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ANALYSIS OF PAVEMENT ROUGHNESS FOR THE AASHTO DESIGN METHOD IN PART OF BAGHDAD CITY

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ANALYSIS OF  PAVEMENT ROUGHNESS FOR THE AASHTO DESIGN METHOD IN PART OF BAGHDAD CITY Powered By Docstoc
					                                                (Manuscript No: I12528-05)
                                         April 15, 2012 / Accepted: April 21, 2012




ANALYSIS OF PAVEMENT ROUGHNESS
FOR THE AASHTO DESIGN METHOD IN
     PART OF BAGHDAD CITY
                                                 Siham E. Salih *
              Lecturer (Dep.of H.W.Y.‟s &Transportation Eng. - University of Al-Mustansiriyah)
                                    (Email: shohanaiffat@yahoo.com)

Abstract - Road roughness is a major factor in evaluating the condition of a highway pavement section because
of its effects on ride quality for road users and vehicle operating costs. The objective of the present study is to
develop the prediction model for international roughness index (IRI) for flexible pavement in part of Baghdad
city. The measures to predict model were used serviceability (Present Serviceability Index, PSI), that include
pavement deteriorations, a (150) selected pavement sections in many sites in the study area .The pavements
were rated to measure the required data for (IRI) model building requirements. These data include: Present
Serviceability Index (PSI), cracking, patching, and rutting and slope variance. Serviceability is an indicator that
represents the level of service a pavement provides to the users. This subjective opinion is closely related to
objective aspects, which can be measured on the pavement‟s surface. This research aims specifically at relating
serviceability results obtained by a 9-member evaluation panel, representing the general public as closely as
possible, to parameters (particularly of roughness) measured with instruments on 50, and 100 road sections of
asphalt concrete and Portland cement concrete, respectively. Results show that prediction of serviceability is
quite accurate based on roughness evaluation, while also revealing that, by comparison to studies in more
developed countries, this study are seemingly more tolerant, it is assign a somewhat higher rating to ride
quality. Furthermore, visible distress does not have a significant influence on serviceability values in this study .

Keywords - International Roughness Index, Flexible pavement, Rigid pavement, Present Serviceability Index ;
Longitudinal and Transverse Cracking , Rut Depth , Patching, prediction of pavement condition.

                                                INTROUDECTION

Highway agencies use pavement roughness to monitor the condition and performance of their road networks due
to its effects con ride quality and vehicle operation costs. Pavement roughness can be defined as irregularities in
the pavement surface that adversely affect the ride equality of a vehicle"(Kasibati and Al-Mahmood, 2002)". In
its broadest sense, road roughness has been defined as "the deviations of a surface from a true planer surface
with characteristic dimensions that affect vehicle dynamics, ride quality, dynamics loads, and drainage"[Sayers,
1985]. Despite this broad description, the practice today is to limit the measurement of roughness qualities to
those related to the longitudinal profile of the road surface which cause vibrations in road-using vehicles. Road
roughness can also be defined as "the distortion of the road surface that imparts undesirable vertical
accelerations and forces to the vehicle or to the riders and thus contributes to an undesirable, uneconomical,
unsafe, or uncomfortable ride" (Hudson, 1981).
In general, road roughness can be caused by any of the following factors (Yoder and Hampton, 1958):

    i.   Construction techniques which allow some variation from the design profile.
    ii.  Repeated loads, particularly in channelized areas, that can cause pavement distortion by plastic
         deformation in one or more of the pavement components.
    iii. Frost heave and volume changes due to shrinkage and swell of the subgrade.
    iv. Nonuniform initial compaction.

During the last three decades, several studies pointed out the major penalties of roughness to the user. In 1960,
Carey and Irick (1960) showed that the driver's opinion of the quality of serviceability provided by a pavement
surface is primarily influenced by roughness. Between 1971 and 1982, the World Bank supported several
research activities in Brazil, Kenya, the Caribbean, and India. The main purpose of these studies was to
investigate the relationship between road roughness and user costs. In 1980.


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International Journal Of Structronics & Mechatronics


Rizenbergs (1980) pointed to the following penalties associated with roughness: rider no acceptance and
discomfort, less safety, increased energy consumption, road-tire loading and damage, and vehicle deterioration.
Gillespie and Sayers (1981) examined the relationship between road roughness and vehicle ride to illustrate the
mechanisms involved and to reveal those aspects of road roughness that play the major role in determining the
public's perception of road serviceability. It has been widely suspected that the initial roughness of a pavement
section will affect its long-term performance. Recently, a study conducted by Janoff (1990) suggested that initial
pavement roughness measurements are highly correlated with roughness measurements made 8-10 years after
construction.

Due to the importance of pavement roughness, most highway agencies have established smoothness
specifications for new pavement construction. Smoothness specifications are normally written for the use of
profilographs. About half of the states require that a specific limit of smoothness be met, whereas the remainder
of the states are using a variable scale with pay adjustments, depending on the degree of the smoothness
achieved (Wood strom, 1990). These pay adjustment factors are made based on the assumption that lower initial
pavement roughness will result in better pavement performance.

                              SERVICEABILITY AND ROUGHNESS INDICES


    A. SERVICEABILITY INDEX

Pavement Serviceability represents the level of services that pavement structures offer users. This indicator first
appeared as a rating made by users with respect to the state of the road, particularly the road‟s surface. This
rating is represented by a subjective index called „Present Serviceability Rating‟ (PSR) and may be replaced by
an objective index called „Present Serviceability Index‟ (PSI). The latter index is determined on a strictly
objective basis by applying the users‟ rating scale to sections of roads featuring different states of distress. This
scale enables users to rate the pavement‟s state in terms of its service quality. The scale rates pavements from 0
to 5, from an extreme state of distress to a new or almost new pavement [Jorge alberto,2001]. Thus, a
quantitative relationship is established between this Serviceability rating and certain parameters that measure
physical distress of pavement surface.

                                                 Roughness Index

Roughness is defined as irregularities in pavement surface that adversely affect ride quality, safety, and vehicle
maintenance and operating costs. Roughness is the factor that most influences users‟ evaluation when rating ride
quality. One of the problems faced by technicians when rating ride quality and comfort for vehicle users and
comparing experiences among countries is the great diversity of techniques, equipment, and indicators available
in each country.

Consequently, there arose an international interest in developing a single and common index as reference. This
index had to be independent from equipment or techniques used to obtain the profile‟s geometry, and at the
same time had to represent the full range of users‟ perceptions when driving an average vehicle at an average
speed. The need for this index originated in the mid-eighties, giving rise to the concept, definition, and method
for calculating the International Roughness Index (IRI) [7, 8].

IRI is a statistical indicator of surface irregularity in road pavements. The real profile of a newly–built road
represents a state defined by its IRI with an approximate range of 1.0–2.5 (m/km). After the road is constructed,
pavement roughness varies as a function of traffic, gradually increasing the pavement IRI values (greater
irregularities).



                         Categories or Classes of Equipment for Measuring Roughness

The different evaluation methods available to measure surface roughness were grouped into four categories,
classified according to how directly their measurements came close to the IRI [7, 9]. These methods may be
summed up as follows: Class 1, Precision Profiles (which require the longitudinal profile of a rut to be measured
in a precise manner); Class 2, Other Methods for Profile Measuring (calculation of IRI is based on
measurements of the longitudinal profile, but is not as accurate as Class 1 measurement method); Class 3,


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Siham E. Salih


Estimations of IRI through Correlation (systems for measuring roughness by Profilometer, Rod &Level ); Class
4, Subjective Ratings and Un calibrated Measurements (devices with an un calibrated response, sensations of
comfort and safety which a person experiences when driving on a road).

                                 METHODOLOGY OF THE EXPERIMENT

In order to achieve the objectives proposed in this paper, it was first necessary to select a sufficient number of
pavement sections for study in Baghdad city, covering the range of possible conditions (good, fair, and poor).

Next, roughness of these sections had to be measured, first using Profilometer and then Rod &Level (Machine
for Evaluating Roughness using Low-cost Instrumentation). Also, surface integrity had to be established using
condition survey of the pavement. The last stage of data collection would involve evaluating serviceability by a
panel of people representative of habitual vehicle users. Figure 1 shows the principal stages of the methodology
of the experiment.




                                        Figure 1- Methodology of the Experiment




                                        Selection of Pavement Sections

Selection of pavement sections for the study had to be conducted by an objective process that would allow
discrimination among the different pavements to be studied. Therefore certain requirements, based mainly on
the feasibility of evaluating roughness and serviceability, were established [10]. These requirements were:
length, safety (number of lanes, vehicle flows and visibility), accessibility, possibility of measuring with
equipment. Pavement sections that met conditions as defined by the evaluation panel were selected from
different municipal districts.

                                   Relative Importance of each Requirement

To be able to discriminate when selecting the sections, each requirement had a different importance in the final
weighting. The percentage assigned to each weight depended mainly on the possibility of measuring the
sections‟ roughness with the available equipment and of the traffic modal composition, leaving safety at a
secondary level.


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International Journal Of Structronics & Mechatronics


The previously selected sections were then divided into three levels of serviceability, according to the scores
obtained by the work group: good, fair and bad. A weighting with the previous criteria was applied to each
section, and a list of sections arranged by each serviceability level and pavement type was obtained.

                                    Procedures Used to Measure Roughness


    A. ROD & LEVEL INSTRUMENT

Measures of surface elevation are obtained at constant intervals along a line on a traveled surface to define a
longitudinal profile. The line used for the profile is called a wheel track, a path followed by the tire of a road
vehicle. The measured numbers are recorded and entered into a computer for graphical display and analysis. The
profile points are used as input to a computational algorithm that produces a summary roughness index.

This method describes the use of conventional survey equipment comprising an optical level and graduated rod,
but it may also be applied to automated techniques (for example, laser-based systems) with appropriate
adjustments. At a minimum, two persons are required; one to locate and hold the rod (the rod-man), and a
second to read relative heights through the leveling instrument and record the readings. For better efficiency, it
is recommended that a third person record the readings to allow the instrument operator to concentrate on
adjusting and reading the instrument. When maximum measuring speed is desired, a fourth crew member is
recommended to act as relief.



    B. LASER PROFILER

A Two Laser Profiler (TLP) was used to measure the cross-section profile and calculate roughness (IRI) of the
project‟s sections [12]. It is a Class 1 type of equipment as it is able to obtain the profile with great precision,
which then allows the calculation.

To calculate IRI, the Laser Profiler‟s computer program has a profile processing module, which is independent
from the measurement and can be performed at any time after the profile has been measured. Only the
processing distance is needed, that is, the distance from which the program is to report the IRI. A distance of 10
meters was established as reasonable, because it allows one to recognize singularities and to obtain a sufficient
amount of IRI values from the section‟s 400 meters.

The profile processing yields a file text which may then be easily worked on with spreadsheets. Interesting
results that can be seen on the file, in the different columns, are: the distance traveled from the beginning of the
section, the IRI value of the left rut and the IRI value of the right rut [15].

    C. DISTRESS SURVEY M ETHODOLOGY

Existing levels of distress are a very important measurement of pavement sections‟ requirements. This
information is added to roughness data measured with Rod &Level and the Profilometer. There are different
types of deterioration and each type has different degrees of severity.

Every distress condition is the result of one or more factors, which when known give a very good diagnosis of
the pavement‟s “weaknesses”. Thus, a detailed distress survey of the pavement is one of the steps necessary to
establish pavement condition. In this research, the condition survey of the pavement consisted of detecting,
recording and quantifying the distress conditions that each section had at the moment of conducting the study.

There are several distress survey procedures [9], and it is felt that the most complete one, supported by years of
study and experience, is the procedure proposed by the Strategic Highway Research Program [13]. This is the
methodology used in this study.

                                           Evaluation of Serviceability

This section describes those planning aspects which are relevant for serviceability rating by the evaluation
panel.


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Siham E. Salih




    A. COMPOSITION OF THE PANEL

The people that made up the evaluation panel was one of the most important aspects in this study: (i) they had to
represent the public which generally circulates on the country‟s streets and roads; (ii) they needed a broad range
of experience both as drivers and passengers in cars, as well as passengers in public transportation buses; and
(iii) they should not have any kind of bias or prejudice regarding trips in cars and buses.

The size of the evaluation panel had to be defined so that it was administratively manageable while permitting
an adequate precision. The number of people needed to obtain a certain degree of certainty in the PSR, at a
given level of confidence, had been tabulated in previous studies [14].

As the group had to represent the general public as closely as possible, the panel was finally made up by 11 men
with different activities, obtaining 90% of level of confidence and an error of 0.5 in the PSR value (14).

                                           Design of the Rating Form

In this study, we adopted the widely used AASHTO scale. It consists of reporting in words the levels of quality,
in addition to a line where the person performing the rating makes a mark. The other evaluation category that
was used is the acceptance criteria. In it, the evaluator is asked to judge if ride quality on the section seems
acceptable so as to include it in: (a) expressways, and (b) initial streets .

The responses to this segment of the form provide a measure of the minimum acceptance threshold of functional
quality of pavements.It was important for the form to be simple, so that it enabled the evaluator to rapidly judge
and decide the serviceability rating as well as his position regarding the acceptance or not of that ride quality.
Figure 2 shows this form.




                                   Figure 2: Rating Form used by the Evaluation Panel

                               Training of the Members of the Evaluation Panel

Training of the evaluation panel and the instructions they would be given were very important aspects in the
process of subjective rating. Studies that show that a team rating a subjective variable without receiving any



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International Journal Of Structronics & Mechatronics


instructions obtains results that are significantly different to another team who has received instructions [15].
Besides, the variance in the team that receives instructions is much lower than in the other one [16].

Considering the above, careful instructions were developed for this study‟s evaluators. The instructions were
designed to be as simple as possible, but at the same time to have the sufficient level of detail to prevent any
kind of confusion as to procedures and definitions.The rating procedure was explained to all evaluators in a
training session. They were given instructions in writing which were discussed by the research team; also, all
panel members‟ questions were answered.

After the training session, evaluators were taken for a ride on some pavement sections featuring a broad range of
roughness. During the ride, evaluators were motivated to discuss the procedure both among themselves as well
as with those in charge. The purpose of this was to orient the evaluation panel so that they could perceive the
differences and acquire confidence with the procedure.

                                                          Evaluation Sessions

In order to prevent results from being influenced by changes in pavement characteristics (e.g. new distress or
possible rehabilitation), section evaluation by the panel in different vehicles must carried out over a brief period
of time. It is also important that evaluations in the same vehicle are not made very far apart in time, so that
variations in the mechanical response of the vehicle‟s body does not alter the results.

It must also be borne in mind that panel members‟ weariness and fatigue may alter their rating of a section. A
suggestion made in a prior study to have breaks every 1.5 or 2 hours was adopted in this study [15]. Based on
the above, daily evaluation sessions were from 9:00 a.m. to 5:30 p.m., with time off for lunch between 12:30
p.m. and 2:00 p.m., and rest periods halfway through the morning and afternoon. In each session, sections of
asphalt and concrete were included, so that each time the team covered a wide spectrum of roughness in circuits
that optimized driving time, while simultaneously avoiding any effects due to the evaluation‟s sequence. The
sections were evaluated at a constant speed of approximately 50 km/hour.

                                                         RESULTS OBTAINED


    A. RELATIONSHIP BETWEEN ROD &LEVEL                             AND THE    PROFILOMETER

Different adjustment curves were tested, using data obtained for IRI from measurements with Rod &Level and
from those obtained with the Profilometer. Considering the good adjustments obtained, the larger sample size
and for simplicity of future general treatment, the use of the linear equation (Equation 1) obtained by using data
for all types of pavements (concrete and asphalt ,) is recommended.

Concrete:

    1.   IRI PRO = 0.0171*( R&L) + 1.8227                                     R2 = 0.9169

                                           2.4
                                           2.3
                                           2.2                 R2 = 0.9169
                        IRI Profilometer




                                           2.1
                                            2
                                           1.9
                                           1.8
                                           1.7
                                           1.6
                                           1.5
                                                 0   5    10         15       20    25      30       35
                                                                    IRI Rod&Level



                       Figure 3: Relationship Proposed and Existing Relationships For Concrete Pavement



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Siham E. Salih


Asphalt

    2.    IRI PRO =0.4072* Rod&L + 7.7176                                                R2= 0.9297


                                    45
                                    40

                                    35
                 IRI Profilometer



                                    30

                                    25
                                                                R2 = 0.9297
                                    20

                                    15

                                    10

                                     5

                                     0
                                         0            10             20             30            40             50         60
                                                                          IRI RO d& Le ve l



                                         Figure 4: Relationship Proposed and Existing Relationships For Flexible Pavement

                                                                    Rating Serviceability

The evaluation panel had to drive on all road sections under study using passenger car types of vehicles. All
evaluations were performed by the same group of 11 evaluators, under the direction of the personnel responsible
for the study. The evaluation panel members were subjected to prior training and were asked to drive on some
test sections so they could be in a position to compare their opinions. Subsequently, during the evaluation
sessions, they rated their perception of the pavements on an individual and secret basis. Finally, it is worth
mentioning that all evaluations were performed within a time frame no longer than two weeks. The evaluators
were asked to indicate possible conditions of comfort and ride quality from very bad to very good.

This subjective rating was converted into a numerical value, assigning a score to each road section which could
range from 0 to 5. The average of the individual scores assigned by each evaluator for the same length of road is
the PSR of the road section. Thus, the panel evaluated a total of 50 road sections for rigid pavement and 108
road sections for flexible pavement. The results of the evaluation panel are shown in Table 1

                                                 Relationship between Roughness and Serviceability

  The regression between the panel values for PSR and IRI is called PSIROUGH.. Serviceability ratings are
available for two types of asphalts (AC, ACC) used in the study, and the vehicle used in mention is made of the
Serviceability relationship for cars, because it is the one most commonly used and the one established by the
AASHTO Test.

The best adjustments were obtained with square root and exponential models (nonlinear equations). In order to
use regression analysis to calibrate these equations, some transformations (such as log transformations) were
made to change the nonlinear relationship between PSIROUGH and IRI into a linear relationship. For flexible
pavements, Equations 3 and 4 were obtained and Figure 5 shows the representative graph of the regression for
these pavements.

Concrete:


    3.    PSIROUGH = -6.785Ln(                           IRI ) + 10.336                  R2 = 0.882



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International Journal Of Structronics & Mechatronics


                                                        IRI
    4.     PSIROUGH = 82.962e-1.1669                                             R2 = 0.9232


                                       6

                                       5

                                       4
                P,Serviceability



                                       3                                                                Series1
                                       2                                                                Log. (Series1)
                                                    2                                  2
                                       1        R = 0.8823                            R = 0.9231        Expon. (Series1)

                                       0
                                           0            2                        4                6
                                   -1

                                   -2
                                                                  IRI m/km


                                               Figure5: PSIROUGH Regression in Concrete Pavements

In an analogous manner, Equations 5 and 6 were obtained for Asphalt pavements:

Asphalt:


    5.     PSIROUGH = -6.8101 Ln                        IRI + 10.737                       R2 = 0.885




                                                            IRI
    6.     PSIROUGH = 144.31 e-1.2897                                            R2 = 0.8921

In Figure 6, the regressions shown previously may be observed


               6

               5

               4
                                                                                       R2 = 0.8921
               3
                                                                                                        Series1
               2                                                                                        Expon. (Series1)
                                               R2 = 0.885                                               Log. (Series1)
               1

               0
                                   0            2                            4                   6
               -1

               -2


                                               Figure 6: PSIROUGH Regression in Flexible Pavements




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Siham E. Salih


Although the exponential and LOG equations are very similar, in this study we have preferred LOG regressions
to predict the values of PSR for two reasons: (a) they have a higher coefficient of determination; and (b) for low
IRI values they predict a higher Serviceability. The position of the adjustment curves between the types of
pavement is set forth in Figure 7. It can be seen that for the same IRI value, a concrete pavement is rated better
than an asphalt pavement and that the difference between them is greater as roughness increases.


                                   6

                                   5
               Serviceability ,P




                                   4
                                                                                                                            Asphalt
                                   3
                                                                                                                            Concrete
                                   2                                     R2 = 0.8831

                                   1
                                        R2 = 0.8153
                                   0
                                       2.4               3.4                4.4                5.4                6.4
                                                                        IRI m/km


                                         Figure 7: Comparison of PSIROUGH Regressions between Asphalt and Concrete

                                                   Effects of Other Pavement Distress on Serviceability

The equations of serviceability developed by AASHTO to predict the PSR [6] include slope variance and other
pavement distresses like surface rutting, cracking, and patching. All these distresses had been measured in this
research on a condition survey of the pavement. In order to determine if some types of distress had an effect on
Serviceability for this study , it was necessary to consider the results of the condition survey incorporating those
distresses that could contribute to the regression, and finally, prove its significance in the model. Tables 2 and 3
show the different models, the explanatory variables, and the t-statistic. Interestingly, surface rutting (RD),
cracks (C), and patching (P) are not significant in determining Serviceability. However, the IRI is always
significant. This means that predict model for this study the surface distresses are not significant in determining
Serviceability compared to IRI.

Model                                              Variable       Regressor         R2           SEM             T-Statistic     T     Critical
                                                                                                                                 (95%)

         IRI                                       a              5.189560          0.6044       0.76074         29.1426
a+b*
                                                   b              -0.92057                                       -13.5991

                     IRI                           A              6.65957           0.45         0.41362         5.39982
a   +   b                                    +c*
    CP                                            b              -1.08183                                       -1.25120

                                                   c              -0.15178                                       -6.41522


                                         Table 2: Effects of Other Pavement Distress on Serviceability in Concrete Pavements.

            IRI: International Roughness Index (m/km); C: cracks (m2/1000m2) P: patch (m2/1000m2)




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International Journal Of Structronics & Mechatronics



                                                                                                              T
Model                                  Variable        Regressor        R2        SEM         T-Statistic     Crit(95
                                                                                                              %)

a + b *RD2                             a               4.81641          0.666     0.80430     27.1561

                                       b               -2.70457                               -14.5627

                                       A               7.13651          0.865     0.51179     32.7820
a+b       IRI +c*RD2
                                       b               -0.87602                               -12.4630

                                       c               -2.19867                               -17.5961


a+b       IRI +c* C  P                A               6.94556          0.61      0.87644     18.5390

                                       b               -1.28535                               -11.2899

                                       c               -0.00710                               -6.0859


a+    IRI +c*RD2+d C  P               A               6.55689          0.5477    0.9354      17.1449

                                       b               -1.16816                               -10.0822

                                       C               -0.00337                               -1.1842

                                       d               -0.00337                               -1.1842


               Table 3: Effects of Other Pavement Distress on Serviceability in Asphalt Pavements.

             RD: rutting (inches); IRI: International Roughness Index (m/km); C: cracks (m/1000m2)

                                             P: patch (m2/1000m2)

                                Application of the AASHTO Method of Design

Serviceability ratings performed by the evaluation panel of Iraqian users were much higher than in other similar
studies. These higher ratings generate the problem that final Serviceability is also higher, whereby the loss in
Serviceability is lower. Therefore, these Serviceability results according to Iraqian users are not recommended
for use in the AASHTO design method. In order to solve this problem, the PSI was calculated by resorting to
procedures recommended by AASHTO, using information such as slope variance, rutting, cracked surface and
potholed surface. Applying these procedures to data obtained during the study, the PSIAASHTO was then
calculated for each one of the road sections analyzed and compared to the corresponding IRI for each road
section. The equations of the IRI-p relationships obtained with the above information may be used in designing
pavements according to AASHTO (Equations 7 and 8):

Concrete:

     7.    IRI PSIAASHTO = -1.1406Ln(IRI) + 3.9946                       R2 = 0.943

Asphalt:

     8.    IRI PSIAASHTO = -1.343Ln(IRI) + 4.1807                 R2 = 0.9622




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Siham E. Salih




The comparison of these new design curves developed by other studies [2– 5] is more favorable, because the
Serviceability value which they predict is quite similar. Figure 12 shows this comparison in the case of asphalt,
and Figure 8 for concrete.

Final Serviceability values recommended by this study originate from the evaluation panel‟s results, but must be
read in terms of the PSIAASHTO curve, because it is this curve which generates the values to be used for design
purposes.




                                6

                                5                                                                                              IRI PSI AASHTO
                                                   R2 = 0.9019
     Serviceability ;P




                                4                                                                                              IRI_PSI

                                3
                                                                        R2 = 0.943                                             Log. (IRI PSI
                                2                                                                                              AASHTO)
                                                                                                                               Log. (IRI_PSI)
                                1

                                0
                                            0         2             4            6             8            10
                                                                         IRI


                                                   Figure 8 - Comparison of IRI-PSIAASHTO curve with previous studies for Concrete




                                            7
                                            6                                                                                IRI PSI AASHTO
                                                     R2 = 0.8852
                                            5
                                                                                                                             IRI _PSI
                         Serviceability;P




                                            4
                                                                                                                             Log. (IRI PSI
                                            3
                                                                                                                             AASHTO)
                                            2                    R2 = 0.9622                                                 Log. (IRI _PSI)
                                            1
                                            0
                                            -1 0                   10                     20                      30

                                            -2
                                                                               IRI


                                                    Figure 9 - Comparison of IRI-PSIAASHTO curve with previous studies for Asphalt




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International Journal Of Structronics & Mechatronics


                DEVELOPMENT OF MODEL FOR THE PREDECTION OF INTERNATIONAL
                                   ROUGHNESS INDEX

The statistical techniques used for the models development required for evaluation of the pavement
serviceability and performance of the selected roads in the study area. A suitable amount of data representing
many variables is presented in this investigation. For the purpose of model development of the present
serviceability index, the data include; patching, cracking, slope variance and rutting. The choice of sample size
is presented in the following paragraph.

       1.   Selecting Sample Size

The following formula is used to determine the required sample size. [17],

E =V t / (n) 0.5

V=S/ X

Where

E = Error of the mean,

V = Coefficient of Variation,

T = t – statistics,

n = Sample Size,

S = Standard deviation, and;

X = Sample Mean.

N = 80

For confidence level = 95%, df = 79, then t = 1.99

E = (1.107029* 1.99) / (80) 0.5

E = 0.2485

Then, the sample size is accepted with this percent of error.

       2.   THE M ODELS DEVELOPMENT PROCESS

The following steps, which are recommended and presented by many statisticians and researchers,[17] are
followed in this study;

i.     Identifying the dependent variables.
ii.    Listing potential predictors.
iii.   Gathering the required observations for the potential models.
iv.    Identifying several possible models.
v.     Using statistical software to estimate the models.
vi.    Determining whether the required conditions are satisfied.
vii.   Using the engineering judgment and the statistical output to select the best   models.

       3.   REGRESSION M ODEL

 Regression analysis is a statistical method that uses the relationships between two or more quantities variables
to generate a model that may predict one variable from the other(s). The term multiple linear regression (MLR)

                                                                                                              12
Siham E. Salih


is employed when a model is a function of more than one predictor variable. The objective behind (MLR) is to
obtain adequate models, at a selected confidence level, using the variable data while at the same time satisfying
the basic assumptions of regression analysis.

The main assumptions of regression include:

           severe multicollinearity does not exist among predictor variable
           Influential observation or outliers do not exist in the data.
           The distribution of error is normal.
           The mean of error distribution is zero.

The objective is accomplished by selecting the model, which provides the highest adjusted coefficient of
determination (R²) and lowest mean square error (MSE), for a given data [17]. The same variables and criteria
used to perform AASHO Road Test (IRI) model are used to develop a (IRI) of the present study. Accordingly,
multiple linear regressions are used for the development process of this model.

                                                                  Outliers

If one or more of observations is different significantly from all others, it is called “outlier “. The cause of a
faulty observation may be a mistake. Outliers and influential observations are checked by using
Chauvinist's criterion [17]. The results of this test can be found in Tables (4).



                                                                                  Standard
                                                                                                              xm  x               xm x
Variables      Mean                Minim                  Maximum                                                           /s                /s
                                                                                  Deviation (s )
                                                                                                          X m = min              X m=maxi

PSI            4.074748            2.61206                4.852703                0.535917                2.729                  1.45

Slope
               0.0262481           0.00263                0.1005665               0.0293210               0.857                  2.534
Variance


                                  Table (4): Results of Chauvenet' Test for Outliers of PSI Database

                          Sample Size: 80 , X m = value of outlier. x= sample mean. s = standard deviation.


                        xm  x
                    (                  /s) tabulated = 2.74 >   all calculated values. Thus the outliers are not rejected

                                                            Multicollinearity

It is a condition that exists when the independent variables are correlated with another one. The adverse effect of
multicollinearity is that the estimated regression coefficient (b1, b2, etc.) tends to have large sampling
variability. By using STATISTICA software the correlation coefficients between all of the variables were
calculated and the correlation matrix was setup.



                                                            Developed Model

Scatter plot was carried out between the dependent and independent variables for the requirements of IRI model
building process. From the plots, the nature of relation between these variables can be expected and the best
relations are selected, the Scatter plots for selected function are illustrated in (Figures 10, 11, and 12) for
patching, cracking, and rut depth.


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International Journal Of Structronics & Mechatronics




                                                                                               1.8
                                                                                               1.6
                                                           y = 0.0035x - 0.0063
                                                                                               1.4
                                                               R2 = 0.9034
                                                                                               1.2
                                                                                               1




                                                                                                     SV
                                                                                               0.8
                                                                                               0.6
                                                                                               0.4
                                                                                               0.2
                                                                                            0
         350       300            250         200          150         100           50   0 -0.2
                                                Paching


                                               Figure 10 - Patching vs SV value


                                                                                               1.8

                                                                 y = 0.0018x - 0.014           1.6
                                                                                               1.4
                                                                     R2 = 0.8502
                                                                                               1.2
                                                                                               1




                                                                                                     SV
                                                                                               0.8
                                                                                               0.6
                                                                                               0.4
                                                                                               0.2
                                                                                            0
          600         500               400          300            200             100   0 -0.2
                                                Cracking

                                                Figure 11 - Cracking vs. SV value


                                                                                              2
                                                         y = 0.8285x - 0.2928
                                                              R2 = 0.8109
                                                                                              1.5


                                                                                              1
                                                                                                     SV




                                                                                              0.5


                                                                                              0
         2                  1.5                      1                    0.5             0
                                                                                              -0.5
                                                    RD


                                                    Figure 12 - RD vs. SV value




                                                                                                          14
Siham E. Salih


The multiple linear regression technique that is used for the purpose of IRI model development results in the
following model form;

    12. IRI=0.244759-.155070 LN (PSI)

Where:

      IRI =             International Roughness Index

      PSI =             Present Serviceability Index



The summary of the multiple linear regressions, and several possible developed models can be seen in Tables
(5) and (6).


 Regression Summary for Dependent Variable: SV

 R= .93524029 R²= .87467440 Adjusted R²= .87306766

 F(1,78)=544.38 p<.00000 Std.Error of estimate: .00979



                            St. Err.         St. Err                B                     St. Err.         t(78)      P-level

                            BETA             Of BETA.                                     Of B

 Intercpt                                                           .244759               0.009431         25.9538    000000

 LNPS                       -.935240         .040084                -.155070              .006646          -23.3319   000000



                                          Table (5): Regression Summary for IRI Model



Models                                                                             R2                      SEE


IRI=--1.55070*LN(PSI)+0.244759                                                     0.873                   0.00979


IRI=-1.8541*PSI+4.5148                                                             0.78                    9.7433



                 Table (6): Several Possible models From the Multiple Linear Regression Analysis for the IRI Model.

                                                   Results of the Analysis

The multiple linear regressions, using STATSTICA software has served its purpose in drawing attention to
developing a model by using the same independent variables as those used previously by Observed AASHO
model. The model developed is shown at the end of the previous section as IRI model (equation 2). The
independent variables ;Serviceability (PSI) that represent in slope variance , rut depth, cracking and patching
and those used in the model development process show that, the value of IRI is strongly affected by these
mentioned variables . The model indicates that the value of IRI decreases with the increase in Serviceability .



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International Journal Of Structronics & Mechatronics




                                                  Discussion of Results

Referring to the IRI model; one variable was found to be common to the general picture of the model
development, but this variable is content from many criteria slope variance, rut depth, cracking, and patching.
The coefficient of determination was found to be 0.94 that means; 94 percent of the IRI prediction can be
explained by this model.

                                                    Models Limitation

As with all regression models, the model is only valid within the ranges of the variables they were developed
from .Some additional limitations may be related to the study area. Specific specifications can be listed as
follows;

          i.    The select of sections is randomly in the study area and is uniform of a (1200 ft) each is used for
                the purpose of IRI model development.

          ii.   The range of data for IRI model can be seen in Tables (7).The intention of the limitation is not to
                suggest that the modeling effort has not been successful. It merely serves to alert of the limitations
                of the data.


Variable                     Mean                           Minimums                      Maximum

IRI                          0.026214                       0.002634                      0.100567

PSI                          4.144817                       2.529651                      4.949475


                                        Table (7): Ranges of Data in IRI Model Database

                                  VALIDATION OF THE DEVELOPED MODEL

The final step in the model building process is validation of the developed models. The objective is to assess the
ability of pavement condition index prediction model to accurately predict amount of IRI in the field. A review
of the statistical researches suggested the following methods for validation of a regression model [Ahmed,
Namir G 2002].

         check on model predictions and coefficients
         collection new data
         comparison with previously developed Models
         data splitting
         prediction sum of squares

                                   SELECTION OF VALIDATION METHODS

The literature suggests that all available methods of validation could be used. However, in this case, it is not
possible to use all the methods of validation .Therefore, the applicability of each method in terms of the
validation of the IRI model will be discussed and the most appropriate methods of validation will be selected.

          The third method (Comparison with Previously Developed Models).

The results of a newly developed model are compared with the previously developed model or with a theoretical
model. AASHO (IRI) Model is used to be compared with the developed IRI model.



                                                                                                                   16
Siham E. Salih


Due to the time constraints and the difficulties of collection additional data to maximize the sample size, the
above – mentioned third method (Comparison with Previously Developed Models) is proposed to be used in the
validation process of the developed IRI model.

                                                    Validation Results

As previously mentioned, AASHO _IRI model was used in the validation process of the new IRI_ model .The
values of the IRI estimated by use of AASHO model are plotted against those obtained by the application of the
new developed model. This plot can be seen in Figure (13).


      y = -4.1632Ln(x) + 0.557                                                           4
                 R2 = 0.7752                                                             3.5
                                                                                         3




                                                                                                IRI Observed
                                                                                         2.5
                                                                                         2
                                                                                         1.5
                                                                                         1
                                                                                         0.5
                                                                                         0
           1.4       1.2        1          0.8          0.6          0.4         0.2   0 -0.5
                                          IRI Estimation


                                Figure (13) Observed IRI Model versus Estimation IRI



The relation between observed and estimated IRI can be found in the following form in eq. 12;

    12. (IRI Observed) =-4.1632* LN (Developed IRI) + 0.557

These findings seem to be in good agreement with the relation y= x. The results of checking the goodness of fit
for the relation between observed and estimated IRI model by using Chi-square test t- test and the distribution
of errors ,these testing can be seen in the following paragraphs .

                                                    Goodness of Fit

To checking the goodness of fit for the predicted models. t – test and Chi- square test were carried out and the
following results are expressed;

T-test :

n= 80 4, df = 159    confidence level = 95%

There is no reason to reject the null hypothesis.

Thus the difference is not significant.

X2 –test

n =80, d f = 79, confidence level = 95%




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International Journal Of Structronics & Mechatronics




                 Variable                         x2 – value                      x2c – value

                 X= observed                      87.076531                       96.202

                 Y= predicted

                 IRI Model



For x2 < x2c. Thus is no significant difference between the observed and the predicted values

                                                CONCLUSIONS

The main conclusions that can be drawn from this research are summarized as follows:

    1.    The IRI model for the prediction of International Roughness Index is developed to be used for
          evaluation process of flexible pavement in Baghdad.

    2.    Depending on AASHO criteria, the Roughness index IRII model is developed through a multiple linear
          regression technique as follows:



          IRI = -0.1551Ln (PSI) + 0.2448               R2 = 0.8747

                     where:

    IRI          =            International Roughness Index,

    PSI                       Present Serviceability Index



    3.     The final Serviceability values should be differentiated from those originating directly from the this
          study opinion and from the values that are consistent with the AASHTO design method, since they are
          different. The final values are those expressed by users with respect to a minimum acceptable
          condition.

    4.     Serviceability values, as a function of roughness as perceived by this study, are higher than those
          obtained in similar studies in developed countries. Accordingly, it is advisable that results should not
          be used directly in the AASHTO design method. For this reason, Serviceability was calculated as it
          was in the AASHTO test and was related to roughness. With this method, the IRI-p relationships that
          were obtained for asphalt and concrete can in fact be used in conjunction with the AASHTO design
          method.

    5.      The study showed that users, other pavement distress like surface rutting, cracks and patch, are not
          statistically significant for predicting Serviceability values compared to IRI. For pavement users, the
          expression of serviceability only depends on roughness.

    6.      Serviceability in buses is considerably lower to that perceived in cars, and consequently roads to be
          used by buses should have a better standard in order to offer the same level of user comfort. The
          maximum roughness which users deem acceptable is lower in asphalt than in concrete, although users
          associate the same value of final Serviceability for both types of pavements.

    7.    The latter reveals that irregularities in the longitudinal profile, as represented by IRI, are less
          uncomfortable in the case of concrete than asphalt.

                                                                                                               18
Siham E. Salih


                                               REFERENCES

    1.   W. N. Carey and P. E. Irick, “The Pavement Serviceability Performance Concept”. Highway Research
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    3.   B. Al-Omari and M. I. Darter, “Relationships Between International Roughness Index and Present
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    17. Kennedy , J. B.; & Neville A.M [1975],"Basic Statistical Methods               for    Engineers and
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International Journal Of Structronics & Mechatronics


    19. Ogden, K.W. Safer roads – A guide to road safety engineering. Avebury Technical, Aldershot, 1996.

    20. Rigden P.J. Skid resistance of roads and streets. CSIR Special Report Pad 64,Pretoria, 1988.




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