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(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. 1 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, 2 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. 3 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. 4 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 5 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 6 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 7 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 8 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* CP 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) 9 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 10 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 11 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. 13 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 . 15 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% 17 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 Board. Record 250, 1960. 2. K. T. Hall and C. E. Correa, “Estimation of Present Serviceability Index from International Roughness Index”, Transportation Research Record, 1655 (1999), pp. 93–99. 3. B. Al-Omari and M. I. Darter, “Relationships Between International Roughness Index and Present Serviceability Rating”, Transport Research Record, 1435 (1994), pp. 130–136. 4. W. D. O. Paterson, Road Deterioration and Maintenance Effects. 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