An Investigation of Factors Influencing Permeability o

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AN INVESTIGATION OF FACTORS INFLUENCING PERMEABILITY OF SUPERPAVE MIXES Mohd Rosli Hainin (Corresponding Author) Graduate Research Assistant National Center for Asphalt Technology 277 Technology Parkway Auburn, AL 36830 Phone: (334) 844-6228 Fax: (334) 844-6248 Email: [email protected] L. Allen Cooley, Jr. Manager, Southeastern Superpave Center National Center for Asphalt Technology 277 Technology Parkway Auburn, AL 36830 Phone: (334) 844-6228 Fax: (334) 844-6248 Email: [email protected] Brian D. Prowell Assistant Director National Center for Asphalt Technology 277 Technology Parkway Auburn, AL 36830 Phone: (334) 844-6228 Fax: (334) 844-6248 Email: [email protected] Word Count:

Abstract: 249 Text: 2968

3 Tables and 14 Figures: 4250 Total Words: 7467

Submission Date: August 1, 2002 Submitted for Presentation and Publication at the 82nd Annual Meeting of Transportation Research Board, January 2003

TRB 2003 Annual Meeting CD-ROM Paper revised from original submittal.

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An Investigation of Factors Influencing Permeability of Superpave Mixes

Abstract Permeability has become a continuing issue discussed in the hot mix asphalt (HMA) community, especially with the introduction of Superpave mixes in late 1990s. Permeability can cause an increased potential for oxidation, raveling, cracking, and water damage in HMA pavements. The objective of this study is to evaluate the permeability characteristics of Superpave pavements and determine the influence of in-place density, nominal maximum aggregate size (NMAS), gradation, lift-thickness, and design compactive effort (Ndes) on the permeability of these pavements. A total of 354 cores were obtained from 42 different Superpave projects immediately after paving. Five different mix types utilized in this study were fine-graded 9.5 mm, 12.5 mm, and 19.0 mm NMAS mixes and coarse-graded 9.5 mm and 12.5 NMAS mixes. Bulk specific gravity of cores was determined using AASHTO T 166 and a vacuum sealing method (Corelok). The permeability test was performed according to ASTM PS129-01. The results of the investigation indicate that in-place void content is the most significant factor impacting permeability of Superpave pavements. Air voids determined using vacuum sealing method (Corelok) has more impact on permeability than AASHTO T 166 method. This is followed by coarse aggregate ratio, percent passing 12.5 mm sieve, percent passing 1.18 mm sieve, Ndes, and, lift thickness. As the values of coarse aggregate ratio, percent passing 12.5 and 1.18 mm sieve, and Ndes increase, permeability increases. For coarse-graded mixes, as the coarse aggregate ratio approaches 1.0 or higher, permeability increases significantly. Permeability decreases as lift thickness increases.


An Investigation of Factors Influencing Permeability of Superpave Mixes INTRODUCTION Permeability is defined as the ability of fluid to infiltrate into a porous medium, and it can cause an increased potential for oxidation, raveling, cracking, and water damage in hot mix asphalt (HMA) pavements (1, 2, 3). Kumar et al. (2) have noted that permeability gives a better indication of a pavement’s long-term durability than air voids. Gotolski et al (3) and Maupin (4) have suggested using permeability as a mix design requirement for HMA mixes.

Permeability has become a continuing issue discussed in the HMA community, especially with the introduction of Superpave mixes in late 1990s. A survey by Brown et al. (5) suggested that coarse-graded Superpave mixes seem to be more permeable than conventional dense-graded mixes at similar air void contents. Work by Westermann (6) and Choubane et al. (7) using a laboratory permeability device showed that coarse-graded Superpave mixes became permeable when the air void contents were more than 6 percent. A survey by Choubane (7) also revealed that the problems encountered with coarse-graded Superpave mixes resulted from the size and the interconnectivity of the air voids instead of the total volume of air voids. Lack of fine materials resulted in more air voids in the mixes and increased the amount of interconnected voids. This finding agreed with results of an investigation by Hall et al. (8).

There are several factors influencing the interconnectivity of the air voids. Recent work by Mallick et al. (9) and Cooley et al. (10) showed that nominal maximum aggregate size (NMAS) has a great influence on the permeability characteristics of a pavement. By increasing the NMAS, the size of the individual air voids increases which results in higher potential for interconnected air voids. The findings also showed that gradation shape affects the size of voids in the pavements. The coarser the gradations, the larger the individual air voids; thus, there is a higher potential for interconnected air voids. A study by Tan et al. (11) also revealed similar results. Mallick et al. (9) also investigated the effect of lift thickness on permeability. The result suggested that as lift thickness increases permeability decreases. A thicker lift thickness reduces the chance of interconnected voids. However, studies by Mallick et al. (9) and Cooley et al. (10) were based on values from a field permeability device and the lift thickness samples were laboratory compacted.

OBJECTIVE The objective of this study was to perform laboratory permeability testing on cores obtained from numerous coarse- and fine-graded Superpave designed pavements and to determine the influence of in-place density, NMAS, gradation, lift-thickness, and design compactive effort (Ndes) on the permeability of these pavements. TEST PLAN In this study, 42 on-going HMA construction projects were visited. Five different types of mixes were studied: fine-graded 9.5 mm, 12.5 mm, and 19.0 mm NMAS mixes and coarse-graded 9.5 mm and 12.5 mm NMAS mixes. At each of the projects, cores were obtained from the roadway so that the actual placed lift thickness and in-place density could be determined. Plant produced mix was also sampled at each project in order to determine the theoretical maximum density (TMD) and the project gradation. Cores brought to laboratory were sawed and tested for bulk specific gravity (both AASHTO T 166 and the vacuum


Hainin, Cooley, and Prowell 2

sealing method), thickness and laboratory permeability. The permeability of the cores was determined using the laboratory permeameter and method of ASTM PS 129-01, Standard Provisional Test Method for Measurement of Permeability of Bituminous Paving Mixtures Using a Flexible Wall Permeameter. MATERIALS AND METHODS A total of 354 cores were obtained from 42 different Superpave projects. Cores were cut from the roadway at each location prior to traffic. Information about the projects is presented in Table 1. Of the 42 projects, 13 projects utilized 9.5 mm NMAS gradation, 26 projects utilized 12.5 mm NMAS gradation, and 3 projects utilized 19.0 mm NMAS gradation. Gradations for all the mixes are illustrated in Figures 1 through 3, by NMAS from 9.5 to 19.0 mm. Average lift thickness ranged from 22.3 to 78.8 mm. The calculated lift thickness to NMAS ratio (t/NMAS) ranged from 1.74 to 6.31 and the Ndesign ranged from 50 to 125 gyrations with a Superpave gyratory compactor. The height of each core was measured and the bulk specific gravity was determined in accordance with AASHTO T-166. As shown in Table 1, the in-place air void contents of the cores ranged from 3.7 to 19.8 percent. As a comparison, the bulk specific gravity of the cores was also measured with CorelokTM device. The in-place air void contents ranged from 3.5 to 21.3 percent. To calculate the in-place air void content, the TMD test was performed on the plant produced mix according to ASTM D 2041.

Laboratory permeability test was conducted on each core in accordance with ASTM PS 129-01, Standard Provisional Test Method for Measurement of Permeability of Bituminous Paving Mixtures Using a Flexible Wall Permeameter. This method utilizes a falling head approach in measuring permeability. Each core was vacuum-saturated for five minutes prior to testing. Water from a graduated standpipe is allowed to flow through the saturated sample and the time to reach a known change in head is recorded. Saturation is considered sufficient when four consecutive time interval measurements do not differ by more than 10 percent of the mean. In this method, Darcy’s law is then applied to determine the permeability of the sample. TEST RESULTS AND ANALYSIS The gradation type, Ndes, average thickness, average in-place air void content (AASHTO T 166) and average laboratory permeability for all projects are shown in Table 1. Figure 4 illustrates the relationship between in-place air voids and permeability for all projects. In this figure, the y-axis was reduced to show a clearer relationship. The largest permeability value is 12,800 x 10-5 cm/s. The data shows that as the in-place air voids increase the permeability increases. There is a relatively strong relationship between in-place air voids and permeability with an R2 value of 0.60. Based on the trend line, permeability is very low at air void content less than 6 percent. The permeability starts to increase at a greater rate with changes in in-place air voids from 6 to 7 percent voids. The figure also shows that the pavements become excessively permeable at approximately 8.0 to 8.5 percent air voids Figures 5 through 7 presents the plots of in-place air voids versus permeability for each NMAS mix. The relationship between in-place air voids and permeability for the 9.5 mm NMAS mix is illustrated in Figure 5. Both coarse-graded and fine-graded mixes show a relatively strong relationship with R2 values of 0.58 and 0.76, respectively. For both gradations, the permeability begins to increase at greater rate at approximately 9.0 percent air



voids. At this air void content, the pavement is expected to have a permeability of 200 x 10-5 cm/sec for coarse-graded mix and 50 x 10-5 cm/sec for fine-graded mix. However, at air voids larger than 11 percent, the fine-graded mix becomes more permeable than coarse-graded mix. The relationship for the coarse-graded and fine-graded 12.5 mm NMAS mixes is shown in Figure 6. The correlation between in-place air voids and permeability for both gradations is relatively strong with R2 of 0.68 for coarse-graded mix and 0.58 for fine-graded mix. Similar to Figure 5, the permeability starts to increase at a greater rate at approximately 8.0 percent air voids. The permeability at 8.0 percent air voids for coarse-graded and fine-graded mixes were approximately 80 x 10-5 and 50 x 10-5, respectively.

Figure 7 illustrates the relationship between in-place air voids and permeability for fine-graded 19.0 mm NMAS mix. The R2 value for this mix was 0.65. Based on the trendline, permeability is very low at air void contents less than 8.0 percent. At air void contents above 8.0 percent, the permeability begins to increase with a small increase in in-place air void content. Figure 7 also suggests that the fine-graded 19.0 mm NMAS mix is the least permeable compared to fine-graded 9.5 and 12.5 mm NMAS mixes at similar in-place air void content. At 8.0 percent air voids, the fine-graded 19.0 mm NMAS mix has a permeability value of 17 x 10-5 cm/sec compared to fine-graded 9.5 and 12.5 mm NMAS mixes which have permeability values of 25 x 10-5 and 50 x 10-5 cm/sec, respectively. This can be likely be explained in that 19 mm NMAS samples have higher average lift thickness than 9.5 mm NMAS samples and the 9.5 mm NMAS samples have higher percentage of fine materials (passing 4.75 mm sieve) and lower Ndes than 12.5 mm NMAS samples. A thicker HMA layer reduces the chance of interconnected voids and also as Ndes decreases, optimum binder content increases and, thus, creates lower potential for interconnecting voids.

In order to evaluate the interrelatedness between the influencing factors and permeability, a multiple linear regression (MLR) was performed. This procedure was conducted to identify factors most affecting permeability. A stepwise regression (forward and backward method) was also utilized to evaluate all independent variables and select the variables that provide the most significant relationship with the dependent variable (permeability). The stepwise regression procedure allows the user to input numerous factors that have the potential to impact the dependent parameter. For this analysis, the natural log of permeability was the response, while natural log of in-place air voids from AASHTO T 166 and Corelok, NMAS, Ndes, average sample thickness, the percent passing each sieve from the 19.0 mm to the 0.075 mm, the fineness modulus, the surface area of each gradation, the percent coarse aggregate in the blend, the percent fine aggregate in the blend, and the coarse aggregate ratio were included as the predictors. The gradation data was included to differentiate between different gradations, as a regression requires quantitative variables. The coarse aggregate ratio is defined as the percent retained on the 4.75 mm sieve divided by the percent passing the 4.75 mm sieve. Therefore, this property indicates whether a gradation is coarse or fine-graded.

The first MLR performed indicated that the natural log of air voids (Corelok) has more impact on permeability than the natural log of air voids (T 166). Thus, the natural log of air voids (T 166) was omitted in the second MLR. Factors having a significant impact on permeability identified by the MLR were the natural log of air voids (Corelok), coarse aggregate ratio, percent passing the 12.5 mm sieve, percent passing the 1.18 mm sieve, Ndes, percent passing 0.6 mm sieve, average sample thickness and percent passing 19 mm sieve.



The selected factors are interesting in that NMAS was not among the factors identified as affecting permeability. However, this can likely be explained in that of the 42 projects included in this study, 39 had either a 9.5 or 12.5 mm NMAS. Therefore, there was little variation in this NMAS that would cause it not to be identified during the stepwise procedure. After conclusion of the stepwise procedure, the identified factors were regressed versus the natural log of permeability. The percent passing 0.6 mm sieve was removed from the analysis because it has small impact on permeability (P-value more than 0.05). The percent passing 19 mm was also not included because of 42 projects, only 3 projects using mixes having 19 mm NMAS. Therefore, a little variation in percent passing 19 mm sieve causing it to be identified by the MLR. Results of the stepwise regression and the regression are presented in Table 2. Based on the t-value, the most significant factor impacting permeability is the natural log of air voids (Corelok). This followed by the coarse aggregate ratio, the percent passing 12.5 mm sieve, the percent passing 1.18 mm sieve, the Ndes, and the average sample thickness. Following is the regression equation:

Ln (k) = -19.2 + 5.96Ln(CL) + 1.47(CA Ratio) + 0.078(P12.5) + 0.0485(P1.18) + 0.00928(Ndes) - 0.0124(Ave. Thickness)

Where, Ln (k) = natural log of permeability Ln (CL) = natural log of air voids from Corelok CA Ratio = coarse aggregate ratio P12.5 = percent passing 12.5 mm sieve P1.18 = percent passing 1.18 mm sieve Ndes = Design compactive effort of mix design Ave. Thickness = Average thickness of a given core (lift thickness at location of core)

There was a good correlation for the above equation with an R2 of 0.76. This model was used to show how these factors affect permeability, as presented in Figures 8 through 14. Table 3 shows the selected constant values were used for the factors when calculating the permeability at each given air void content. Figure 8 illustrates the effect of air voids (from Corelok) on permeability. Permeability increases as air voids increases. The regression lines in Figure 8 also present the effect of thickness on permeability at given air void contents. The constant values were 1.0 for coarse aggregate ratio, 95.0 percent for percent passing the 12.5 mm sieve, 25.0 percent for percent passing the 1.18 mm sieve, and 100 gyrations for Ndes. The relationship shows that as the thickness of placed layer placed increases, permeability decreases for all void contents. A thicker HMA layer reduces the chance of interconnected voids.

Figures 9 to 14 illustrate the relationship between permeability and the other independent variables. The influence of Ndes on permeability is shown in Figure 9. The relationship shows that as Ndes increases, permeability increases. As would be expected, as Ndes increases, optimum binder content decreases and, thus, creates higher potential for interconnecting voids. A constant value for the average thickness was 50 mm and the same values as in Figure 8 were used for other variables.

Since coarse aggregate ratio, percent passing the 12.5 mm sieve, and percent passing the 1.18 mm sieve are different for each mix, the relationship between permeability and coarse aggregate ratio is presented for each mix as shown Figures 10 through 14. Using the



corresponding values of the percent passing 12.5 mm and 1.18 mm sieve at a given coarse aggregate ratio and the same constant values used for other variables as in Figures 8 and 10, the permeability was determined. The constant values for each mix are presented in Table 3.

Figure 10 illustrates the relationship between permeability, coarse aggregate ratio, and in-place air voids for coarse-graded 9.5 mm NMAS mix. The result shows that as the mix gets coarser resulted in increased permeability. The coarser the gradations, the larger the individual air voids which lead to higher potential for interconnected voids. The relationship between permeability, coarse aggregate ratio, and in-place air voids for fine-graded 9.5 mm NMAS mix is illustrated in Figure 11. The regression lines suggest that as coarse aggregate increases, the permeability also increases at a very slow rate.

Figure 12 illustrates the relationship for coarse-graded 12.5 mm NMAS mix. The figure shows that the permeability increases at a significant rate at coarse aggregate ratio of approximately 0.9. Figure 13 shows the relationship for fine-graded 12.5 mm NMAS mix. The result indicates that as the coarse aggregate ratio increases, permeability increases. Figure 14 shows the relationship for fine-graded 19 mm NMAS mix. Similar to Figure 11, the result suggests that as the coarse aggregate ratio increases, there is a small increase in the permeability.

CONCLUSIONS The objective of this study was to evaluate factors affecting permeability using a laboratory permeability device (ASTM PS 129-01). The results indicate that in-place void content is the most significant factor impacting permeability of Superpave pavements. Air voids determined using vacuum sealing method (Corelok) has more impact on permeability than air voids determined from AASHTO T 166. This is followed by coarse aggregate ratio, percent passing 12.5 mm sieve, percent passing 1.18 mm sieve, Ndes, and, lift thickness. As the values of coarse aggregate ratio, percent passing 12.5 and 1.18 mm sieve, and Ndes increase, permeability increases. For coarse-graded mixes, as the coarse aggregate ratio approaches 1.0 or higher, permeability increases significantly. Permeability decreases as lift thickness increases.



REFERENCES 1. Mullen, W.G. Beam Flexure and Permeability Testing of Bituminous Pavement

Samples. Proceeding of the Association of Asphalt Paving Technologists, Volume 36, 1967.

2. Kumar, A. and Goetz, W.H. Asphalt Hardening as Affected by Film Thickness,

Voids and Permeability in Asphaltic Mixtures. Proceedings of the Association of Asphalt Paving Technologists, Volume 46, 1977.

3. Gotolski, W.H., Roberts, J.M., Smith, R.W. and Ciesielski, C.A. Permeance as a Mix

Design Criterion for Asphaltic Concrete Pavements. Research Project 68-1, Pennsylvania Department of Transportation, 1972.

4. Maupin, G.W. Jr. Asphalt Permeability Testing in Virginia. Presented at the 79th

Annual Meeting of the Transportation Research Board, 2000.

5. Brown, E.R., D. Decker, R.B. Mallick, and J. Bukowski. Superpave Construction Issues and Early Performance Evaluations, Journal of the Association of Asphalt Paving Technologists, Volume 68, 1999.

6. Westerman, J.R. AHTD’s Experience with Superpave Pavement Permeability.

Presented at Arkansas Superpave Symposium, January 21, 1998. 7. Choubane, B. Gale, P.C. and Musselman, J.A. Investigation of Water Permeability of

Coarse Graded Superpave Pavements. Journal of the Association of Asphalt Paving Technologists, Volume 67, 1998.

8. Hall, K.D. and Ng, H.G. Development of a Void Pathway Test for Investigating Void

Interconnectivity in Compacted Hot Mix Asphalt Concrete. Presented at the 80th Annual Meeting of the Transportation Research Board, 2001.

9. Mallick, R.B., Cooley L.A. Jr., Teto, M.R., Bradbury R.L. and Peabody, D. An

Evaluation of Factors Affecting Permeability of Superpave Designed Pavements. Presented at the 80th Annual Meeting of the Transportation Research Board, 2001.

10. Cooley, L.A. Jr., Brown, E.R. and Maghsoodloo, S. Development of Critical Field

Permeability and Pavement Density Values for Coarse Graded Superpave Pavements. Presented at the 80th Annual Meeting of the Transportation Research Board, 2001

11. Tan, S.A., Fwa, T.F. and Chuai, C.T. Laboratory Evaluation of Clogging Potential of

Porous Asphalt Mix. Transportation Research Record 1681, 1999.



LIST OF TABLES AND FIGURES LIST OF TABLES TABLE 1 Project Mix Information TABLE 2 Results of Regression on Factors Impacting Permeability TABLE 3 Constant Values Used for Independent Variable to Calculate Permeability LIST OF FIGURES FIGURE 1 Plot of 9.5 mm NMAS gradations. FIGURE 2 Plot of 12.5 mm NMAS gradations. FIGURE 3 Plot of 19.0 mm NMAS gradations. FIGURE 4 Plot of permeability versus in-place air voids for all data. FIGURE 5 Plot of permeability versus in-place air voids for 9.5 mm NMAS mixes. FIGURE 6 Plot of permeability versus in-place air voids for 12.5 mm NMAS mixes. FIGURE 7 Plot of permeability versus in-place air voids for 19.0 mm NMAS mixes. FIGURE 8 Relationship between lab permeability, thickness, and in-place air voids. FIGURE 9 Relationship between lab permeability, Ndesign, and in-place air voids. FIGURE 10 Relationship between lab permeability, coarse aggregate ratio, and in-place air voids for coarse-graded 9.5 mm NMAS mix. FIGURE 11 Relationship between lab permeability, coarse aggregate ratio, and in-place air voids for fine-graded 9.5 mm NMAS mix. FIGURE 12 Relationship between lab permeability, coarse aggregate ratio, and in-place air voids for fine-graded 19.0 mm NMAS mix. FIGURE 13 Relationship between lab permeability, coarse aggregate ratio, and in-place air voids for coarse-graded 12.5 mm NMAS mix. FIGURE 14 Relationship between lab permeability, coarse aggregate ratio, and in-place air voids for fine-graded 12.5 mm NMAS mix.



Table 1 Project Mix InformationProject NMAS, Gradation Ndes Average Range Average Range Average Range of

No. mm Thickness Thickness, In-place of Voids, Lab. Permeability

mm mm Voids, % Permeability 10-5cm/sec

T 166, % 10-5cm/sec1 9.5 Coarse 86 34.3 31.3-36.9 8.1 7.3-9.8 74 9-2492 9.5 Coarse 90 40.5 32.6-44.2 9.5 6.5-10.7 468 10-8803 9.5 Coarse 90 44.5 38.9-46.7 9.1 8.2-10.6 214 63-3894 9.5 Coarse 105 45.7 38.7-50.7 8.3 6.8-9.5 242 60-4945 9.5 Coarse 50 31.2 16.4-47.7 16.3 14.6-19.8 2198 881-38666 9.5 Coarse 100 33.9 29.3-37.1 8.4 7.5-8.8 108 36-1667 9.5 Coarse 125 34.9 23.8-39 7.6 5.1-8.8 130 6-3428 9.5 Coarse 100 46.5 38.7-56.6 7.7 4.5-10.7 1045 21-35109 9.5 Coarse 75 42.0 32.2-47.9 11.2 8.1-16.1 914 136-3720

10 9.5 Coarse 100 44.1 34.2-52.8 9.9 7.0-12.2 606 23-161911 9.5 Fine 100 22.3 18.1-27.4 9.7 8.0-12.5 339 108-99812 9.5 Fine 75 40.5 37.2-44.5 7.1 5.5-7.8 6 1-1613 9.5 Fine 75 32.4 28.7-36.5 10.4 8.3-12.6 385 53-163114 19 Fine 95 33.0 17.7-47.5 8.4 7.2-10.7 12 2-2915 19 Fine 68 49.6 37.6-59.1 6.6 4.2-9.5 38 1-13216 19 Fine 96 48.7 40.4-52.7 7.0 6.2-7.6 12 4-2117 12.5 Coarse 106 39.9 39.1-40.6 11.6 11.6-11.7 453 275-63218 12.5 Coarse 100 42.4 39.0-48.1 12.5 11.3-14.1 5656 275-1280019 12.5 Coarse 100 38.0 29.9-51.8 10.6 8.7-11.6 420 23-84020 12.5 Coarse 75 33.7 29.4-38.7 10.4 8.0-12.8 279 13-93521 12.5 Coarse 125 53.5 51.9-55.5 8.1 6.0-8.7 346 48-56922 12.5 Coarse 125 51.0 44.5-55.8 11.3 10.3-12.1 2379 812-363923 12.5 Coarse 125 52.8 49.3-55.4 8.8 8.0-9.6 238 37-39924 12.5 Coarse 125 56.8 49.6-66.1 9.6 8.2-10.3 361 164-51025 12.5 Coarse 109 50.6 43.9-58.3 6.9 6.4-7.6 39 8-8826 12.5 Coarse 86 47.6 33.6-62.0 6.3 5.4-7.1 92 28-19527 12.5 Coarse 100 44.1 35.0-56.6 5.3 3.8-7.0 2 1-728 12.5 Coarse 125 51.1 48.8-54.2 7.3 4.8-9.8 260 1-157429 12.5 Coarse 100 78.8 64.8-93.9 8.6 6.6-10.4 59 1-16630 12.5 Coarse 125 48.4 47.2-50.6 6.5 5.7-7.3 30 1-8331 12.5 Coarse 100 36.3 29.5-40.9 7.7 5.9-8.8 43 6-11932 12.5 Coarse 75 50.0 40.2-58.5 9.8 5.4-13.3 898 17-216033 12.5 Coarse 75 57.2 47.2-67.8 7.3 4.8-14.0 340 1-260034 12.5 Coarse 75 63.7 54.5-69.8 8.8 6.0-11.4 716 5-279035 12.5 Fine 86 53.3 45.5-62.3 5.3 3.7-6.4 9 1-2436 12.5 Fine 86 44.3 31.8-50.8 8.6 6.9-9.3 133 44-24237 12.5 Fine 125 45.8 26.7-66.1 10.3 8.7-12.2 86 40-14938 12.5 Fine 68 39.8 34.9-47.5 8.1 7.0-9.4 19 4-3539 12.5 Fine 76 51.2 46.3-56.4 9.2 7.1-10.2 124 61-21240 12.5 Fine 109 55.2 49.6-64.4 7.9 7.1-8.7 78 3-21841 12.5 Fine 100 34.8 31.5-41.0 9.6 6.7-12.6 318 28-79742 12.5 Fine 75 38.7 22.5-47.8 8.5 6.1-10.5 144 27-313



TABLE 2 Results of Regression on Factors Impacting Permeability

Stepwise Regression Step 1 2 3 4 5 6 7 8 Constant -9.682 -9.921 -15.947 -18.185 -20.418 -18.837 -20.047 62.30 LnCL 6.33 6.14 5.92 5.96 5.84 5.76 5.84 5.91 T-Value 29.47 29.04 27.45 27.99 27.36 26.93 26.72 27.01 P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 CAratio 0.85 0.85 1.38 1.84 1.97 1.84 1.89 T-Value 5.10 5.19 6.16 6.81 7.24 6.52 6.72 P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 P12.5 0.067 0.074 0.092 0.084 0.088 0.140 T-Value 3.68 4.08 4.89 4.45 4.63 4.97 P-Value 0.000 0.000 0.000 0.000 0.000 0.000 P1.18 0.040 0.144 0.141 0.124 0.119 T-Value 3.39 3.94 3.90 3.33 3.20 P-Value 0.001 0.000 0.000 0.001 0.002 P0.6 -0.130 -0.131 -0.100 -0.091 T-Value -2.99 -3.05 -2.15 -1.95 P-Value 0.003 0.002 0.032 0.051 Ave.Thick. -0.0137 -0.0129 -0.0122 T-Value -2.64 -2.48 -2.36 P-Value 0.009 0.014 0.019 Ndes 0.0062 0.0102 T-Value 1.68 2.56 P-Value 0.093 0.011 P19 -0.88 T-Value -2.49 P-Value 0.013 S 1.13 1.09 1.07 1.06 1.04 1.04 1.03 1.03 R-Sq 71.21 73.20 74.20 75.03 75.66 76.14 76.34 76.75 R-Sq(adj) 71.13 73.05 73.98 74.74 75.31 75.73 75.86 76.21

Regression Analysis Predictor Coef SE Coef T-Value P-Value Constant -19.205 2.102 -9.14 0.000 LnCL 5.9568 0.2134 27.91 0.000 CAratio 1.4719 0.2252 6.54 0.000 P12.5 0.07799 0.01857 4.20 0.000 P1.18 0.04850 0.01256 3.86 0.000 Ndes 0.009281 0.003397 2.73 0.007 Ave.Thick. -0.012372 0.005223 -2.37 0.018 S = 1.039 R-Sq = 76.0% R-Sq(adj) = 75.6% Analysis of Variance Source DF SS MS F-Value P-Value Regression 6 1183.16 197.19 182.79 0.000 Residual Error 346 373.27 1.08 Total 352 1556.43



TABLE 3 Constant Values Used for Independent Variables to Calculate PermeabilityIndependent Seleceted Constant ValuesVariable Thickness Ndes CAratio P12.5 (%) P1.18 (%)Thickness 20 100 1.0 95.0 25.0

40 100 1.0 95.0 25.060 100 1.0 95.0 25.080 100 1.0 95.0 25.0

Ndes 50 50 1.0 95.0 25.050 75 1.0 95.0 25.050 100 1.0 95.0 25.050 125 1.0 95.0 25.0

CAratio (9.5 Coarse) 50 100 0.3 100.0 27.550 100 0.8 100.0 24.450 100 1.3 100.0 21.350 100 1.8 100.0 18.1

Caratio (9.5 Fine) 50 100 0.2 100.0 41.250 100 0.4 100.0 36.350 100 0.6 100.0 31.350 100 0.8 100.0 26.4

Caratio (12.5 Coarse) 50 100 0.6 97.7 25.650 100 0.9 97.2 23.550 100 1.2 96.7 21.350 100 1.5 96.3 19.1

Caratio (12.5 Fine) 50 100 0.3 95.6 36.450 100 0.5 96.1 35.050 100 0.7 96.5 33.7

Caratio (19.0 Fine) 50 100 0.5 87.5 35.850 100 0.6 88.5 32.150 100 0.7 89.6 28.4



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cen

t P

assi

ng

0.075 2.36 4.75 9.5 12.5 19.0 25.00.6




0

200

400

600

800

1000

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0

In-Place Air Void Contents (AASHTO T 166), %

Lab

Per

mea

bili

ty, E

-5 c

m/s

ec

y = 0.1867e0.7112x

R2 = 0.604

FIGURE 4 Plot of in-place air voids versus permeability for all data.



y = 4.2428e0.4303x

R2 = 0.5005Coarse-graded

y = 0.0127e0.9475x

R2 = 0.7597Fine-graded

0

500

1000

1500

2000

2500

3000

3500

4000

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0

In-Place air voids (AASHTO T 166), %

Lab

Per

mea

bili

ty, E

-5 c

m/s

ec

Coarse-graded

Fine-graded

FIGURE 5 Plot of in-place air voids versus permeability for 9.5 NMAS mix.



y = 0.0598e0.8604x

R2 = 0.6799

y = 0.2908e0.6266x

R2 = 0.5819

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0

In-Place Air Void Contents (AASHTO T 166), %

Lab

Per

mea

bili

ty, E

-5 c

m/s

ecCoarse-graded

Fine-graded

FIGURE 6 Plot of in-place air voids versus permeability for 12.5 mm NMAS mix.



y = 0.0321e0.7753x

R2 = 0.6534

0

20

40

60

80

100

120

140

0.00 2.00 4.00 6.00 8.00 10.00 12.00

In-place Air Void Contents (AASHTO T 166), %

Lab

Per

mea

bili

ty, E

-5 c

m/s

ec

FIGURE 7 Plot of in-place air voids versus permeability for 19.0 mm NMAS mix.



0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60 70 80 90Average Thickness, mm

Lab

Per

mea

bilit

y, E

-5 c

m/s

ec

7.0 % Air Voids

7.5 % Air Voids

8.0 % Air Voids

8.5 % Air Voids

FIGURE 8 Relationship between lab permeability, thickness, and in-place air

voids.



0

10

20

30

40

50

60

70

0 20 40 60 80 100 120 140

Ndesign

Lab

Per

mea

bili

ty, E

-5 c

m/s

ec

7.0 % Air Voids

7.5 % Air Voids

8.0 % Air Voids

8.5 % Air Voids

FIGURE 9 Relationship between permeability, Ndesign, and in-place air voids.



0

20

40

60

80

100

120

140

160

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8

Coarse Aggregate Ratio, %

Lab

Per

mea

bili

ty, E

-5 c

m/s

ec

7.0 % Air Voids

7.5 % Air Voids

8.0 % Air Voids

8.5 % Air Voids

FIGURE 10 Relationship between lab permeability, coarse aggregate ratio, and

in-place air voids for coarse-graded 9.5 mm NMAS mix.



0

10

20

30

40

50

60

70

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9


Lab

Per

mea

bili

ty, E

-5 c

m/s

ec

7.0 % Air Voids

7.5 % Air Voids

8.0 % Air Voids

8.5 % Air Voids

FIGURE 11 Relationship between permeability, coarse aggregate ratio, and in-

place air voids for fine-graded 9.5 mm NMAS mix.



0

10

20

30

40

50

60

70

80

90

100

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6


Lab

Per

mea

bili

ty, E

-5 c

m/s

ec7.0 % Air Voids

7.5 % Air voids

8.0 % Air Voids

8.5 % Air Voids

FIGURE 12 Relationship between lab permeability, coarse aggregate ratio,

and in-place air voids for coarse-graded 12.5 mm NMAS mix.



0

10

20

30

40

50

60

70

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8


Lab

Per

mea

bili

ty, E

-5 c

m/s

ec

7.0 % Air Voids

7.5 % Air Voids

8.0 % Air voids

8.5 % Air Voids

FIGURE 13 Relationship between lab permeability, coarse aggregate ratio,

and in-place air voids for fine-graded 12.5 mm NMAS mix.



0

5

10

15

20

25

30

0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75


Lab

Per

mea

bili

ty, E

-5 c

m/s

ec

7.0 % Air Voids

7.5 % Air Voids

8.0 % Air Voids

8.5 % Air Voids

FIGURE 14 Relationship between lab permeability, coarse aggregate ratio, and in-place air voids for fine-graded 19.0 mm NMAS mix.


An Investigation of Factors Influencing Permeability o

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