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Publication Number: FHWA-HRT-06-109
Date: September 2006

Improving Pavements With Long-Term Pavement Performance: Products for Today and Tomorrow

Studyof Long-Term Pavement Performance (LTPP): Pavement Deflections

PAPER 4: assessment of overlay roughness in the LTPP—a Canadian case study

James T. Smith, B.A.Sc.
University of Waterloo
Waterloo, Ontario, Canada,

Faculty Advisor
Susan L. Tighe, Ph.D., P.E.
University of Waterloo
Waterloo, Ontario, Canada

ABSTRACT

This paper studies asphalt pavement overlay performance in the Canadian environment. It investigates the impact of asphalt overlay thickness, climatic zone, and subgrade type on the progression of roughness as described by the International Roughness Index (IRI). Data from the Canadian Long-Term Pavement Performance (LTPP) program test sites were analyzed. Through the investigation, pavement factors that significantly impact overlay performance in the Canadian environment can be identified.

Data collected over the first 13 years of study were used to show national and provincial roughness trends from 53 test sites. The IRI data were statistically summarized (mean, standard deviation) for each category by the age of the overlay section. Using the summarized data, regression analysis was used to determine an equation that best describes the progression of roughness. Two-factor analysis of variance was used to determine any significant differences within specific categories. The results of the regression analysis were compared to the Canadian Strategic Highway Research Program (C–SHRP) LTPP data to confirm the validity of the roughness progression equations.

Results show that overlay thickness and climatic zones significantly impact roughness, while subgrade type has little influence on the IRI values. The roughness progression equations achieved squared correlation coefficients (R2) between 0.93 and 0.39, demonstrating the accuracy of the model equations.

INTRODUCTION

In 1987, as part of a comprehensive 20-year study of inservice pavements, the Long-Term Pavement Performance (LTPP) program was initiated. The purpose of this program was to develop an understanding of why some pavements perform better than others and how to maintain a cost-effective highway system.(1) The LTPP program monitors 107 test sections in Canada. At each site, data on distress, roughness, structural capacity, traffic, and other pavement performance measures were collected.

The individual test sites are identified by a climatic zone, overlay thickness, and subgrade. Figure 1 and Table 1 shows the distribution and identification numbers of the 53 test sites in the LTPP study that have overlays and are included in the data analysis for this paper.

The International Roughness Index (IRI) is a measurement scale to evaluate pavement roughness. The index is based on the result from a response-type road roughness measuring system (RTRRMS) to the longitudinal profile of the road surface. The profile captures the movement between the axle and vehicle body in response to the motion of the vehicle traveling down the pavement surface at 80 kilometers per hour (km/h).(2)

IRI is measured in units of meters per kilometer (m/km). An absolutely perfect pavement profile, one with no vertical displacements, has an IRI value equal to 0 m/km. As the index value increases, the smoothness of the road decreases. There is no maximum limit to the scale; however, IRI values greater than 8 m/km are classified as damaged pavements or rough, unpaved roads. At an IRI value greater than 2.15 m/km, pavements are in poor condition and become uncomfortable at speeds greater than 80 km/h.

Figure 1. Diagram. Distribution of L T P P test sites. Diagram showing the political boundaries of Canada sectioned off into test site zones, indicating the respective number of test sites in each zone.

Figure 1. Distribution of LTPP test sites.

Table 1. LTPP test site identification numbers.
BC AB SK MB ON QC NB PE NS NF
82-1005 81-502 90-6405 83-502 87-1620 89-1021 84-1684   86-6802  
82-6006 81-503 90-6410 83-503 87-1622 89-1125 84-6804      
82-6007 81-504 90-6412 83-504 87-1680 89-1127        
  81-505 90-6420 83-505 87-1806 89-9018        
  81-506 90-6801 83-506 87-2811 89-A310        
  81-507 90-A310 83-507 87-2812          
  81-508 90-B310 83-508 87-A310          
  81-509   83-509 87-A311          
  81-1804   83-3802 87-B310          
  81-1895   83-6450 87-B311          
  81-8529   83-6451            
      83-6452            
      83-6454            
      83-A310            

SCOPE AND OBJECTIVES

This paper analyzes the relationship between pavement performance measured by IRI and the age of the asphalt pavement overlay. All data used in the analysis was extracted from the LTPP Information Management System’s DataPave Online Release 15.

Based on this extraction, the following analysis was carried out:

Overall, the paper is directed at determining the pavement factors with the most significant impact on overlay performance in the Canadian environment.

IMPORTANCE OF IRI AS A PAVEMENT INDICATOR

Pavement roughness is the primary measure most transportation agencies use to establish the need for rehabilitation. Pavement roughness affects driving comfort, vehicle operating costs, and safety.(3) ASTM International (originally known as the American Society for Testing and Materials) defines pavement roughness as “the deviation of the surface from a true planar surface with characteristic dimensions that affect vehicle dynamic, ride quality, dynamic loads, and drainage.”

Methodology for Analysis

Two-way analysis of variance (ANOVA) was used to compare population means based on a simple random sample (SRS). Each population is assumed to be normal, possibly with different means and the same standard deviation. ANOVA separates the total variation of the data into variation between group means and variation within groups. The null hypothesis (Ho) states that the population means are equal. The alternative hypothesis (Ha) is true if there is any difference between the population means. If the variation between groups is large compared to the variation within the groups, there is evidence against the null hypothesis.(4)

Analysis was conducted using a four-step process:

NATIONAL ROUGHNESS TREND

Figure 2 is a boxplot that shows the national roughness trend from 1989 to 2002 for all Canadian test site overlays in the LTPP program. For each year, six measures are used to describe roughness. Minimum and maximum IRI values are used to illustrate the best and worst performing test sections. The mean and median are also given to describe the IRI distribution. The first and third quartiles, which capture 50 percent of the test population, are provided to show the divergence of IRI values from the median value.

Figure 2. Graph. National I R I trends. Graph showing the I R I in meters per kilometer graphed against the overlay age in years for the entire country. The graph indicates a positive, gradually increasing trend with the R squared, mean, median, minimum, maximum, quartiles, and slope values provided.

Figure 2. National IRI trends.

Figure 2 shows an increase in the overlay roughness during the study period. Over the period, the average IRI measured increased from 1.031 m/km to 1.630 m/km, representing a pavement that is still smooth and functioning properly. The difference between the first and third quartiles remained fairly constant, approximately 0.4 m/km. National roughness progression is best explained using an exponential regression, as shown by equation 1, and accurately predicts the LTPP IRI data as evidence of the squared correlation coefficient (R2) equal to 0.9467. Using an IRI trigger level of 2.15 m/km for maintenance, rehabilitation, and reconstruction (MR&R), the average lifespan for overlays in Canada is 25 years. Note that all factors have been aggregated in this analysis. However, the results show that overlays can provide good performance.

Equation 1. Equation. I R I is equal to the product of 0.9971 and the exponential function E raised to the power of 0.0314 times Age. (1)

PROVINCIAL ROUGHNESS TREND

Figure 3 shows the boxplot roughness trends for the province of Ontario. There are 6 sites and 10 test sections included in this analysis.

The provincial roughness trend shows a constant increase in the average IRI value similar to the national roughness trend. The average IRI changed from 1.076 m/km to 1.852 m/km in 8 years. The difference between the first and third quartiles did not remain constant over the study period, varying between 0.055 m/km to 0.676. Ontario roughness progression is best explained using an exponential regression equation shown by equation 2. The high R2 value of 0.9195 shows that equation 2 accurately predicts the LTPP IRI data. An overlay in the province of Ontario has a useful life of approximately 12 years. This significantly lower service life compared to the national value could be attributed to Ontario’s high traffic loads and extreme climate. Both factors could result in decreased service life because of traffic- and environment-related distresses.

Equation 2. Equation. I R I is equal to the product of 0.9621 and the exponential function E raised to the power of 0.0671 times Age. (2)

Figure 3. Graph. Ontario I R I Trends. Graph showing the I R I in meters per kilometer graphed against the overlay age in years for Ontario. The graph indicates a positive, gradually increasing trend with the R squared, mean, median, minimum, maximum, quartiles, and slope values provided.

Figure 3. Ontario IRI trends.

OVERLAY THICKNESS EFFECTS

Overlay thickness is a primary consideration for pavement designers. Figure 4 illustrates the effects of overlay thickness on the progression of roughness. The overlays were divided into three categories, thin (30 to 60 mm), medium (60 to 100 mm), and thick (100 to 185 mm). Overall, this resulted in six thin overlays, two medium overlays, and three thick overlays.

During the first 8 years, the thin overlay had the greatest increase in roughness, while the changes for moderate and thick overlays remained almost identical. After the eighth year, the deterioration rate for the moderate overlay thickness was accelerated while the thick overlay increased gradually. Table 2 presents the results of the regression analysis and approximate lifespan of the overlay for all three overlay categories.

Figure 4. Graph. Effect of overlay thickness on roughness progression in wet-freeze climatic zones with fine-grained subgrades. Vertical bar chart showing the I R I in meters per kilometers on the vertical axis and the overlay age in years on the horizontal axis. As the overlay age increases the I R I tends to increase slightly.

Figure 4. Effect of overlay thickness on roughness progression in wet-freeze climatic zones with fine-grained subgrades.

As the thickness of the overlay increases, the structural capacity of the pavement increases. This allows the pavement to resist deterioration and produce lower roughness values. The results must be interpreted with caution, however, because thickness cannot increase indefinitely. Correct compaction cannot be achieved if the overlay thickness becomes excessive, resulting in accelerated roughness progression due to early failure of the pavement structure.

Table 2. Overlay regression analysis.
Pavement Class Regression Equation R2 NOBS. Life (Years)
Wet-freeze / Fine grained / Thin y = 0.0059x2 + 0.033x + 1.0147 0.9124 41 12
Wet-freeze / Fine grained / Medium y = 0.0059x2 + 0.033x + 1.0147 0.9719 25 16
Wet-freeze / Fine grained / Thick y = 0.8732e0.0429x 0.7949 37 21

A two-factor ANOVA analysis conducted at an a level of 0.05 or 95 percent showed a significant change in the IRI values over time for all overlay thicknesses. The results from the ANOVA analysis are presented in table 3.

Table 3. Overlay thickness ANOVA analysis.
Overlay Thickness df FCalculated FCritical Significant
Thin and Medium 1,6 7.74 5.99 Yes
Thin and Thick 1,5 13.66 6.61 Yes
Medium and Thick 1,12 12.62 4.75 Yes

CLIMATIC ZONE EFFECTS

The three climatic zones presented on the LTPP sites are wet-freeze (WF), wet-no freeze (WNF), and dry-freeze (DF). This analysis is intended to isolate the impact of climatic zone on performance. Overall, this resulted in 10 wet-freeze climatic zones, 2 wet-no freeze climatic zones, and 2 dry-freeze climatic zones. Figure 5 presents the relationship between climatic zone and roughness progression.

Figure 5. Graph. Effect of climatic zone on roughness progression in thin overlays with coarse-grained subgrades. Vertical bar chart showing the I R I in meters per kilometers on the vertical axis and the overlay age in years on the horizontal axis. As the overlay age increases the I R I tends to increase slightly. Wet-freeze, Wet-No freeze, and Dry-freeze relationships are all indicated in the graph.

Figure 5. Effect of climatic zone on roughness progression in thin overlays with coarse-grained subgrades.

The roughness values varied widely from one year to another. This is possibly explained by the time of year the roughness data were collected. However, regardless of the time of year, dry-freeze zones exhibited the poorest performance in this study. Table 4 presents the results of the regression analysis and approximate lifespan of the overlay for the three climatic zone categories.

Roughness progression is accelerated by freeze-thaw effects and trapped water. This effect is shown by the wet-no freeze zone being the best performing zone. The presence of water did not affect roughness progression because it does not freeze in this zone and cause additional stress to and deterioration of the pavement structure.

Table 4. Climatic zone regression analysis.
Pavement Class Regression Equation R2 NOBS. Life (Years)
Wet-freeze / Coarse grained / Thin y = 0.0355x + 0.9319 0.7789 72 34
Wet-no freeze / Coarse grained / Thin y = -0.0007x2 + 0.0651x + 0.7301 0.8218 17 35
Dry-freeze / Coarse grained / Thin y = 0.0050x2 - 0.0156x + 0.9474 0.9475 22 18

A two-factor ANOVA was performed to determine if the differences between the climatic zones for thin overlays on coarse-grained subgrade were statistically significant. Two-factor ANOVA analysis conducted at an a level of 0.05 showed a significant change in the IRI values over time between the wet-freeze and wet-no freeze, dry-freeze and wet-freeze climatic zones. The difference between the wet-no freeze and dry-freeze climatic zones is not statistically significant. The results from the ANOVA analysis are presented in table 5.

Table 5. Climatic zone ANOVA analysis.
Climatic Zone df FCalculated FCritical Significant
Wet-freeze and Wet-no freeze 1,5 8.13 6.61 Yes
Wet-freeze and Dry-freeze 1,5 7.19 6.61 Yes
Wet-no freeze and Dry-freeze 1,9 0.81 5.12 No

SUBGRADE EFFECTS

The next analysis in this research focused on examining subgrade type on pavement performance. Two categories of subgrade were used, coarse and fine. Coarse-grained subgrades are composed of sands and gravels, whereas fine-grained subgrades are composed of silts and clays.(5) Overall, this resulted in two fine-grained subgrades and four coarse-grained subgrades. Figure 6 compares the effect that the roadway subgrade has on the progression of roughness.

Figure 6. Graph. Effect of subgrade type on roughness progression in medium overlays with wet-freeze climatic zones. Vertical bar chart showing the I R I in meters per kilometers on the vertical axis and the overlay age in years on the horizontal axis. As the overlay age increases the I R I tends to increase slightly. Fine-grained and coarse-grained subgrade types are indicated in the graph.

Figure 6. Effect of subgrade type on roughness progression in medium overlays with wet-freeze climatic zones.

The progression of roughness for both coarse and fine subgrades is similar. During the first 8 years, the overlay with fine-grained subgrades preformed better than coarse-grained subgrades. This trend was reversed during the second half of the life cycle. Table 6 presents the results of the regression analysis and approximate lifespan of the overlay for the two subgrade categories.

Table 6. Subgrade regression analysis.
Pavement Class Regression Equation R2 NOBS. Life (Years)
Wet-freeze / Fine grained / Medium y = 0.0054x2 - 0.0061x + 1.0273 0.9719 25 15
Wet-freeze / Coarse grained / Medium y = 0.0050x2 - 0.0254x + 1.2357 0.9862 41 17

Although the effect of subgrade type produced similar results for pavements in a wet-freeze climatic zone and with medium overlay thickness, special attention must be made to match the subgrade to the environmental conditions. Pavements in areas susceptible to frost should avoid fine-grained subgrades because of the problems associated with continuous freeze-thaw effects.

Two-factor ANOVA analysis conducted at an a level of 0.05 showed no significant change in the IRI values over time when comparing coarse- and fine-grained subgrades. The results from the ANOVA analysis are presented in table 7.

Table 7. Subgrade ANOVA analysis.
Subgrade Type df FCalculated FCritical Significant
Fine grained and Coarse grained 1,12 0.87 4.75 No

ROUGHNESS and C–SHRP

The Canadian Strategic Highway Research Program (C–SHRP) LTPP program began in 1989, 2 years after the start of the LTPP program. The goal of the C–SHRP LTPP experiment was to build on the LTPP program, focusing on inservice pavement performance of rehabilitated pavements over 15 years at the national and provincial levels.(6)

The roughness deterioration equations from the LTPP study were validated by comparing them to the roughness data taken from the C–SRHP LTPP study and the R2 calculated.(7)

Figure 7 presents the comparison between the two LTPP studies for thin overlays in a wet-freeze fine-grained subgrade. The second-degree polynomial regression equation accurately predicts the C–SHRP roughness deterioration with a R2 of 0.9346.

Equation 3. Equation. I R I is equal to the sum of the product of 0.0059 and Age squared, plus the product of 0.033 times Age, plus 1.0147. (3)

Figure 7. Graph. Roughness progression of thin overlays in wet-freeze climatic zones with fine-grained subgrades. Vertical bar chart showing the I R I in meters per kilometers on the vertical axis and the overlay age in years on the horizontal axis. As the overlay age increases the I R I tends to increase slightly. L T P P, C dash S H R P, and the L T P P regression equation are indicated in the graph.

Figure 7. Roughness progression of thin overlays in wet-freeze climatic zones with fine-grained subgrades.

Figure 8 illustrates the comparison between the two LTPP studies for moderate overlays in a wet-freeze fine-grained subgrade. The second-degree polynomial regression equation accurately predicts the C–SHRP roughness deterioration with a R2 of 0.7676.

Equation 4. Equation. I R I is equal to the sum of the product of 0.0054 and Age squared, minus the product of 0.0061 times Age, plus 1.0273. (4)

Figure 8. Graph. Roughness progression of medium overlays in wet-freeze climatic zones with fine-grained subgrades. Vertical bar chart showing the I R I in meters per kilometers on the vertical axis and the overlay age in years on the horizontal axis. As the overlay age increases the I R I tends to increase slightly. L T P P, C dash S H R P, and the L T P P regression equation are indicated in the graph.

Figure 8. Roughness progression of medium overlays in wet-freeze climatic zones with fine-grained subgrades.

Figure 9 shows the comparison between the two LTPP studies for moderate overlays in a wet-freeze fine-grained subgrade. The second-degree polynomial regression equation moderately predicts the C–SHRP roughness deterioration with a R2 of 0.3879.

Equation 5. Equation. I R I is equal to the product of 0.8732 and the exponential function E raised to the power of 0.0429 times Age. (5)

Figure 9. Graph. Roughness progression of thick overlays in wet-freeze climatic zones with fine-grained subgrades. Vertical bar chart showing the I R I in meters per kilometers on the vertical axis and the overlay age in years on the horizontal axis. As the overlay age increases the I R I tends to increase slightly. L T P P, C dash S H R P, and the L T P P regression equation are indicated in the graph.

Figure 9. roughness progression of thick overlays in wet-freeze climatic zones with fine-grained subgrades.

CONCLUSIONS

The LTPP experiment data represents only the first 13 years of testing. The major conclusions and findings to date can be summarized as follows:

  1. The progression of roughness on a national level increases steadily over time and is best explained using an exponential regression equation.
  2. Provincial trends follow the same trends as the national average.
  3. Overlay thickness and climatic zones significantly affect roughness.
    1. As the thickness of the overlay increased, the pavement performance increased in areas characterized by wet-freeze climates and fine-grained subgrade.
    2. Wet-no freeze climatic regions had the best pavement performance, while dry-freeze regions performed the worst for test sections with a thin overlay and coarse-grained subgrade.
  4. Subgrade type has little influence on the IRI values for asphalt overlays.
  5. Asphalt overlays in Canada should have a lifespan of 12 to 35 years.
  6. LTPP regression equations adequately explain roughness progression for the C–SHRP sites.

RECOMMENDATIONS

This study gives an initial look at the performance of overlay roughness for Canadian LTPP test sites. Further study would be beneficial in the following areas:

  1. Continue study for the remaining study period.
  2. Create smaller subcategories to better describe the test section properties.
  3. Investigate the potential for other performance factors (overlay type, traffic level, etc.) influencing roughness.
  4. Develop a single equation to explain roughness progression that accounts for all performance factors.

REFERENCES

  1. Federal Highway Administration. (2003). DataPave Online, Long-Term Pavement Performance Program, at http://www.datapave.com.

  2. Sayers, M.W., Gillespie, T.D., and Patterson, W.D. (1986). Guidelines for the Conduct and Calibration of Road Roughness Measurement. World Bank Technical Paper 46. The World Bank, Washington, DC.

  3. Transportation Association of Canada. (1997). Pavement Design and Management Guide. Ottawa, Canada.

  4. Moore, D., and McCabe, G. (1996). Introduction to the Practice of Statistics (2nd ed.). New York: W.H. Freeman & Co.

  5. Craig, R.F. (1997). Craig’s Soil Mechanics (6th ed.). New York: E & FN Spon.

  6. Tighe, S., Haas, R., and Li, N. (2001). Overlay Performance in the Canadian Strategic Highway Program’s LTPP Study. Transportation Research Board, Washington, DC.

  7. Haas, R., Li, N., and Tighe, S. (1999). Roughness Trends at CSHRP LTPP Sites. Canadian Strategic Highway Research Program, Ottawa, Canada.

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The Federal Highway Administration (FHWA) is a part of the U.S. Department of Transportation and is headquartered in Washington, D.C., with field offices across the United States. is a major agency of the U.S. Department of Transportation (DOT). Provide leadership and technology for the delivery of long life pavements that meet our customers needs and are safe, cost effective, and can be effectively maintained. Federal Highway Administration's (FHWA) R&T Web site portal, which provides access to or information about the Agency’s R&T program, projects, partnerships, publications, and results.
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