monetary
value of costs as well as the pay adjustment factor which will
serve as condition evaluator and will help in effective
management of pavement maintenance.
TABLE II . PERFORMANCE INDICES O F T HE MODEL
Performance Indices
Mean
8.48
8.30
0.0041
0.977
0.86
VI. CONCLUSIONS
In this study, the potential of artificial neural network
methods, Levenberg-Marquardt back-propagation is
employed for the estimation of IRI from pavement condition
for effective management of construction work zones. The
developed model were trained and tested and the statistical
criteria of performance evaluation were calculated. The model
yields an R2 value of 0.86 and MSE of 0.041.The results
indicate that the performance of neural network is satisfactory
and it is feasible for IRI prediction. Higher precision can be
obtained with large database and with more Input variables.
REFERENCES
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Fig. 5 Coefficient of correlation for the training data and testing
data
The accuracy of the system proposed is determined by ttest. This is done to validate to show the closeness between
actual and Predicted IRI values at 0.05 significance level. The
result of the t-test is summarized in Table III.
The null hypothesis cannot be rejected as t stat were lesser
than tcritical value which indicates that the Predicted IRI are
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ACEE Int. J. on Transportation and Urban Development, Vol. 3, No. 1, October 2013
[6] Jyh-Dong Lin, Jyh-Tyng Yau, and Liang-Hao Hsiao,
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