Small Scale Accelerated Pavement Testing in the Laboratory for the Fatigue Characterization of Hot Mix Asphalt Sudip Bhattacharjee1, Rajib B. Mallick2 and Jo Sias Daniel3 1
Assistant Professor, Alabama A & M University, Normal, AL, 35762, Email:
[email protected], Phone: 256-372-4148, Fax: 256-372-5909, Corresponding Author 2
Associate Professor, Civil Engineering, Worcester Polytechnic Institute, Worcester, MA, 01609, Email:
[email protected], Phone: 508-831-5289, Fax: 508-831-5808
3
Associate Professor, University of New Hampshire, Durham, NH, 03824, Email:
[email protected], Phone: 603-862-3277, Fax: 603-862-2364
ABSTRACT: This paper discusses the results obtained from small scale accelerated pavement testing in the laboratory. The small scale loading equipment has been used in the laboratory to perform fatigue tests on HMA test slabs compacted using vibratory compactor in the laboratory. The real time analysis of data obtained from strain gauges and thermocouples indicated the effect of loading and temperature on the strain response. The fatigue characteristic equation has been developed and compared with the standard fatigue equation. INTRODUCTION The full scale Accelerated Pavement Testing (APT) has become one of the important tools for the design of flexible pavements in recent years, primarily because of the fact that the same amount of damage can be induced in the test pavement quicker than in actual pavement. Some of the facilities such as Texas Mobile Load Simulator (TxMLS) and NCAT test track have enabled the authorities to test multiple test sections simultaneously. But, the full-scale testing requires considerable cost and time to investigate the effect of multiple variables. A small scale accelerated pavement testing in the laboratory would help the authority to find the variables which should be investigated in more detail under the full scale. In order to get a laboratory result comparable to the field condition, the laboratory test condition should be similar to the actual field condition. The proper test condition is obtained when (a) the method of specimen preparation (compaction) followed in the laboratory is similar to the actual field compaction, (b) the HMA layer is supported on flexible foundation and (c) the type of loading is similar to the actual pavement loading in the field. The SHRP study (Sousa et al 1991) recommended that roller compactors should be used to produce laboratory specimens for performance testing. 1
Since the distribution of air voids in gyratory compacted specimens is different from the distribution in roller compacted (and hence actual pavements) specimens (Tashman et al 2002), it is expected that the vibratory roller compaction is a better method of compaction in the laboratory which can produce air void distribution similar to the actual field condition. One important study on the application of wheel load on the flexible pavement structure in the laboratory has been reported by Van Dijk (1975). HMA test slabs, compacted by roller and instrumented with strain gauges, were placed on flexible rubber support and tested under bidirectional wheel loading. Both transverse and longitudinal strain gauges were used to develop fatigue characteristics of HMA. However the use of bidirectional loading pattern caused a variation in the duration of loading along the length of the slabs. Rowe and Brown (1997) reported another similar use of bidirectional loading in the laboratory for fatigue characterization of HMA slabs. The theory of model testing requires that all dimensionless properties should have the same value for model and prototype, and the ‘geometric similarity’ should be achieved. Kim et al (1998) applied this principle for model pavement testing in the laboratory and showed that if α is the scaling factor applied between prototype and model, to get the same strains and stress in the model, the length should be scaled down by 1: α and load magnitude by 1: α 2, while keeping 1:1 scale for material properties and stress on surface. This means that a proper scaled pavement testing should have the HMA thickness reduced by 1: α and load by 1: α 2 with the same material properties. While the study by Van Dijk included a scaling of 3.5 to 5, the load was not scaled to full scaled load and the study by Rowe and Brown did not report any scaling factor. These studies show the necessity of using a device with proper scale factor between model and prototype pavement using unidirectional loading. The laboratory equipment which satisfies these criteria of scaled model is the Model Mobile Load Simulator (MMLS). This equipment set consists of a pavement mold, a roller and a scaled down loading device. This equipment occupies a floor space of about 3658 mm by 1829 mm (working space), which is available in most pavement laboratories. The remaining part of this paper pertains to the study with the scaled MMLS (referred to as MMLS3). The description of the device, the test setup and the analysis of data are presented in the following sections. TEST EQUIPMENT AND SETUP The equipment used in this study is an accelerated loading equipment which applies scaled loading on a scaled pavement structure. It has a running wheel load of 2.7 kN and 690 kPa (100 psi) tire pressure. As mentioned before, the loading on model pavement will result same strains as in the prototype pavement provided the thickness is scaled down by 1: α and the load magnitude by 1: α 2 and keeping 1:1 scale for material properties. Therefore the 2.7kN load corresponds to the full scaled single axle single wheel load of 40kN with a scale factor of 3.85. The loads are applied unidirectionally at the rate of 7200 load applications per hour under controlled environmental condition.
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Vibratory roller compactor was used to compact loose mix in a mold and four test slabs of thickness 36 mm and dimension 1295 mm x 495 mm were prepared by saw cutting each compacted mix in the mold. Four transverse and four longitudinal stain gauges were glued under each test slab and four thermocouples were used along the length of the slabs. A typical test slab was placed on flexible neoprene layers followed by a steel plate and sand foundation. The number of neoprene layers was varied to provide different support conditions to achieve different strain levels. The neoprene layers provided the condition for flexible support to the slabs and the rigid base below the neoprene layers helped minimizing rutting (which affected the fatigue performance and measurements during loading). Figure 1 shows the positions of strain gauges and thermocouples under the slabs. The data from strain gauges and thermocouples was recorded continuously during the loading using NI DAQTM data acquisition system and later post processed using MATLABTM . The mix consisted of a PG 64-28 asphalt binder and a 9.5 mm NMAS aggregate gradation used by Maine Department of Transportation. The following sequence of steps were performed: 1. Preparation of the substructure: The substructure (from bottom to top) consisted of a sand layer of 300 mm thickness followed by one steel plate of 0.5 inch thickness and several layers of neoprene sheets of 1.0 inch thickness each. The sand layer was compacted and the steel plate was provided to reduce the effect of substructure settlement (subgrade rutting) on fatigue performance, since the primary objective of the tests was to investigate the fatigue performance. The neoprene layers provided the flexible support under the test HMA layer. 2. Preparation, lay-down and compaction of the mix: Loose HMA mix was prepared in batches and aged according to short term aging protocol. The mix was then laid down on a steel plate on top of the sand subgrade, which was preheated with heater before lay-down. The mix was then compacted using vibratory roller compactor to produce a uniform thickness of 36 mm. 3. Cutting and removing the test slabs: After the compacted mix cooled down, dry ice was used to cool down further. The whole section was then cut into four equal parts using saw and removed and stored in separate place to prepare for instrumentation. The dry ice helped to reduce the effect of deformation during transportation to storage from the mold. 4. Instrumentation: The bottom surface of each slab was instrumented with eight strain gauges and four thermocouples along the length of the slab. Among the eight strain gauges, four were installed in transverse direction and four in longitudinal direction. Four thermocouples were also installed along the length of the slabs. 5. Testing: A typical slab was placed on the neoprene layers and clamped down on the edges to prevent any movement. The wires from the strain gauges and thermocouples were taken outside of the slab and connected to the NI DAQTM data acquisition system. The MMLS3 loading equipment was then placed on top of the slab and leveled to make sure all wheels were loading with same load. The environment chamber was then placed on top of the slab. Once the temperature under the slab was detected as uniform target temperature, the data acquisition was started followed by the start of the loading machine.
3
Compactor
Compacted mix (a)
(b)
(c)
MMLS3 3
Test Section (d) (e) FIG. 1. Test setup; (a) roller compactor and the compacted pavement, (b) installation of strain gauges with wires attached, (c) environment chamber with ducts to control air temperature, (d) MMLS3 equipment, (e) data acquisition during a test. ANALYSIS OF STRAIN Real time acquisition and analysis of data Continuous data was acquired during the tests using NI DAQ™ data acquisition hardware and Labview™ software. The acquired data included the data from each of the eight strain gauges and the four thermocouples. A Labview™ program was written to acquire data at certain interval which varied from one minute to one hour depending on the length of the test. Total duration of a typical test was about one week including the stoppage of the machine for maintenance and profiling of the test slab surface. During each acquisition, one minute of data was acquired through all channels and was immediately processed using MATLAB™. A MATLAB™ program was written to perform this real time analysis of data at the same time the data acquisition. After performing the analysis, the program saved the analysis results in text file on the hard 4
drive. Since many of the tests were run throughout night, it was not possible for the researcher to be present in the laboratory during the whole night. For this reason, the program was written not only to save the results in hard drive, but also to send an email to a predefined email address with the attachment of the results. The attachment included the latest plots of the strain versus time from all strain gauges and the temperature versus time from all thermocouples. Thus, the researcher was able to check periodic emails to monitor the progress of the test. In addition to that, a live web cam was also set up. Using the periodic email with the updated results and the live web cam, a complete automation of the test was possible with minimum presence in the laboratory. This system of real time acquisition, analysis and monitoring of data can be used to any testing schedule where long term running of the tests is essential with minimum presence of laboratory personnel. Strain history The slabs were subjected to repeated loading until failure. During loading, the temperature was controlled at constant value. The tests were stopped at frequent intervals to measure the surface profile of the pavement using a profilometer. Figure 2 shows the variation of recoverable strain over time for both longitudinal and transverse strain gauges. The recoverable strain was calculated as the amount of strain recovered at the end of each cycle of load. As indicated in Figure 2, the recoverable strains increased over time showing three distinct phases: initial sudden increase of strain, followed by relatively lower rate of increase which is followed by a higher rate of increase before failure. This indicated that the damage developed in the model pavement during the tests. Figure 2(a,b,c) also indicates that the transverse strains were higher than the longitudinal strains. Figure 2(d) shows the final cracked surface of the pavement. FATIGUE PERFORMANCE The fatigue characteristic equation is traditionally expressed as a relationship between strain and number of loads to failure. One of such frequently used equations is the Asphalt Institute (AI) equation, which can be written in the following form:
N f = k 1 Cε − k 2 E *
− k3
⎛ Vb ⎞ − 0.69 ⎟⎟ C = 10 M , M = 4.84⎜⎜ ⎝ Va + Vb ⎠
(1)
where, k1, k2 and k3 are three regression constants, Va is the percent air voids, Vb is the effective binder content, ε is the initial strain and |E*| is the dynamic modulus of the mix. This form the characteristic equation was chosen to represent the material behavior in this study and the regression constants were calculated from the acquired data from the strain gauges. The |E*| values were obtained at a particular temperature using the dynamic modulus master curve and the shift factors of the mix.
5
Recoverable strain
Micro Strain
1000 500 0 0
1
2
Recoverable strain
1000 800 600 400 200 0 0
-500
(a) 2000
0.5
1
1.5
2
Tim e , s e c
Tim e , s e c
Resilient Strain
Micro Strain
This produced the following values of the regression constants: k1 = 109.833, k2 = 5.828 and k3 = 4.402.
(b)
Transverse SG
1500 1000
Longitudinal SG
500 0 0
100000
200000
300000
Tim e (s e c)
(c) (d) FIG. 2. Typical reading from strain gauges; (a) longitudinal strain gauge, (b) transverse strain gauge, (c) recoverable strain, (d) cracked surface. Comparison with the AI equation
The failure loads obtained using Eq. 1 was compared with the traditional AI equation. It is to be noted that the traditional AI equation uses the following values of the regression constants: k1 = 0.0795 (including shift factor = 18.4), k2 = 3.291 and k3 = 0.854. The comparison is shown in Figure 3 for two different values of |E*| and the following points can be observed from the figure: 1. Slope of the MMLS3 fatigue equation is greater than the AI equation. 2. For lower stiffness of the mix, the MMLS3 curves lie above the AI fatigue curves for the strains up to 500 micro strains. This is observed even after applying the shift factor of 18.4 in the AI equation and no shift factor was applied to MMLS3 curve. 3. The effect of dynamic modulus is greater for MMLS3 curves than AI curves. Effect of temperature and density on Nf
To investigate the effect of compaction density and the temperature on fatigue failure loads, slabs were compacted at different percent air voids and tested at various temperatures. Following regression equation has been developed which reflects the direct relationship between temperature (oC) and Nf. 6
N f = 0.241× 10 7 − 1.0 × 10 5 T
(2)
MMLS3, |E*| = 290 ksi MMLS3, |E*| = 435 ksi
10 9
Log(Nf)
8 7 6 5 4 3 2 -4.1
AI, |E*| = 290 ksi (top) AI, |E*| = 435 ksi (bottom) -3.9
-3.7
-3.5
-3.3
-3.1
Log(s train)
FIG. 3. Fatigue equation from MMLS3 and AI.
The above equation was then modified to include the effect of density (% of Theoretical Maximum Density, TMD) to produce the following regression equation.
N f = −6.6 × 10 6 − 7.7 × 10 4 T + 9.1 × 10 4 (%TMD)
(3)
The above two equations confirm the standard findings that the increase in temperature reduces the failure load, whereas the increase in density increases the failure load; thus indicating that the MMLS3 equipment can be used in the laboratory for the fatigue characterization of HMA. This is shown in Figure 4.
Nf
1.40E+06 1.20E+06 1.00E+06
15 C
8.00E+05
20 C
6.00E+05 4.00E+05 2.00E+05
25 C
0.00E+00 92
94
96
98
100
% TM D
FIG. 4. Effect of density and temperature on fatigue load. Effect of temperature on strain response
Several tests were also run at different constant temperatures. Figure 5 shows the effect of temperature on strains, which indicates that the failure occurred earlier with greater temperature. This again confirms that the MMLS3 is capable of capturing the realistic behavior of HMA under repeated loading.
7
Strain (MS)
2000
High temp
1500 1000
Low temp
500 0 0
100000
200000
300000
Tim e (s e c)
FIG. 5. Effect of temperature on strain response. CONCLUSIONS
The following conclusions can be drawn from the study: 1. The accelerated loading equipment MMLS3 is an effective tool for the fatigue characterization of HMA in the laboratory which applies realistic loading pattern to a model pavement structure. 2. The effect of various variables such as the temperature and density on the fatigue performance of HMA can be easily observed in the laboratory using this equipment within reasonable time period. ACKNOWLEDGEMENT
The authors acknowledge the support of the Maine Department of Transportation for funding and Jon Gould of WPI and Heather Bolton of UNH for running tests in the laboratory. REFERENCES
Dijk Van. W. (1975). “Practical Fatigue Characterization of Bituminous Mixes.” Journal of The Association of Asphalt Paving Technologists, Vol 44: 38-74. Kim S. M., Hugo F. and Roesset J. M. (1998). “Small Scale Accelerated Pavement Testing.” ASCE Journal of Transportation Engineering, Vol 124, No 2: 117-122. Rowe G. M. and Brown S. F. (1997). “Validation of Fatigue Performance of Asphalt Mixtures with Small Scale Wheel Tracking Experiments.” Journal of the Association of Asphalt Paving Technologists, Vol 66: 31-73. Sousa, J. B., Harvey J, Painter L, Deacon J. A., and Monismith C. L. (1991). “Evaluation of Laboratory Procedures for Compacting Asphalt-Aggregate Mixtures.” Strategic Highway Research Program (SHRP), Project SHRP-A003A, National Research Council, Washington, DC. Tashman L., Masad E., D’Angelo J., Bukowski J. and Harman T. (2002). “X-Ray Tomography to Characterize Air Void Distribution in Superpave Gyratory Compacted Specimens.” International Journal of Pavement Engineering, Vol 3(1): 19-28. 8
