Performance Prediction Models of Pavement Highway ...

O. Salem, A. El-Assaly, and S. AbouRizk

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Performance Prediction Models of Pavement Highway Network in Alberta By: O. Salem1, Ph.D., CPC, A. El-Assaly2, Ph.D., and S. AbouRizk3, Ph.D., P.Eng. 1

Assistant Professor, Department of Civil & Environmental Engineering, University of Cincinnati, PO Box 210071, Cincinnati, OH, USA 45221, tel.: 513-556-3759, fax: 513-556-2599, email: [email protected]

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Project Manager, CoSyn Technology, 9405- 50th street, Edmonton, Canada T6B 2T4, tel.: 780-440-7188, email: [email protected] 3 Professor, Civil and Environmental Engineering Department, 220 Civil/Electrical Engineering Building University of Alberta, Edmonton, Alberta, Canada T6G 2B7, tel.: 780- 492-8096, fax: 780-492-0249, email: [email protected]

TRB 2003 Annual Meeting CD-ROM

Paper revised from original submittal.


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ABSTRACT: Pavement engineers and managers should be aware of the economical consequences of selecting a particular rehabilitation and construction alternative. Pavements are complex physical structures that respond to the influence of numerous environmental, subsurface, and load-related variables and their interactions. Subsequently, the task of predicting the multi-faceted responses of pavements to the series of interrelated variables is complex and must be addressed by using a number of assumptions and simplifications. This paper presents a methodology used to develop deterioration models for the primary highway network in the province of Alberta, Canada, based on statistical stratification of the highway network. A major assumption is that a road is considered to be deteriorated and reached its service life limit when its roughness reaches a specified trigger value. The International Roughness Index (IRI) was taken as the roughness measurement in this research, with a value of IRI equal to 2.8 considered to be the trigger for initiating rehabilitation action. Approximately 1,700 road segments comprise the primary highway network in Alberta. Each of these segments has its own set of attributes. A stratification methodology was used to classify and group these segments into eight groups, each possessing the same characteristics in terms of its life cycle. This paper describes the various components of the developed deterioration models, the factors affecting pavement performance and service life, and the statistical stratification process of highway pavement networks.




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INTRODUCTION The study and development of pavement performance models have received the greatest impetus from user agencies that are beginning to develop or upgrade their Pavement Management Systems (PMS). Reliable pavement performance prediction models are essential for any practical pavement design and pavement preservation effort. The need for pavement performance prediction encompasses the financial planning and budgeting functions. Responsible agencies need to assess how long a given pavement section will provide an adequate level of service before requiring rehabilitation or reconstruction. This knowledge, together with the knowledge of available rehabilitation strategies and their costs and benefits, enables the estimation of long-range funding requirements for pavement preservation and provides a means to analyze the consequences of various budget scenarios on the condition of the pavement network.

Pavement Performance Models Previous researchers have explored numerous pavement performance prediction models in the past (Alsherri and George, 1988; Butt et al., 1994; Davis and Van Dine, 1988; Hutchinson et al., 1994; Lee et al., 1993; Zhang et al., 1993). Traditionally, these pavement performance models have been categorized as being either project-level or network-level models. The following section provides a synopsis and categorization of prediction models according to the assumptions and methods upon which they are based. Mechanistic Models Mechanistic models can either yield pavement serviceability predictions directly (Queiroz, 1983) or they may predict pavement distresses which are then synthesized and related to serviceability. Over the past three decades, considerable effort has been devoted to the development of deterministic-based prediction models (Sobanjo, 1993; Feighan et al., 1988). As a result, various deterministic models have been developed for regional or local pavement management systems. However, it is not adequate to apply deterministic models to all situations of pavement management due to the following: 1) the uncertainties in pavement behavior under changeable traffic load and environmental conditions; 2) the difficulties encountered in quantifying the factors or parameters that substantially affect pavement deterioration; and 3) the errors associated with measuring pavement condition and the bias from subjective evaluations of pavement condition. Probabilistic Models Probabilistic models have recently received considerable attention from pavement engineers and researchers. Darter and Hudson (Darter, 1973; Hudson, 1975) extensively discussed the principles of applying probabilistic models for the prediction of pavement deterioration versus time in the 1970’s. In their studies, a quantitative relationship between reliability and the four basic elements involved in pavement system design; probability, performance, time, and environment has been developed on the basis of substantial investigations and statistical analysis of all types of variations between design and actual values. Although great progress has been made in the development of probabilistic modeling of pavement performance, the applicability of many existing probabilistic models is limited to local or regional pavement networks, which are classified on the basis of traffic level, subgrade condition, and pavement thickness. One of the major challenges facing the existing probabilistic models is the difficulty in establishing Transition Probability Matrices (TPMs). Most TPMs of the existing probabilistic models are built using either Markov process modeling or regression analysis through a large amount of observed long-term pavement performance data. Karan investigated pavement deterioration functions by means of Markov process modeling for maintaining the pavement covering the Waterloo, Ontario regional road network (Karan, 1979). In his study, pavement performance deterioration versus age was modeled as time-independent using the Markov process with a constant TPM throughout the programming period. Each element of the TPM is built on the basis of the average subjective opinions of experienced engineers through individual interviews and questionnaires. It should be noted that considerable time and expenses were incurred to develop the TPM through subjective information collection and processing.




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Pavement Roughness Pavement roughness is defined by two indexes in Alberta. International roughness index (IRI), and riding comfort index (RCI). The IRI is an index that is calculated via sensors that can measure longitudinal profile. The IRI, is an open-ended roughness scale where zero represents a perfectly smooth road measured in unit meters per kilometer (m/km). The RCI is the most commonly used and recognized roughness-based performance index. This index is measured on a scale of 0 to 10, where 10 is a perfectly smooth road. The RCI is developed through a panel rating procedure, which correlates the measured roughness to subjective ratings generated, by a panel of agency personnel riding in a special vehicle. The Alberta Infrastructure (AI) decided to convert to the IRI in the 1990s, due to the growth of IRI usage as a current industry standard for roughness measurement. Research Objective The objective of this project is to develop a set of deterioration models to predict future pavement performance based on a statistical stratification of all the pavement classes that are existing in Alberta. The highways under the jurisdiction of Alberta Infrastructure (AI) are stratified into groups, each exhibiting a similar performance behavior (Salem, 1999). This stratification formed the basis for deriving deterioration curves for each of the resulting groups. The deterioration models were developed to predict the rate of change in performance, rate of future deterioration, and years of need. Historical data and expert knowledge in Alberta revealed that the vast majority of rehabilitation needs in the past were triggered by the reduction of pavement’s RCI values below the accepted level of 5.5 or IRI reaches the value of 2.8. It is stated in the pavement design manual of Alberta Infrastructure that “a significant portion of pavements are rehabilitated due to an unacceptable ride rather than due to structural problems. For this reason roughness monitoring constitutes an important rehabilitation design input” (AT&U, 1997). FACTORS AFFECTING INFRASTRUCTURE PERFORMANCE Identifying and analyzing the factors that affect life cycle performance of pavement structures are essential for developing performance models. These factors can be either identified from historical performance data or from accelerated failure tests. Hudson et al (1997) lists factors affecting infrastructure deterioration in five categories. These categories are: 1) load/usage, 2) environment, 3) material, 4) construction quality, and 5) interaction effects. Turay and Haas (1991) presents several factors affecting pavement deterioration that were considered in constructing the transition probability matrices for a Markov process to predict deterioration. The factors presented in the study include environmental condition, subgrade type, traffic volume, pavement type, and pavement thickness. The proposed stratification process utilizes the pavement performance factors considered in formulating the design formula in the widely used “AASHTO Guide for Design of Pavement Structures” (AASHTO, 1986). As for the pavement thickness factor, the AASHTO procedure for designing pavement structures recommends similar pavement thickness for the same level of traffic (i.e., low, medium, high) (AASHTO, 1986; AT&U, 1997). This was verified through the correlation analysis performed between traffic volume and pavement thickness. As a result, the effect of the following factors on pavement service life (i.e., time to failures of pavement sections) is analyzed: Traffic Volume /Equivalent Single Axle Load (3 levels) Environment /Climatic Regions (3 regions) Subgrade Condition /Soil Types (6 types) Pavement Base Types (3 types) The combinations of the various levels of these factors will result in 162 pavement classes (6 soil types × 3 pavement types × 3 climatic regions × 3 ESAL levels) (Salem, 1999)



O. Salem, A. El-Assaly, and S. AbouRizk DATA ACQUISITION

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In Alberta, there are more than 14,250 kilometers of paved highways on the primary highway network. Pavement data for the primary highway network was obtained from the Alberta Infrastructure database. This database is stored in the department’s mainframe in a flat text file format and includes information dated back to the 1950s. The database is organized by inventory section along each highway and contains extensive historical information regarding construction history, pavement performance, traffic volumes, environmental conditions, material types, and rehabilitation and overlay information. The database fields include the following information (El-Assaly et al., 1998): Highway control section number Location and length of each control section Pavement inventory by designated section, width, and lane direction Climatic region of each pavement inventory section (3 regions) Northern region Central region Southern region Pavement layer types and thickness (3 types) GB – Granular Base SC – Soil-Cement base FD – Full Depth pavement Subgrade soil types according to the Unified Soil Classification System (6 types) CL – Inorganic clays of low plasticity, gravelly clays, sandy clays, silty clays, lean clays CL-CI CI – Inorganic clays of medium plasticity, gravelly clays, sandy clays, silty clays CI-CH CH – Organic clays of high plasticity, fat clays BO – Rock layer Traffic data ESAL/Day – Equivalent (18-kip) Single Axle Loads per day (3 levels) Year of construction RCI/IRI readings for each section Years of rehabilitation activities Years of failure due to roughness Figures 1 and 2 illustrate the percentages of each subgrade soil and pavement type, respectively, within the primary highway network in Alberta. From the two figures it can be concluded that more than 90% of the total length of the network is built on six soil types. These types are BO, CH, CI, CI-CH, CL, and CL-CI. In a similar fashion, three pavement types constitute about 98% of the network pavement structures. These types are GB, SC, and FD (ElAssaly et al., 1998). After filtering and querying the database, a subset data is generated that contains the records that have control sections with RCI of 5.5 or less (i.e., IRI of 2.8 or more). Furthermore, new fields were created to indicate the service lives of pavements according to the length of time until their RCI/IRI dropped to the trigger (time-to-failure). Times-to-failure for pavement sections are calculated from the actual historical data that is collected and entered into the Pavement Management System (Salem, 1999).

STRATIFICATION OF PAVEMENT CLASSES For the purpose of this study, a pavement class is defined as each combination of factors’ levels that affect pavement service life. For example, the combination of climatic region “Central”, pavement type “GB”, soil type “CL”, and Traffic Level “medium” is considered a one-pavement class. And the combination of climatic region “South”, pavement type “GB”, soil type “CL”, and Traffic Level “medium” is considered another pavement class.



O. Salem, A. El-Assaly, and S. AbouRizk 6 The objective of the stratification process is to group the various pavement classes in such away to ensure that each group represents pavement classes that have no significant differences between their service-life means. A Multiple Comparison Analysis of means is performed on the pavement classes that are resulted from all possible combinations of different levels of the considered factors. As a result, all pavement classes contained in a group will have a similar behavior (non-significant difference) in terms of their service lives. Multiple Comparison Tests Before applying any of the statistical multiple comparison tests (post hoc tests), the mean of pavement service life for each pavement class must be calculated. In many cases, the statistical “F” test, in an analysis of variance (ANOVA) table, can be used to show whether a significant difference among a group of means exists or not. In most practical cases, this is not enough. In this research, it is desirable not only to know whether a difference exists among the means (service life means of all pavement classes), but also which mean differs from another. In other words, the interest of this study is to compare all pairs of service life means for all pavement classes. In this case, the null hypotheses that should be tested are:

µi = µj,

Ho:

for all i ≠ j…………(1)

Where µi, µj are the means of service life for pavement class “i” and “j” respectively. The two-sample “t” test can be used to test for significant differences between all possible pairs of service life means. However, this test has many limitations when testing a large number of means. This procedure will require a large number of “t” tests even if the number of means is relatively small. Testing for significant difference between pairs of “N” means will require performing the test for    

N

2

   

=

N ( N − 1) times………………………….(2) 2

For example, if the resulting pavement classes are equal to 72, this will require 2556 “t” tests to check for significant differences between the pairs of means. Additionally, the probability (confidence level) associated with the “t” test assumes that only one test is performed. When several means are tested pairwise, the probability of finding one significant pair by chance increases rapidly with the number of pairs (Miller, 1990). For example, if a 0.05 significance level is used to test whether means “a” and “b” are equal, and means “c” and “d” are equal, the overall confidence level in this case would be less than the assumed 0.95 (95%) and would be equal to 0.95 x 0.95 = 0.9025. For 10 pairs of means tested at the 0.05 significance level, the confidence level will go down to less than 0.6 (60%). As a result, these tests are not independent and they will show, in many cases, significant differences between pairs of means even when such difference does not exist. There are several multiple comparison tests that overcome the disadvantages of the two-sample “t” test solution. Some of these more widely used tests are: • • • • •

Duncan’s Multiple Range Test Tukey’s test The Least Significant Difference (LSD) Method Bonferroni Method The Newman-Keuls Test

Professional statisticians often disagree over the utility of the various multiple comparison methods. Among the more popular of these methods is Duncan’s multiple range test (Duncan, 1955). Several statistical references indicate that Duncan’s multiple range test is quite powerful and very effective at detecting differences between means when they exist; it should be satisfactory for many general applications (Carmer, 1973; Miller, 1990; and



O. Salem, A. El-Assaly, and S. AbouRizk 7 Montgomery, 1991). On the other hand, Tukey’s multiple comparison test is considered to be more conservative and less powerful in detecting differences between means than most of the other procedures (Montgomery, 1991). However, if the number of comparisons is large, the Tukey’s test becomes more sensitive in detecting differences, and is highly recommended (SPSS, 1998). As a result of the previous discussion, the stratification process utilizes Duncan’s method, and also uses Tukey’s test for validation to stratify the pavement classes into groups. To check the sensitivity of the results to different significance levels (alpha), Three different values for alpha are used: 0.1, 0.05, and 0.01. The sensitivity analysis showed that the stratification results from the multiple comparison tests are consistent for different alpha values. As a sort of validation, expert knowledge and engineering judgement are finally integrated to provide the final stratification list. Duncan’s Multiple Range Test As explained by Montgomery (1991), to apply Duncan’s multiple range test, all the treatment means are arranged in ascending order, and the standard error of each mean is determined as

S yi =

MS E n ………………………………….(3)

Where, MSE = Mean Square of Error n = Sample Size From Duncan’s table of significant ranges, The “rα (p, f)” value for p = 2,3, … , a, can be determined. Where “α” is the significance level and “f” is the number of degrees of freedom for error. The next step is to convert the means differences into a set of “a-1” least significant ranges by calculating Rp = rα (p,f) Syi,

for p=2,3,….., a …………………(4)

The observed differences between means are tested, starting with the largest and compared with the least significant range Ra. Next, the difference between the largest and the second smallest variable is computed and compared with the least significant range Ra-1. These comparisons are continued until all means have been compared with the largest mean. Then, the difference between the second largest mean and the smallest is computed and compared to the least significant range Ra-1. This process is continued until the differences of all possible a(a-1)/2 pairs of means have been considered. If an observed difference is greater than the corresponding least significant range, it is concluded that the pair of means in question is significantly different (Montgomery, 1991). Tukey’s Multiple Comparison Test Tukey’s procedure is largely based on the studentized range statistic. It requires the use of the upper “∝” percentage point of the studentized range for groups of means of size “a” and “f” error degrees of freedom “q∝(a,f)”. This “q∝(a,f)” is necessary for determining the critical value for all comparisons between pairs of means. Hence, Tukey’s test assumes that a pair of means is significantly different if the absolute value of their differences exceeds

Tα = qα (a, f )S yi

………………………….(5) Where, q∝(a,f) can be determined from the table of percentage points of the studentized range statistics (Montgomery, 1991), and Syi is the standard error and can be determined as

S yi =

MS E n ……………………………….(6)

Where,



O. Salem, A. El-Assaly, and S. AbouRizk MSE = Mean Square of Error n = Sample Size

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Final Pavement Classes The next step is to examine all the levels within each performance factor (i.e., pavement type, soil type, climatic region, etc.). For each main effect (i.e., factor), the Duncan’s and Tukey’s multiple comparison tests are performed to investigate whether significant differences exist within the various levels of the main effect itself. For example, in the case of the “soil type” main effect (or factor), which is comprised of six levels (types), the multiple comparison test indicates that the differences between two pairs of service life means are not significant (i.e., CI & CI-CH, and CL & CL-CI). Each of those soil type pairs would then be treated as one type (level). This would result in reducing the number of soil levels to four instead of six. The same procedure is repeated for the “pavement type” main effect. This time, the test resulted in three distinct pavement types. As for the ESAL levels, the results of the multiple comparison test recommend combining the Low and the Medium categories into one category. This result is also supported by the AASHTO design procedure, which provides similar pavement thickness for low and medium traffic levels, and increased pavement thickness for high traffic levels (AT&U 1997). For the climatic regions, the multiple comparison test shows significant difference among the means of the three climatic regions, hence the pre-identified three regions are all considered. Hence, after applying the multiple comparison procedures to the individual factors, the following factors are considered: • • • •

Pavement type – Three types (GB, SC, and FD) Soil type – Four Types (BO, CH, CI, and CL) Climatic region – Three Regions (Central, Northern, and Southern) Traffic - Two levels (Low 1000 ESAL)

As a result, the number of pavement classes (combinations of all factor levels) is reduced from 162 to 72 (4 soil types × 3 pavement types × 3 climatic regions × 2 ESAL levels). Final Pavement Groups The multiple comparison tests are then applied in such away that the variability arising from pavement types can be controlled. Pavement types are used as 3 blocks, and each of the 24 combinations (classes) of the other factor levels (3 climatic regions, 4 soil types and 2 traffic levels) is assigned as a treatment for each pavement type. The objective of this design is to determine whether the difference in service lives’ means between various pairs of treatments within each pavement type is significant or not. Consequently, the multiple comparison test is performed on the pavement classes in order to stratify them into groups within each pavement type. An example of one of the multiple comparison test results is shown in Table 1. It illustrates the service life mean differences between each pair of pavement classes and the level of significance of such differences. If there were no significant difference in means at a certain confidence level (i.e., 95%), this would be an indication that the two classes should be combined in one group. For example, class 1 and class 15 can be combined in one group, while class 1 and class 8 may not be in a different group. Finally, Table 2 presents the final result of the stratification process and defines the classes within each group. In the next section, The resulting eight pavement groups are used to develop the deterioration models. These models will represent the deterioration behavior of the entire primary highway network in Alberta. It should be noted that only 85% of the data were used for the model development. The remaining 15% of the data were used for validation. DETERIORATION PREDICTION MODEL DEVELOPMENT A single prediction model cannot provide enough accuracy to capture the variety of conditions that occur in a pavement network including pavement types, soil types, climatic regions, and ESAL. Therefore, the prediction approach was not limited to developing one model representing the entire network, but rather the concept of a



O. Salem, A. El-Assaly, and S. AbouRizk 9 “family of curves” was adopted. This approach consisted of developing a model for each of the previously defined eight groups. As indicated in the previous section, table 1 includes the average recorded service lifetime for each of these eight groups calculated by assuming the year of need, when the RCI reaches a critical value of 5.5. These values will be used in a later stage to validate the developed prediction models, which are based on the IRI values. A major assumption made during model development was that rehabilitation actions are initiated based on a roughness IRI value. The pavement experts in Alberta Infrastructure suggested that the threshold value to trigger rehabilitation is assumed to be IRI=2.8. Sigmoidal Prediction Models The sigmoidal prediction model was used to represent the pavement roughness in terms of IRI (Garcia, 1985). The prediction model used in this research is a transformation of the general sigmoidal model. The transformed model is defined as:

IRI = IRI o + e( a − b*c where IRI IRIo x t a, b, c

= = = = =

x

)

(7)

Roughness at given age Roughness at age zero Ln (t) time at given age model coefficients

This sigmoidal model formulation is based on models analyzed by the Texas Transportation Institute (Garcia, 1985). The sigmoidal model was selected due to its increased accuracy in performance predictions (Hajek, 1985). Although exponential models will often provide sufficient accuracy, the computational power of today’s computers enable the calculation of more complex models such as non-linear regression, which uses an iterative process. The sigmoidal model can also present differently shape functions. Depending on the model coefficients, the curve can either be a straight line, convex, concave, or S-shaped with various degrees of curvature. Historical data was used to calculate the coefficients. To obtain the required historical data points, the predefined eight groups were used. As previously mentioned, each of these groups includes several road combinations attaining specific road segments each with different service lives. Therefore, a single group, which has the same deterioration behavior, contains several segments with different ages. To illustrate the model approach taken in research, two groups are presented. Model for Group 3 Group 3 has combination as shown in Table 2. The coefficients for this model were found to be as follows:

a b c

= = =

3.28820 4.23943 0.86640

The previous analysis conducted in the stratification process concluded that the average lifetime of pavement that has Group 3 characteristics is equal to 22.41 years based on the RCI data. Figure 3 illustrates twenty four historical data points with ages starting from 0 to 25 years; the IRI range of these points is from 1.2 to 2.9. As illustrated in Figure 3, the model is displayed as a solid line while the observed roughness data are displayed as a diamonds shape. The model predicted a service life of 22.8 years. This value closely matches the average lifetime



O. Salem, A. El-Assaly, and S. AbouRizk 10 (22.41) calculated from the RCI records in the stratification process. The calculated coefficient of determination (R2) for this model is 0.534. Model for Group 7 Figure 4 illustrates the developed deterioration model for Group 7. The stratification process resulted in an average lifetime of 17.02 years for this group, based on the historical RCI data. The combinations of attributes contained in Group 7 are again presented in Table 2. The analysis produced the following coefficients for the sigmoidal model:

a b

= =

3.83728 4.57524

c

=

0.88944

The model for Group 7 predicted that the lifetime of the segments whose combinations listed in Table 2 would be approximately 18 years. This value is approximately equal to the average lifetime (17.02 years) calculated from the RCI data. The coefficient of determination (R2) for the regression model is equal to 0.868. Figure 4 is a summary of the research findings and it depicts all the developed models for the eight groups. The point of intersection between the developed deterioration curve and IRI value of 2.8 is considered the need year. The need year is defined as the year in which the roughness of the pavement has reached its limit and rehabilitation is needed. MODEL VALIDATION Table 2 summarizes the research results. As indicated, The modeled (expected) lifetime of each group matches the observed average lives resulted from the stratification process. Note that the “Observed Average Lifetime” is calculated from the stratification process as per Table 2. The “Predicted Average Lifetime” column is the analysis concluded from using the 85% of the IRI data for the regression purpose. The coefficients of determination (R2) for the models vary from 0.868 to 0.534. These variations are due to number of combinations in each group. However, the values of the R2 seem to be reasonable, indicating that the models are an accurate representation of the observed data. As previously mentioned, not all the data was used to develop the deterioration models for the defined eight groups. The rest of the data was left for the model validation. Figure 6 is a summary of the eight models and the validation points. The average error is used to determine the validity of each of the developed models. If the absolute average error is less than 10%, then the model is considered to be valid. Average absolute error is described in equation (8).

1 Average Absolute Error = N

∑i =1 OiP−i Pi N

(8)

Where: Oi = Pi = N =

Observed Value Predicted Value Number of Validating points

Table 3 presents the results for the average errors for each of the developed models. The values of the average errors indicate that the developed models should be accepted as prediction models for pavement deterioration with a high confidence.




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CONCLUSIONS AND RECOMMENDATIONS The primary highway network in Alberta was used to develop the deterioration models, and about 1700 segments were used to perform the statistical analysis and modeling processes. The stratification process was used as a base for classifying the entire network into eight distinct groups. These groups were used to develop the “family of curves” that model deterioration behavior for each of these groups. Deterioration models were developed based on pavement roughness data. The International Roughness Index (IRI) was considered to be the trigger for rehabilitation. No other distresses were included in forming the deterioration models. The sigmoidal model was selected due to its accuracy in pavement performance predictions. The sigmoidal model can also present the roughness data with pavement ages. This model is a nonlinear model, which requires performing iteration processes to achieve the model coefficients. The model showed flexibility and was appropriate to fit the observed roughness data. The models were developed using 85% of the observed data. The rest of the data was used in model validations. Models were validated through visual inspection, coefficient of determination (R2), and calculated average absolute error for each model. Models showed reasonable accuracy in predicting the average lifetime for each group. The sigmoidal model, as observed from the stratification process, indicated that group 6 has the least average lifetime of approximately 13 years. Group 5 showed the highest average lifetime of approximately 24.0 years. The initial curvature in the developed models indicated a higher rate of deterioration in the first 5 to 7 years of the pavement’s life. In general, the deterioration slows down and the curvature flattens after the first 7 years. More investigations are needed to reveal the cause of early pavement deterioration in specific groups (or segments). The developed models are currently integrated into the Pavement Management System (PMS) of Alberta Infrastructure.


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REFERENCES 1. Alsherri, A., and George, K.P., (1988). “Reliability Model for Pavement Performance”, Journal of Transportation Engineering, Volume 114, Number 2, pp. 294-306. 2. Butt, A.A., Shahin, M.Y., Carpenter, S.T. and Carnahan, J.V. (1994). “Application of Markov Process to Pavement Management Systems at Network Level,” Proceedings of the Third International Conference on Managing Pavements, San Antonio, Texas, May, pp. 159-172. 3. Davis, C.F., and Van Dine, C.P., (1988). “Linear Programming for Pavement Management”, Transportation Research Record, Number 1200, Transportation Research Board, National Research Council, Washington, D. C., pp. 71-75. 4. Hutchinson, B., Nix, F.P., and Haas, R. (1994). “Optimality of Highway Pavement Strategies in Canada,” Pavement Management Systems, Transportation Research Record, Number 1455, Transportation Research Board, National Research Council, Washington, D.C., pp. 111-115. 5. Lee, Y., Mohseni, A., and Darter, M.I., (1993). “Simplified Pavement Performance Models,” Pavement Management Systems, Transportation Research Record, Number 1397, Transportation Research Board, National Research Council, Washington, D.C., pp. 7-14. 6. Zhang, Z., Singh, N., and Hudson, W. R., (1993). “Comprehensive Ranking Index for Flexible Pavement Using Fuzzy Sets Model”, Transportation Research Record, Number 1397, Transportation Research Board, National Research Council, Washington, D.C., pp. 96-102. 7. Queiroz, C., “A Mechanistic Analysis of Asphalt Pavement Performance in Brazil,” Asphalt Paving Technology, Volume 52, pp 474-488, 1983. 8. Sobanjo, J.O., (1993). “Deterioration Models for Highway Bridges,” Proceedings of the ASCE Conference on Infrastructure Planning and Management, Denver, Colorado, June, pp. 387-392. 9. Feighan, K. J., Shahin, M.Y., Sinha, K.C., and White, T.D., (1988). “Application of Dynamic Programming and other Mathematical Techniques to Pavement Management Systems”, Transportation Research Record, Number 1200, Transportation Research Board, National Research Council, Washington, D. C., pp. 90-98. 10. Darter, M.I., and Hudson, W.R., “Probabilistic Design Concepts Applied to Flexible Pavement System Design,” Report 123-18. Center for Highway research, University of Texas at Austin, 1973. 11. Hudson, W.R., (1975) “ State-of-the-Art Prediction Pavement Reliability From Input Variability,” report FAA-RD-75-207. U.S. Army Waterways Experiment Station, Vicksburg. Miss. 12. Karan M.A., R. Hass, K. Smeatan, and A. Cheethan, A System for Priority Programming of investments for Road Network Improvements, University of Waterloo, Waterloo, Canada 1979. 13. Salem, O. (1999), “Infrastructure Construction and Rehabilitation: Risk-Based Life Cycle Cost Analysis,” a Ph.D. Dissertation, Civil Engineering Department, University of Alberta, Edmonton, Canada. 14. (AT&U) (1997). “Pavement Design Manual”. First Edition, Alberta Transportation and Utilities Report (Alberta Infrastructure), Edmonton, Alberta, Canada 15. Hudson, W., Haas, R. and Uddin, W., (1997). “Infrastructure Management”. McGraw-Hill, New York, N.Y. 16. Turay, S., and Haas, R. (1991). “A Road Network Investment System (RONIS) for Developing Countries”, TRB, Research Record 1291, Vol. 1 17. (AASHTO) (1986). “AASHTO Guide For Design of Pavement Structures.” American Association of State Highway and Transportation Officials, Washington, D.C. 18. El-Assaly, A., Salem, O. and Hammad, A. (1998), “ Analysis and Stratification of Pavement Data for the Primary Highway Network in Alberta”, NSERC Technical Report. 19. Miller, I., Freund, J. E. and Johnson, R. A., (1990). Probability and Statistics for Engineers, Fourth Edition. Prentice Hall / Englewood Cliffs, New Jersy. 20. Duncan, D., (1955), “Multiple Range and Multiple F tests”, Biometrics, Vol. 11, 1955. 21. Carmer, C., and Swanson, M., (1973), “Evaluation of Ten Pairwise Multiple Comparison Procedures by Monte Carlo Methods”, Journal of the American Statistical Association, Vol. 68, No. 314, 1973. 22. Montgomery, D., “Design and Analysis of Experiments”, John Wiley & Sons Inc., New York, N.Y., 1991. 23. SPSS Base 8.0, (1998), “Applications Guide”, SPSS Inc. Chicago, IL, 1998 24. Garcia-Diaz, A., and Riggins, M. (1985), “Serviceability and Distress Methodology for Predicting Pavement Performance,” Transportation Research Record, Washington D.C. 25. Hajek, J.J., and Phang W.A., Prakash A., and Wrong G., “Performance Prediction for Pavement Management,” Proceedings Vol. 1, North America Pavement Management Conference, Toronto, Canada 1985.


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LIST OF TABLES TABLE 1 Example for a Multiple Comparison Test performed between Classes of Granular Base (GB) Pavement Type and Rocky (BO) Soil type at 90% & 95% Confidence level TABLE 2 Pavement Groups Resulted from The Stratification Process TABLE 3 Comparison of Observed vs. Predicted Average Lifetime TABLE 4 Mean Errors for the Developed Models LIST OF FIGURES FIGURE 1 Classification of soil types in Alberta along the primary highway network. FIGURE 2 Classification of pavement types in Alberta along the primary highway network. FIGURE 3 Prediction model for group three. FIGURE 4 Prediction model for group seven. FIGURE 5 Prediction models for the eight groups. FIGURE 6 Illustration of the validation points for the eight groups.


13



14

Classification of soil types in Alberta

CL-CI 16%

Others 10%

BO 4%

CH 9% CI 21%

CL 30%

CI-CH 10%

FIGURE 1 Classification of soil types in Alberta along the primary highway network.


14



15

Classification of pavement types in Alberta

SC 21%

Others 1%

FD 5%

GB 73%

FIGURE 2 Classification of pavement types in Alberta along the primary highway network.


15



16

TABLE 1 Example for a Multiple Comparison Test performed between Classes of Granular Base (GB) Pavement Type and Rocky (BO) Soil type at 90% & 95% Confidence level

Multiple Comparisons Dependent Variable: Service Life

Tukey HSD

(I) GROUP 1

8

15

16

(J) GROUP 8 15 16 1 15 16 1 8 16 1 8 15

Mean Difference (I-J) Std. Error 10.2997* .810 -.8554 .562 -2.1114* .560 -10.2997* .810 -11.1552* .880 -12.4111* .879 .8554 .562 11.1552* .880 -1.2560 .657 2.1114* .560 12.4111* .879 1.2560 .657

Sig. .000 .425 .001 .000 .000 .000 .425 .000 .223 .001 .000 .223

90% Confidence Interval Lower Upper Bound Bound 8.4435 12.1560 -2.1438 .4330 -3.3956 -.8271 -12.1560 -8.4435 -13.1711 -9.1392 -14.4244 -10.3978 -.4330 2.1438 9.1392 13.1711 -2.7619 .2500 .8271 3.3956 10.3978 14.4244 -.2500 2.7619

Sig. .000 .425 .001 .000 .000 .000 .425 .000 .223 .001 .000 .223

95% Confidence Interval Lower Upper Bound Bound 8.2186 12.3809 -2.3000 .5892 -3.5513 -.6714 -12.3809 -8.2186 -13.4154 -8.8949 -14.6684 -10.1538 -.5892 2.3000 8.8949 13.4154 -2.9444 .4325 .6714 3.5513 10.1538 14.6684 -.4325 2.9444

Multiple Comparisons Dependent Variable: Service Life

Mean Difference (I-J) (I) GROUP (J) GROUP Std. Error Tukey HSD 1 8 10.2997* .810 15 -.8554 .562 16 -2.1114* .560 8 1 -10.2997* .810 15 -11.1552* .880 16 -12.4111* .879 15 1 .8554 .562 8 11.1552* .880 16 -1.2560 .657 16 1 2.1114* .560 8 12.4111* .879 15 1.2560 .657


16



17

TABLE 2 Pavement Groups Resulted from The Stratification Process

Group

Base Type

Soil Type

Climatic Region

ESAL

Mean Life

1

FD

All

All

All

14.48

2

GB

CH

North

Low

17.08

CH

Central

High

CH

South

Low

CH

Central

Low

CH

North

High

CI, CI-CH

North

Low

CI, CI-CH

North

High

CI, CI-CH

South

High

CI, CI-CH

Central

High

CL, CL-CI CL, CL-CI

South Central

High High

CI, CI-CH

South

Low

CL, CL-CI CL, CL-CI

North South

Low Low

3

GB

22.41

4

GB

CI, CI-CH CL, CL-CI

Central Central

Low Low

25.27

5

GB

BO

Central

Low

23.74

BO

South

Low

BO

North

Low

BO

South

High

CI, CI-CH

North

Low

CI, CI-CH

Central

Low

CI, CI-CH

Central

High

BO

Central

Low

CH

North

Low

CL, CL-CI

Central

High

CH

Central

Low

CL, CL-CI

Central

Low

CL, CL-CI

North

Low

CI, CI-CH

South

Low

CL, CL-CI

South

Low

6

7 8

SC

SC SC


12.92

17.02 21.12

17



3

18

2.8

2.5 R2=0.534 IRI

2 1.5 1 0.5 0 0

2

4

6

8

10

12

14

16

18

20

22

24

26

28

Age IRI

Sigmoidal IRI

FIGURE 3 Prediction model for group three.


18



19

3 2.8 2.5 R2=0.868 IRI

2 1.5 1 0.5 0 0

2

4

6

8

10

12

14

16

18

20

Age IRI

Sigmoidal Model

FIGURE 4 Prediction model for group seven.


19



20

3 2.9

IRI

2.8 2.7 2.6 2.5 8

10

12

14

16

18

20

22

24

26

Age Group 1 Group 7

Group 2 Group 8

Group 3

Group 4

Group 5

Group 6

FIGURE 5 Prediction models for the eight groups.


20



21

TABLE 3 Comparison of Observed vs. Predicted Average Lifetime

Group 1 2 3 4 5 6 7 8

Base Type FD GB GB GB GB SC SC SC


Ave. Lifetime (Observed) 14.48 17.08 22.41 25.27 23.74 12.92 17.02 21.12

Ave. Lifetime (Predicted) 15.0 17.0 22.8 24.0 23.0 13.0 18.0 21.0

R2 0.763 0.536 0.534 0.630 0.716 0.690 0.868 0.645

21



22

TABLE 4 Mean Errors for the Developed Models


Model Number 1 2 3 4 5 6 7 8

Absolute Average Errors 4.46% 4.37% 7.77% 9.21% 4.71% 8.64% 2.20% 9.89%

Total Average Error

6.40%

22



23

Group 1

Group 2

3

3.5

2.5

3 2.5

1.5

IRI

IRI

2 1

2 1.5

0.5

1

0

0.5

0

2

4

6

8

10

12

14

16

0

18

0

Age

5

10

Sigmoidal Model

Validation

Model

Group 3 3

2.5

2.5

30

Validation

IRI

1.5

1

1

0.5

0.5 0

0

5

10

15

20

25

0

30

5

10

15

Sigmoidal Model

Validation

Sigmoidal Model

Group 5 3

IRI

2 1.5 1 0.5 0 5

10

25

30

Validation

Group 6

2.5

0

20

Age

Age

IRI

25

2

1.5

0

15

20

25

3.5 3 2.5 2 1.5 1 0.5 0 0

30

5

10

15

20

25

Age

Age Sigmoidal Model

Sigmoidal Model

Validation

Validation

Group 8

Group 7 3

3

2.5

2.5

2 IRI

2 IRI

20

Group 4

3 2 IRI

15 Age

1.5

1.5

1

1

0.5

0.5 0

0 0

5

10

15

20

0

5

10

15

20

25

Age

Age Sigmoidal Model

25

Validation

Sigmoidal Model

Validation

FIGURE 6 Illustration of the validation points for the eight groups.


23


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