This paper presents the estimation of the International Roughness Index (IRI) in construction work zones, focusing on the NH 67 highway during its four-laning. It employs a neural network model using the Levenberg-Marquardt back-propagation algorithm to predict IRI from the Pavement Condition Index (PCI) based on data collected from various test sections. The model showed a high correlation between predicted and actual IRI values, highlighting its effectiveness for assessing road conditions and managing maintenance in work zones.

1. Full Paper
ACEE Int. J. on Civil and Environmental Engineering, Vol. 2, No. 1, Aug 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
1
vidyarajesh123@gmail.com,2moses@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).
The objective of this study is to estimate IRI from PCI
(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.
I n d e x Te r m s — R o u g h n e s s , I n t e r n a t i o n a l R o u g h n e s s
IndexConstruction 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.
© 2013 ACEE
DOI: 01.IJCEE.2.1. 22
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.
Fig 1. Images depicting detoriated pavement conditions
of construction work zones along NH– 67 of Trichy –Tanjavur
section
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2. Full Paper
ACEE Int. J. on Civil and Environmental Engineering, Vol. 2, No. 1, Aug 2013
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).
Fig 2. IRI determination along construction work zones of NH 67
stretch using MERLIN
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
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 stretchis calculated asPCI = 100 – CDVwhere, CDV = Corrected or
Normalised Deduct Value (Not exceeding 100). The curves
for determination of corrected deduct values depending on
the number andcombination of distress parameters.Pavement
Condition details along NH 67 Stretch of Trichy–Tanjavur
Section is given in Table I.
Fig 3. Methodology Flowchart
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).
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 4. Schematic Representation of neural network
IV. NEURAL NETWORK MODEL FOR ESTIMATION OF IRI
V. RESULTS AND DISCUSSION
The schematic representation of the network with three
inputs, 1 hidden layer with 4 neurons and 1 output layer is
IRI were found manually for 43 test sections. PCI of these
sections were also calculated using the variables namely
© 2013 ACEE
DOI: 01.IJCEE.2.1.22
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ACEE Int. J. on Civil and Environmental Engineering, Vol. 2, No. 1, Aug 2013
TABLE I. PAVEMENT
© 2013 ACEE
DOI: 01.IJCEE.2.1.22
CONDITION DETAILS ALONG
NH 67
3
STRETCH OF
TRICHY TANJAVUR
SECTION

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ACEE Int. J. on Civil and Environmental Engineering, Vol. 2, No. 1, Aug 2013
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)
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.
TABLE II.PERFORMANCE INDICES OF
Fig 6. Scatter plot of predicted and Actual IRI
The null hypothesis cannot be rejected as t statwere lesser
than tcritical value which indicates that the Predicted IRI are
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.
THE MODEL
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
satisfactoryand it is feasible for IRI prediction. Higherprecision
can be obtained with large database and with more Input
variables.
REFERENCES
Fig 5. Coefficient of correlation for the training
data and testing data
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Engineering, vol. 112(4), pp. 381–399, 1985.
[2] M.W. Sayers, D. T. Gillespie, and W. D. O. Paterson,
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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 0.05significancelevel.The
result of the t-test is summarized in Table III.
TABLE III. RESULTS OF STATISTICAL -TEST FOR PREDICTED AND ACTUAL IRI
© 2013 ACEE
DOI: 01.IJCEE.2.1.22
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ACEE Int. J. on Civil and Environmental Engineering, Vol. 2, No. 1, Aug 2013
Record, Journal of the Transportation Research Board, No.
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[5] S. A. Dewan, and R. E. Smith, “Estimating International
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Vehicle Operating Costs for the San Francisco Bay Area,”
Transportation Research Record, Journal of the
Transportation Research Board, No. 1816, TRB, National
Research Council, Washington, D.C , 2002.
© 2013 ACEE
DOI: 01.IJCEE.2.1.22
[6] Jyh-Dong Lin, Jyh-Tyng Yau, and Liang-Hao Hsiao, “Correlation
Analysis Between International Roughness Index (IRI) and
Pavement Distress by Neural Network,” 82th Annual Meeting
of the Transportation Research Board, Washington, D.C,
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[7] M.S Amarnath, Vivian Robert, L.Udaya kumar , “Arterial
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