Evaluation of Asphalt Field Cores with Simple Tomography
Evaluation of Asphalt Field Cores with Simple
Performance Tester and X-ray Computed
Florentina Angela Farca ș
Division of Highway and Railway Engineering
Department of Transport Science
School of Architecture and the Built Environment
Royal Institute of Technology
SE-100 44 Stockholm
The importance of aggregate structure and air voids distribution for asphalt mixture rutting and cracking performance has been well established on the basis of experience and is well documented in the literature. Past and current investigations are limited to assessment of performance based on macroscopic behavior due to the difficulty associated with the quantitative measurement and analysis of the internal structure of asphalt mixtures. Lately, technical advances in X-ray Computed Tomography (CT) and image processing and analysis has made possible to bring the attention also to the internal structure of asphalt mixtures.
SPT results from asphalt field cores, including dynamic modulus (before and after loading) and microstrain accumulation (flow number), exhibited significant variability; most likely, induced by irregularities in the core shape. The analysis of aggregate structure and air voids distribution performed trough X-ray CT, clearly identified segregation in the asphalt mixture as a key factor that induced variability in SPT results.
X-ray CT provides fundamental resources to enhance understanding about role that aggregate structure and air voids distribution of asphalt mixtures play on rutting and cracking of asphalt mixtures; such valuable knowledge could eventually generate further development of asphalt mixture design procedures and/or optimization of pavement construction methods that ultimately may lead to long lasting and economical asphalt pavements structures.
Keywords: asphalt mixture, dynamic modulus, flow number, simple performance tester, X-ray computed tomography, air voids distribution.
First of all, I would like to express special thanks to my supervisor professor
Björn Birgisson for providing great assistance and encouragement during my thesis.
I would also like to thank the following people:
• Co-supervisors: Dr. Alvaro Guarin and Dr. Denis Jelagin
• Guest professor: Manfred Partl
• Associate professor: Nicole Kringos
• Skanska: Kenneth Olsson
• NCC: Dr. Jonas Ekblad
• Colleagues: Agneta Arnius, Åsa Laurell Lyne, & others
• Family and Friends
• All others that I might have forgot to mention
Stockholm, March 27, 2012
Table of Contents
Table of Figures
Index of tables
Aggregates are a key constituent of asphalt mixtures, since they represent about
95% of the total weight of the mixture. Particle size distribution and air voids distribution are factors that affect the most important properties of asphalt mixtures, such as rutting and cracking resistance.
The SPT for Superpave mix design was developed as part of the National
Cooperative Highway Research Program NCHRP project 9-29 “Simple
Performance Tester for Superpave Mix Design”, from 2001 until 2011. Under this project the Advanced Asphalt Technologies research agency proof-tested
SPT for permanent deformation and fatigue cracking in asphalt mix design. SPT has been mostly used for laboratory-prepared asphalt samples; this project evaluated the potential use of field cores for SPT
X-Ray Computerized Tomography (X-Ray CT) is a non-destructive technique that allows visualizing the interior of solid objects by capturing digital information on their 3-D microstructure. Several researchers have used this equipment for construction materials to characterize internal structure, determine air void distribution, quantify material permeability, and determine the evolution of microstructure during loading.
This work was focused on evaluating the viability of using SPT for characterization of asphalt field cores and the use of X-ray CT for evaluation of the internal structure of asphalt mixtures e.g., aggregate structure and air voids distribution.
The main purpose of this study was to investigate variability of SPT results when asphalt field samples are evaluated; X-ray CT was also utilized to analyze the internal structure (Aggregate structure and air voids distribution) of the asphalt cores and possibly identify factors that may have induced variability of
SPT results. Detailed objectives of this research work were as follows:
Simple Performance Tester SPT
• Measure dynamic modulus
• Generate dynamic modulus master curve
• Evaluate variability of dynamic modulus
• Determine flow number
• Establish characteristic creep curve
• Evaluate air void distribution along the vertical direction
• Assess air voids distribution before and after loading
• Compare aggregate structure from different samples
• Estimate particle size distribution
This study focused on the use of asphalt field cores to perform Simple
Performance Tester SPT and in the detection of elements that can induce variability on SPT results. An X-ray CT system was employed for detailed analysis of the internal structure of the asphalt mixture. All the field cores were produced using a single asphalt base type (AG22) prepared with binder type
2.1 Simple Performance Tester
Simple Performance Tester (SPT), also widely known as the Asphalt Mix performance Tester, is a computer controlled hydraulic testing machine to measure dynamic modulus and flow number of asphalt mixtures according to the AASHTO TP79 specification.
A project sponsored by the Federal Highway Administration began in 1996 at the University of Maryland to validate SPT for measurement of permanent deformation and fatigue cracking. Three years later, National Cooperative
of protocols for SPT to be incorporated in the Superpave volumetric mix design method. A final report was prepared including updated test protocols for the validated SPT and guidelines for their implementation and adoption by
The SPT for Superpave mix design was part of the project NCHRP 9-29
2011. Under this project the Advanced Asphalt Technologies research agency was assigned the task of designing, procuring, and evaluating an SPT for:
• Proof-testing for permanent deformation and fatigue cracking in HMA mix design and
• Materials characterization for pavement structural design according to the Mechanistic-Empirical Pavement Design Guide (MEPDG).
permanent deformation, fatigue cracking, and low-temperature cracking. This
report recommended three test-parameter combinations for further field validation:
• Dynamic modulus , E*/sinφ,
• Flow time, Ft, determined from the triaxial static creep test; and
• The flow number, Fn, determined from the triaxial repeated load test.
analyzed the variability of dynamic modulus and flow number. The Phase III,
NHCRP Report 530, led to the production of a SPT machine capable of accurately measuring dynamic modulus for calculation of master curves.
In 2004, the NCHRP report 530, “Evaluation of Indirect Tensile Test (IDT)
underlines the importance of understanding of low-temperature cracking mechanisms in asphalt pavements and contributes to SPT development by reducing test variability and improving its precision and reliability.
ﬁning the Simple
Performance Tester for Use in Routine Practice” proposed a new standard practice for developing the dynamic modulus master curve (frequency and temperatures for testing) for a limited temperature range (from 4°C to 40°C).
Improvements for cooling capacity, load capacity, indicators were also suggested.
flow number tests with the Simple Performance Tester”, describes a series of experiments to be conducted and analyzed to assess the SPT equipment and test procedures for the dynamic modulus and flow number tests. Phase V included two major experiments:
• A formal ruggedness experiment in accordance with ASTM E1169,
Standard Guide for Conducting Ruggedness Tests.
• An experiment designed to investigate whether there are significant differences in SPT data collected with equipment from the three manufacturers: Interlaken Technology Corporation (ITC); IPC Global
(IPC); and Medical Device Testing Services (MDTS).
dynamic Modulus and flow number tests conducted with the asphalt mixture performance tester”. An inter-laboratory study was designed to analyze:
• Dynamic modulus and phase angle,
• Unconfined flow and
• Permanent strain in confined flow number tests.
X-ray Computed Tomography 5
The findings of this project were multiple:
• Variability in the tests increases with decreasing specimen stiffness. The variability of low stiffness dynamic modulus tests and the permanent deformation in confined flow number tests is higher.
• Variability of unconfined flow number tests is unacceptable considering
• Specimen fabrication was found to be a major source of between-lab variability in both the dynamic modulus and flow number tests.
Compactor type, air void content, and specimen age were evaluated, and none were found to have a systematic effect on the study dynamic modulus and flow number data.
• Gauge point drift was evident in the high-temperature dynamic modulus test data from two of the participating laboratories out of 7.
• Differences in the fabrication and use of the greased latex end friction reducers are likely a source of significant variability in the flow number tests. Better control on the type, amount, and distribution of the grease is needed.
The NCHRP Project 9-29 successfully devised tests, methods and specifications for the development of a SPT machine. The project resulted in the development, improvement and validation of SPT machines by several manufacturers.
2.2 X-ray Computed Tomography
X-Ray Computerized Tomography (X-Ray CT) is a non-destructive technique that allows visualizing the interior of solid objects by capturing digital
an X-Ray source, a detector, and a turntable carrying the test specimen in between the source and the detector. X-Rays intensities are measured before and after they are emitted through the specimen in different directions for a full rotation of the specimen. The intensity values are used to calculate the distribution of the linear attenuation coefficient in order to generate a map representing the density at every point in the microstructure. Brighter regions correspond to dense objects such as aggregates, and dark regions correspond to low-density objects such as voids. X-Ray CT systems are very sensitive to small variation in density, which could be as low as 1% or smaller. This enables the
X-Ray CT system to characterize a wide spectrum of engineering materials (e.g.
For a long time, researchers have assumed that granular construction materials such as asphalt and concrete are isotropic in developing continuum models. In reality, these materials are complex composite structures of aggregates (rock)
and binder material consisting of bitumen and cement paste for asphalt and concrete, respectively. There is a vital need to quantify the relationship between microstructure characteristics that are known to influence the behavior of the
X-Ray CT systems provide the ability to characterize the microstructure and its evolution for asphalt and concrete as these materials are subjected to loading, which is critical for a better understanding of the behavior of these composite construction materials needed for the development of more realistic prediction models of the performance of these materials. In fact, without a clear understanding of the evolution of the microstructure, the understanding of the
models for asphalt and concrete materials are developed without experimental measurements of the microstructure distribution. This is due to the difficulties associated with the quantitative analysis of the microstructure, which has prevented continuum modeling from becoming a state-of-the-practice technique
Recently, there have been several successful attempts to quantify the microstructure of granular materials using imaging technology. Initial attempts focused on two-dimensional (2-D) measurements conducted on cut sections of
the material (e.g. , , , , ). The 2-D measurements were extended to 3-D using stereological principles . The stereology approach to quantify the microstructure was initiated by Hilliard in the 1960’s ,  and expanded to Cartesian tensor formulation by Kanatani in 1980’s , , who
presented a systematic approach using Buffon transform and microstructure tensors to represent the distribution of microstructure quantities. However, this approach is laborious, destructive, ineffective in capturing the evolution of the microstructure, and provides an approximation of the actual 3-D distribution from 2-D measurements.
X-Ray computed tomography (CT) is fast becoming a powerful tool to accurately and non-destructively characterize the microstructure of many granular materials. It has been successfully utilized to quantify the 3-D
Ray CT images can be processed using image processing techniques to digitally reconstruct the 3-D microstructure of the scanned specimen. A key aspect of X-
Ray CT equipment is that it allows for further mechanical testing of the specimen after initial loading and imaging, where one can relate the microstructure to the mechanical response of the material.
X-ray Computed Tomography 7
X-Ray CT equipment has been used for construction materials to:
• Characterize their microstructure.
• Determine air void distribution.
• Quantify material permeability.
• Determine the evolution of microstructure during loading.
2.2.1 Microstructure Characterization
microscopic internal displacement fields associated with the permanent deformation of 3-D asphalt cores while satisfying the small gradient approximation of continuum mechanics. They computed the displacement field associated with diametral loading of a cylindrical asphalt core using X-Ray CT to collect 3-D images from sequences of 2-D images scanned before and after loading. The pair of 3-D images was then used to compute the displacement field by comparing their 3-D representation before and after the deformation.
capture some aspects of the internal structure of asphalt. Masad et al. (1999
gradation of asphalt. Images captured using X-Ray CT were used to analyze air void distribution and images captured were used to study aggregate orientation and segregation. Segregation refers to preferential separation of coarse and finegraded aggregates within the material, leading to reduced life and durability problems. Segregation can be caused by the material design, improper handling, or compaction. It is therefore important to be able to quickly determine the presence of segregation through X-Ray CT scanning.
different compaction methods including the gyratory compactor, vibratory, and slab compaction by slicing the material into thin sections. They found that the circumferential aggregate orientation increased with increasing particle size in the gyratory compactor and vibratory compactors. Aggregate segregation was also found to differ using different compaction methods. They also conducted a repeated load axial test and found that the gyratory compactor and vibratory compacted specimens showed higher resistance to permanent deformation than slab compacted specimens.
automated image processing algorithm to isolate the aggregates from the other phases in a digital image and separate those that are in contact. The algorithm significantly improved the accuracy of the image analysis to determine the orientation, segregation, and gradation of aggregates. In their study, Tashman et
measurement of laboratory compacted specimens and field cores from asphalt pavements. The study showed significant difference in terms of aggregate orientation, segregation, and air void distribution, i.e., laboratory prepared asphalt specimens did not simulate the field condition in terms of the microstructure distribution. Segregation was noticed in laboratory compacted specimens. Aggregates had a more preferred orientation towards the horizontal direction (perpendicular to the applied load) in field cores than in laboratory compacted specimens. This has a major impact on interpreting experimental results from laboratory prepared asphalt specimens to predict field performance.
In addition, it was found that laboratory compacted specimens exhibited axisymmetric aggregate distribution, where the aggregates had a preferred orientation in sections cut vertically but had random distribution when the sections were cut horizontally.
The effects of anisotropy on modulus and strength of construction materials need to be studied. Only few studies have been conducted to establish a relationship between these key engineering properties and the material
study the modulus anisotropy of asphalt mixtures within the framework of a micromechanics-based model. They found that the stiffness in the vertical
(axial) direction is 30% more than that in the horizontal (lateral) direction. This
axisymmetric distribution of asphalt microstructure.
Similarly, microstructure anisotropy causes a coupling between the volumetric and deviatoric response. This coupling effect is an important feature in modeling the behavior of granular materials, for which inelastic dilation is a
microstructure distribution so that a scalar quantity is not sufficient to
it is necessary to introduce microstructure quantities that can represent the directional nature of the microstructure. These quantities are referred to as
“microstructure tensors” and are determined from measurements on the solid or void phase.
Tomographic imaging systems have been used to evaluate existing microstructure tensor formulations and possibly develop new formulations based on the scanned images obtained from a wide range of asphalt and concrete materials. The most popular microstructure quantities are the aggregate contact normals, aggregate orientation, void/crack orientation, and branch
normal to the tangent plane at the point of contact between aggregates. An aggregate orientation is defined by the direction of its longest axis. A branch
X-ray Computed Tomography 9 vector is represented by a line joining the centers of mass of the contacting aggregates. The directional distribution of voids is described by dividing the void space into a number of “unit voids”, and assigning a vector to describe the
A continuum representation of the anisotropic distribution of the microstructure is achieved by averaging the directional distributions of the different microstructural quantities within a representative volume element. Kanatani
transform and microstructure tensors to describe the directional distribution of microstructure quantities, regardless of the quantity under consideration.
anisotropy of engineering materials can be approximated using only a second order tensor.
Past efforts relied on destructive techniques that involved cutting specimens in equally spaced sections parallel to three orthogonal planes and applying stereological principles along with the assumption of randomness to obtain the
such an invasive technique restricted the applicability of the above procedure in capturing the evolution of the microstructure during deformation. Its main shortcomings are the large number of samples required for taking accurate successive measurements and the bias introduced due to the randomness assumption. The X-Ray CT offers a solution to such a problem through 3-D measurements of the distribution function in order to determine the components of the deviatoric microstructure tensor.
Characterizing the microstructure of asphalt and concrete requires the isolation of the individual phases in the first place before conducting any image analysis.
These phases are the aggregates, air voids/cracks, and the binder for asphalt or for concrete. The aggregate contact normal distribution is another important
verify the voids in coarse aggregate (VCA) concept for defining stone-on-stone contact in open graded friction coarse asphalt mixtures. The image analysis technique had the advantage over the VCA method in that it can determine the number of contact, which is related to stiffness, while the VCA method gives
A statistical parameter (
) that can be used to quantify the directional distribution of aggregate orientation or contact normals was developed by
∆ ranges between zero and unity.
Zero value indicates the aggregates are completely randomly distributed, which is analogous to isotropic materials, and a unity value indicates the aggregates
(or the contact normals) are all oriented in one direction.
the aggregate orientation (or contact normal) in terms of a vector magnitude that indicates that aggregate distribution in HMA is anisotropic and that the aggregates have a preferred orientation toward the direction perpendicular to the direction of the applied load. This was illustrated for two asphalt mixtures by
ranges between 0.3 and 0.5 on vertical sections of HMA, whereas it does not exceed a value of 0.1 on horizontal (lateral) sections. X-Ray CT systems have been used to further evaluate aggregate particle distribution for a wider range of asphalt and concrete materials.
2.2.2 Air Void Distribution
Though air voids in concrete and asphalt possess no appreciable mechanical strength, their distribution is important in determining the overall response of
void size and spatial distribution in asphalt would present valuable information leading to a better understanding of the permanent deformation and fatigue
and size distribution of the air voids in different asphalt field specimens with known permanent deformation performance. They used X-Ray CT imaging and a virtual cutting technique to conveniently obtain the cross-sections in different orientations.
laboratory asphalt specimens exhibit a “bath-tub” shape where more air voids were present at the top and bottom parts of a specimen. This shape was more pronounced at higher compaction efforts. They also found that specimens prepared with different aggregate sizes were found to have noticeably different air void sizes.
with image analysis techniques to characterize the statistical distribution of air void sizes at different depths in asphalt specimens; they found that air voids follow a Weibull distribution. About 40% of the total number of air voids was found to concentrate at the top third of the sample. In the case of specimens prepared using linear kneading compactor, air void content was found to increase with depth. The effect of gradation was also well reflected on the air void size; the coarser gradation showed larger air voids. In summary, the results mean that different compactors used in current practice produce compact asphalt that can have a significantly different microstructure and thus also a different loading response. There is currently a strong need to better understand
X-ray Computed Tomography 11 the relationship between internal microstructure of asphalt and its: loading response, durability under loading, extended environmental exposure.
2.2.3 Permeability analysis
One of the most promising implications of characterizing the 3-D air void distribution in HMA is the ability to identify and distinguish the connected air voids from the total air voids. This is very important for accurate characterization of asphalt permeability as it is related to the connected air voids, whereas the isolated air voids do not contribute to this phenomenon.
location of air voids in successive X-Ray images taken along the height of asphalt specimens. The algorithm identifies if a void in an X-ray CT image overlaps with another in the image underneath it. The algorithm retains the overlapping voids and deletes the ones that are not, thus isolating the connected air voids along the entire depth of the specimen. After the connected air voids had been isolated, several information were obtained that were related to the permeability characteristics including the total effective void content, specific surface area of the voids, and tortuosity (from the center of masses of the connected voids). These parameters were related using the Kozeny-Carman
establish air void and permeability gradients in ten field sections. Subsequently, they used these gradients in an unsaturated flow finite element model to evaluate the impact on the ingress and flow of water through these pavement sections.
for fluid flow in granular materials by solving the continuity equation and momentum equations (x- and y- direction) for every pixel within the microstructure using a non-staggered scheme arrangement. The non-staggered scheme allows using the same finite difference grid for the continuity cells, momentum cells in x-direction, and momentum cells in y-direction. Hence, each pixel in the digital image of the microstructure represents the continuity cell as well as the momentum cell in both directions.
a commercial 3-D finite element code to simulate the flow of water through scanned images of asphalt specimens. This model was used by Birgisson, et al.
the Florida Department of Transportation and the Federal Highway
known moisture damage potential, along with calculated permeabilities determined from X-Ray CT imaging to illustrate the effect of aggregate gradation on moisture damage.
18.104.22.168 Microstructure Evolution and Damage during Loading
to measure internal damage and crack growth in small mortar cylinders loaded in uniaxial compression. In their experiment, small mortar cylinders were inserted into a small loading frame that could be mounted directly on the X-Ray rotation table. This was done in order to scan the specimens at varying strain values so that the internal damage could be quantified and correlated with load deformation information. Multiple tomographic scans were made of the same specimen at different levels of deformation applied through a custom built loading frame, and image analysis of the scanned images was used to measure the internal crack growth during each deformation increment. They showed that under monotonic loading of concrete, there was elastic deformation up to 30% of peak load; beyond this point, cracking occurred at the cement-aggregate interface. At about 70% of the peak load, these distributed cracks started to localize and matrix cracking occurred, which macroscopically became largescale axial splitting. Post-peak response was characterized by additional matrix cracking and frictional mechanisms in a relatively narrow band.
scanned initially then deformed to prescribed strain levels of 1%, 2%, 4%, and
8% in a triaxial test set-up. The test was stopped when the prescribed strain was achieved, and the deformed specimens were imaged again. They found that the asphalt specimens illustrated a clear localization behavior that appeared related to the microstructure of the material used.
evolution of the aggregate structure of asphalt subjected to a permanent
through obtaining the differences of the particle mass center coordinates before and after testing, which allowed the determination of the average strain in a small element consisting of four adjacent particles. The strains in the surrounding mastic were quantified assuming the aggregate particles have only rigid motions. The study indicated that the strains at the microstructure level deviate significantly from the strains computed through homogeneous continuum theories, and that the strains in the mastic could be ten times larger than the average strains. Nevertheless, the overall average of these strains resulted in the same displacements observed at the boundaries. The experimental observations from the limited study performed by Wang et al.
X-ray Computed Tomography 13
mastic at larger strains need to be characterized for a better description of the mixture properties, and b) the binder and mastic properties at small strains may not represent their behavior at larger strains.
In order to study plastic deformation it is necessary to define a yield surface for granular materials; many plasticity models dealing with anisotropic materials
continuum model for an anisotropic material is by replacing the stress tensor with a combined tensor that consists of a stress tensor and a microstructure
The material deformation represented by the shear strain rate tensor, which is the deviatoric part of the strain rate tensor, can be related to the rate of change of microstructure tensor considering that both are deviatoric through the use of a
allows for the quantification of the changes in asphalt microstructure as reflected by the deviatoric microstructure tensor D
, and relates it to the macroscopic strain of the material as it undergoes the deformation process.
The literature has shown that X-Ray CT is a powerful tool to characterize and capture the damage within a material microstructure. Its power stems from the fact that it is non-destructive; hence the tested specimen is still intact for further mechanical testing where the captured microstructure can then be related to the
of effective stress theory, which has been successfully implemented to account for the effect of microstructure damage on the mechanical response of a damaged material within the framework of continuum damage mechanics (e.g.
damage is defined as a microstructural change that induces some deterioration in the material. The effective stress theory postulates that the material damage can be characterized mainly by the decrease in the load-carrying effective area caused by the nucleation and growth of cracks and voids as Murakami (1988
stress can be represented by a perfect material subjected to a fictitious stress called the effective stress. In order to quantify a damage tensor, which is symmetric, the six independent components of the tensor need to be determined
Asphalt field cores were tested with SPT in order to measure dynamic modulus and flow number; moreover, an X-ray CT system was used for analysis of aggregate structure and air voids distribution in the asphalt mixture.
3.1 Field Cores
The asphalt cores (100mm diameter x 180mm height) were obtained from a trial field section built by Skanska as part of the construction of a road in
Katrineholm; no traffic loads were applied on the section. The required height for the samples is not typical for Swedish roads and proved a challenge for the laydown and compaction of the mixture. Through experimentation Skanska was able to obtain a single layer of 18cm of HMA from which the samples were cored.
For this project, Skanska used a common base mixture named AG22, which was prepared according to the Trafikverket (Swedish Traffic Administration) specifications; additional information about the asphalt mixture is presented below:
• Binder Type: 70/100
• Binder content: 4.4%
• Air void: 5.6%
• Compacting temperature: 140-155°C
• Max specific Gravity: 2.512
• Bulk Specific Gravity: 2.372
see that the mixture meets the gradation requirements according to the Swedish
0.45 Power Gradation Chart
0,5 1 2 11.2
Figure 1. Gradation chart.
Katrineholm AG22 Gradation
Standard AG22 Maxim Gradation
Standard AG22 Minim Gradation
3.2 Testing Equipment
3.2.1 Simple Performance Tester (SPT)
hydraulic loading machine designed to provide researchers and engineers with a tool capable of conducting a range of tests to analyze the performance of HMA.
This device employs hardware technology and software that provides better accuracy, repeatability and operator performance compared to other commercial systems.
The equipment is controlled by a computer system which has installed a
system gathers the dynamic data from the Linear Variable Differential
Transformer (LVDT) transducers attached to the specimen under test then displays plots appropriate to each test type and the function mode, in real time on the PC.
Testing Equipment 17
Figure 2. The IPC SPT machine.
Figure 3. IPC SPT computer software running dynamic modulus test.
The machine is composed of an electrically powered hydraulic loading system, a confining pressure system, an environmental chamber, and appropriate control systems. For confined tests air pressurization (up to 210kPa) is used as the confining medium. This confining technology is a clean approach for the technician compared to other systems based on oil or water. The test control system is computer based, using sensors on the machine for feedback (load and confining pressure) signals. The hydraulic system uses a bottom loading actuator system with feedback loop control and a run time adaptive control that adjusts the command signal on the fly during testing.
The temperature inside the environmental chamber is changed by a unit outside the triaxial cell controlled by a temperature sensor present inside the chamber.
The machine can change the temperature inside the chamber from negative to positive values (temperatures ranging from -4°C to 60°C) via a small refrigeration unit or a heater unit. Thermally conditioned and pressurized air can be provided to the triaxial cell upon command by the operator, thus providing thermal equilibrium within a three minute time limit.
The studs, where the LVDTs are mounted, can be attached with glue (usually
parallel brass studs are glued 100-mm apart and located approximately 25 mm from the top and bottom of the specimen.
Figure 4. Gauge Point Fixing Jig.
are placed vertically on diametrically opposite sides of the specimen.
Testing Equipment 19
Figure 5. SPT - sample setup.
The LVDTs sensors measure the deformation in sample when a load is applied.
SPT aims to relay asphalt mix design to the performance in the field. Asphalt mixture can be characterized in the laboratory by measuring permanent deformation resistance, fatigue life, tensile strength, stiffness, and moisture susceptibility. Specifically for SPT, the common laboratory test methods for evaluating HMA are: dynamic modulus, flow number (dynamic creep test) and flow time (static creep test).
The tests performed on SPT are detailed below. The procedures for running the
22.214.171.124 Dynamic Modulus
The dynamic modulus is a relevant property of HMA and has several applications in asphalt pavement technology:
• Visco-Elastic Analysis of asphalt mixtures (laboratory and field)
• Mixture Design and Rutting Resistance: high temperature (fast load rate for freeways, slow loading rate for intersections), plant aged condition, air voids percentage in the mix.
• Mechanistic Empirical Pavement Design (Stiffness, Rutting Model,
Fatigue Cracking Model).
Dynamic Modulus is the ratio of the stress to the strain for asphalt concrete subjected to sinusoidal loading. In the Dynamic Modulus Test while maintaining a specific test temperature the sample is subjected to a controlled sinusoidal (haversine) compressive stress (load) at various frequencies. The applied stress and resulting axial strains are measured as a function of time and
Figure 6. Dynamic Modulus - schematic loading.
The dynamic modulus is calculated using the following equation:
( 360 )
= dynamic modulus
= phase angle, degree
= applied stress
= measured strain
= time lag between stress and strain
= period of applied stress
The dynamic modulus data generated by SPT at different frequencies is organized in the form of arrays, one for time and one for each transducer. The load is measured when applied and the LVDT sensors register the specimen deformation. The analysis has been devised to provide complex modulus in units of Pascals (1 Pa = 1 N/m
) and phase angle in units of degrees.
The general approach used here is based upon the least squares fit of a sinusoid,
and also includes provisions for estimating drift of the sinusoid over time by including another variable in the regression function. The regression approach also lends itself to calculating standard errors and other indicators of data quality.
Testing Equipment 21
The requirements for data quality from the statistical standpoint are given in the
tests until the results are between the specified limits. In order to cause minimum damage to the samples while measuring the dynamic modulus and phase angle the tests should be conducted by increasing temperature and
summarizes the temperature and frequencies specified for the tests.
Table 1. Characteristics for Dynamic Modulus tests.
Loading frequencies (Hz)
126.96.36.199 Flow Number
Creep is the tendency of a solid material to slowly move or deform permanently under the influence of stresses. The creep curve is created by loading the sample until it fails. The repeated load (dynamic) creep test is used to determine asphalt
deformation test can satisfactorily be used for evaluating asphalt concrete mixtures permanent deformation and fatigue characteristics.
Figure 7. Flow Number Test - schematic loading.
The flow number test is a uniaxial repeated load test in which a HMA sample is subjected to cyclic axial load, then the cumulative permanent deformation as a
been defined as “The number of load cycles corresponding to the minimum rate of change of permanent axial strain during a repeated load test.”
Results are usually presented in terms of cumulative permanent strain vs. load cycles. The test can be conducted with or without a confining pressure; the dynamic creep test usually better correlates with real field loading conditions
and permanent deformation and defined three distinct stages, namely the primary, secondary and tertiary stages.
Figure 8. Cumulative Permanent Strain vs. Load Cycles.
• Primary. Strain rate decreases with loading time.
• Secondary. Strain rate is constant with loading time.
• Tertiary. Strain rate increases with loading time.
Primary stage has high initial level of rutting, with a decreasing rate of plastic deformations, predominantly associated with volumetric change. Secondary stage has small rate of rutting exhibiting a constant rate of change of rutting that
Testing Equipment 23 is also associated with volumetric changes; however, shear deformations increase at increasing rate. While the tertiary stage has a high level of rutting predominantly associated with plastic (shear) deformations under no volume
volume a large increase in cumulative strain occurs within the tertiary zone.
This large increase is due to shear deformation and the number of load cycles.
The number of cycles at which the sample reaches this large increase - called
permanent deformation in the field is inverse proportional to the value of flow numbers (the lower the flow numbers the higher the deformation in the field).
Because of this correlation a minimum acceptable flow number requirement can be established for asphalt mixtures.
The secondary zone appears in the linear portion of the cumulative strain curve which is modeled by an equation of the form: 𝜀 𝑝
= 𝑎𝑁 𝑏 where:
= cumulative permanent strain number of loading cycles y-intercept of total cumulative strain curve slope of total cumulative strain curve
The values of “a” and “b” are usually calculated and reported for each mixture.
As mentioned before, the flow number is the number of test cycles required until tertiary flow starts in the mixture. The higher the flow number, the longer the time until the tertiary flow in the mixture stars. The flow number varies with
Rutting has been considered the most serious distress in flexible pavement and is caused by the accumulation of the permanent deformation (NCHRP 9-33-
The flow number test is based on the result from repeated loading and unloading of HMA sample and the deformation of the specimen is recorded as a
strain during the repeated loading. For 0.1 seconds a load is applied to the sample and then is followed by 0.9 seconds of rest time (dwells) is applied to
The flow number test will be performed at a high pavement temperature representative of the project location and pavement layer depth to evaluate the rutting resistance of the mixture. For the specific mixture used in this thesis project (AG22), the Swedish standard recommends a testing temperature of no
CYCLE 1 CYCLE 2
CONTACT DEVIATOR STRESS +/- 2%
REPEATED DEVIATOR STRESS +/- 2%
CONFINING PRESSURE +/- 2%
Figure 9. Repeated Load Test principle - schematic of flow number test loading.
A major assumption in the flow number test is that the stresses are distributed uniformly over the specimen. Friction between the loading platen and the specimen produces shear stresses which result in a deviation from this assumption. The effects of friction can be minimized by using long specimens.
The test specimen size for the simple performance tests was determined in an extensive specimen size and geometry study conducted in Project 9-19. The specimen diameter of 100 mm was selected to provide flow data that are independent of specimen size. The height to diameter ratio of 1.5 was selected to provide dynamic modulus and flow data that are independent of specimen height. The reduction of end friction in these tests was a significant factor in the recommendation for specimen size.
3.2.2 X-Ray Computed Tomography (CT) System
by the Royal Institute of Technology KTH, was utilized during this project. The system is a seven-axis universal X-ray imaging system designed for the inspection of large objects with a flat panel digital plate. The 5000 Series has an innovative top load cabinet design for easy part loading. It can accommodate a
Testing Equipment 25 variety of part shapes, sizes and weights and its scanning X-ray energy range intensity can be selected from two energy sources: 225kV and 450kV.
Figure 10. The CT machine X-5000.
When X-ray penetrates into the asphalt mixture, the ray intensity becomes attenuated due to the absorption of atoms in the material. The grey levels in a
CT slice image correspond to X-ray attenuation which is the proportion of
X-rays scattered or absorbed as they pass through the sample. Different
density have larger attenuation coefficients. In order to determine the internal structure of the specimen, one should calculate the attenuation coefficient via a process of computerized tomography.
Linear Attenuation Coefficients for different materials
Photon Energy in MeV
The main components of an X-ray tomography image system are:
• X-ray sources
• A series of detectors that measure X-ray intensity along multiple beam paths (linear or planar detector)
• A rotational specimen manipulator
• A collimator (used for linear detector array)
Figure 12. General mechanism of X-ray tomography scanning.
Testing Equipment 27
In the simplest approach, the source generates X-Ray radiation with certain intensity that passes through the specimen along different paths in several directions and a set of CT images is produced. The intensity of the X-Rays is measured before they enter the specimen and after they penetrate through it.
The intensities of the transmitted X-Rays are recorded on the detectors placed at the other side of the specimen. The scanning of a slice is completed after collecting the intensity measurements for a full rotation of the specimen. The specimen is then shifted vertically by a fixed amount (the slice thickness) and the entire procedure is repeated to generate additional slices.
The intensity values are used to calculate the distribution of the linear attenuation coefficient within a specimen. The resulting X-Ray CT image is a map of the spatial distribution of the linear attenuation coefficient. In this map, brighter regions correspond to higher values of the coefficient. Higher values of the attenuation coefficient correspond to regions with higher density. Therefore, since the linear attenuation coefficient at each point depends directly on the density of the specimen at that point it is feasible to distinguish the different features of HMA.
As mentioned before, the ability of the X-rays to differentiate materials depends
attenuation coefficient can be obtained to determine the energy level that is most appropriate for the asphalt concrete sample scanning.
The X-ray scanning process includes warm-up the system, scan the specimen, calibrate the system, and reconstruct the element. An industrial computed tomography software called efX CT was used for visualization, calibration and reconstruction. After reconstruction, Avizo Fire, a 3D Analysis Software for
Materials Science was used for obtaining and visualizing advanced qualitative and quantitative information on material structure images.
188.8.131.52 Warm up the system
Run the fxe-control application to start the warming process; place the beam blocker in front of the X-ray source to protect the detector from the unfiltered
X-rays generated during the warming up procedure.
184.108.40.206 Scan the specimen
This step should not be rushed as the quality of the CT depends on the quality of the acquisition. Ensure that there are no saturations in any area of interest at every degree of rotation, by selecting an appropriate voltage and current configuration. Additionally, try to have the gray level values of all areas of interest in the area of 15 to 75 percent of the total available gray levels of the operating bit depth.
The quantities of images used for the scan vary on part geometry (inside and out) and resolution sought. Use more images for higher detail and complex parts. Rectangular parts, for example, where the gray level values are border line when imaged through the thickest region, should have more images used during acquisition so that there will be more data for reconstruction, from the images of the thinner region. The typical number of images captured for CT scan is 360, 720, and 1440.
Figure 13. X-ray CT - sample setup.
The number of frames per second (fps) used are directly related to how much detail one wants to capture during the scan. A number of 2fps is usually ideal for small objects, but a greater value is recommended objects like asphalt cores.
There is a tradeoff between scanning-speed and scan quality when considering the number of frames per second.
The process of re-alignment of the detector, X-ray source and the scanning platform can be monitored using the CCTV monitor outside the scanning
220.127.116.11 Calibrate the system
Calibration routine includes two steps: capturing images of a calibration block and capturing images of the background.
Calibration Block Radiographs
The calibration block radiographs are used to create an accurate 3D rendering of the part. It also establishes the relationship between voxels and units of
Testing Equipment 29 measurement enabling the user to take accurate measurements from the volume data. The calibration block shall be imaged at the exact same geometry settings as the part. The X-ray tube energies can be varied though. In fact, they should be varied to maximize the contrast sensitivity between the calibration spheres and the surrounding material. The typical number of images used for this step is
It is imperative that no movements be made with the exception of the rotational axis. If the calibrations tool needs elevation it is recommended that it is place on a stable object. Capturing of these images can be done before or after capturing of the inspection radiographs. Additionally, the energies and filters used during acquisition may be altered for the calibration images. The idea is to maximize the contrast between the calibration spheres and the background. Several tests were run to be able to set the required filters for scanning the samples.
Figure 14. The calibration rod.
calibration process by using efX CT software. Positioning the calibration tool will require a degree of trial and error. The ideal situation will be to have the calibration tool travel from edge to edge of the image during a single 360 degree of rotation. The balls of the calibration tool must be at least 1 ball diameter from the edge of the image. It is also beneficial but not necessary to have the calibration tool reach from the bottom of the detector to the top. If the tool does not span from the top to the bottom, place the tool so that one portion of it reaches the top or bottom. This allows the creation of larger ellipses during rotation and better data for the software to build the volume off.
Figure 15. Images of the calibration tool.
The accuracy of the software in interpreting the geometric positioning of the
X-ray tube, detector, and manipulator is given by the number of calibration spheres that are visible.
The background radiograph is an optional step that can improve the quality of the 3D rendering. The background image is captured at the same geometry settings and energies as the part even if the background is being saturated during acquisition. This image is used by the software as a means to improve quality by subtracting that image from every part radiograph.
Artifacts such as beam hardening, ring artifacts, etc. generated by defective pixels which affect the quality of the acquired scanned image may be corrected during the detector calibration stage.
Reconstruction is done via mathematical process that converts the raw data into image slices. During this process the intensity data in the sinogram are mapped to CT values that have a range determined by the computer system (16 bit, 32 bit, 64 bit, etc.). For most industrial scanners, these values map to the grayscale in the image files produced by the systems.
Testing Equipment 31
The size of the reconstruction matrix is determined by the number of views and the number of measurements per view. Spatial resolution in an image can be improved by reducing the pixel size. However, after a certain limit smaller pixels do not increase the spatial resolution anymore and can induce artifacts in the image. Reconstructing with smaller pixels, under certain circumstances can be a useful technique.
After scanning the machine is shut down and the reconstruction phase begins.
The reconstruction program runs for around half an hour and constructs the image of the sample. The image is then further adjusted by using a histogram to achieve the best contrast and visibility of the sample mixture.
The Avizo Fire software package was used to perform analysis on the X-ray CT images. Avizo Fire has a broad range of software tools for obtaining and visualizing advanced qualitative and quantitative information on material structure images. The following techniques were applied to analyze aggregate structure and air void distribution:
• Data import from CT-scans
• Scaling, calibration, conversion, re-sampling
• Image enhancement, comprehensive filtering and convolution
• Thresholding and auto-segmentation, object separation, automatic labeling
• Direct volume visualization
• Automatic or interactive segmentation
• 3D geometry reconstruction
• Orthogonal, oblique, cylindrical, and curved slicing
• Quantification and analysis
• Results viewer with spreadsheet tool and charting
• Automatic individual feature measurements, 3D localization, and spreadsheet selection
• Automated statistics, distributions graphs
4.1 Field Core Samples
A total of twelve specimens were tested in this project; their correspondent identifications (IDs), tests performed and specific test objective are presented in
Table 2. The samples.
Sample Code Test performed
T1 Dynamic modulus
Loading 600 Cycles
Loading 600 Cycles
Loading 1200 Cycles
Loading 1200 Cycles
Effects of sample dimensions
Effects of sample dimensions
Dynamic modulus Master curve
Dynamic modulus Master curve
Microstrain and failure point
Microstrain and failure point
Microstrain and failure point
Microstrain and failure point
X-ray CT before and after loading
X-ray CT before and after loading
X-ray CT before and after loading
X-ray CT before and after loading
4.2 Simple Performance Tester Results
This section presents and discusses the results obtained from SPT. All tests were performed on unconfined samples. The dynamic modulus test was measured on samples T1, T2 to study the effects of the sample geometry. The dynamic modulus master curve was created using samples DM1 and DM2 to analyze the HMA. The flow number test was performed in samples FN1 to FN4 to record the microstrain accumulation during loading; Flow Number was afterwards used to calculate the range of the secondary stage of dynamic creep curve so that two interest loading points could be selected for further sample analysis using X-ray CT.
4.2.1 Dynamic Modulus Test
The SPT software reports the average dynamic modulus for the specimen at each temperature and frequency tested. At lower temperatures, when stiffness is higher the load had to be increased from 0.06 to 6 kN. Several tests were run to find an adequate load to use. The contact stress was adjusted automatically when the load was changed.
5 10 15
Figure 16. Dynamic Modulus - sample rotation.
Simple Performance Tester Results 35
To analyze the effects of the sample geometry (irregularities in the specimen induced during coring) several tests were performed with the sample rotated vertically, switching transducers (rotating sample 120°). A statistical analysis
modulus test was performed at 20°C three times by rotating sample T1 120° for
Dynamic modulus at 20°C was also measured applying different loads (2KN and 6KN) on sample T2; also dynamic modulus was obtained when the same sample was deliberately misaligned (not centered) during testing; the results are
Specimen loaded- 6kN
1000 Specimen loaded- 2 kN
0 5 10 15
20 25 30
Figure 17. Dynamic Modulus - different loads and misalignment.
During the dynamic modulus test several temperatures and load frequencies were set according to the actual road environment. For Sweden weather conditions, dynamic modulus should be measured at low temperature. Hirsch
dynamic modulus at low temperatures from the master curve calculated at higher temperatures.
In general, to generate the dynamic modulus master curve, several steps must be followed:
• Measure dynamic modulus for minimum two samples.
• Plot the measured dynamic modulus values on a logarithmic scale.
• Choose a reference temperature.
• Use superposition principle and calculate shift coefficients.
• Generate fitting curve according to the shift coefficients found.
The procedure followed to calculate the dynamic modulus master curve for the
As expected, one can see that dynamic modulus increases when the loading frequency increases; also dynamic modulus decreases with the increase in temperature.
0 5 10 20 25
Figure 18. Dynamic Modulus [ksi] vs. Frequency [Hz].
Figure 19. Dynamic Modulus [ksi] vs. Frequency [Hz] - logarithmic scale.
Simple Performance Tester Results 37
From the results at different temperatures the master curve is created by using the superposition principle and choosing a reference temperature; in this thesis
20°C was selected.
As part of a research project for evolution SPT, an Excel workbook capable to solve the specific modified version of the Mechanistic-Empirical Pavement
containing the Mastersolver developed by NCHRP to obtain the Witczak (Shift) coefficients and the master curve was used (All the calculations for the master
1,E-03 1,E-01 1,E+01 1,E+03
Reduced Frequency, Hz
Figure 20. Dynamic Modulus [ksi] vs. Reduced Frequency [Hz].
1,E-06 1,E-04 1,E-02 1,E+00 1,E+02 1,E+04 1,E+06
Reduced Frequency, Hz
Figure 21. Phase Angle [deg] vs. Reduced Frequency [Hz].
The phase angle measures how quickly HMA can recover from strain. A phase angle of 0 degrees is for elastic materials and one of 90 degrees corresponds to viscous materials; consequently HMA falls in between as a viscous-elastic
results are typical for HMA where the phase angles decreases with lower temperature and increases with higher temperature. For high temperatures the phase angle increases when the frequency is increased because the sample is more elastic than at low frequencies.
The Federal Highway Administration from USA in cooperation with asphalt researchers and manufacturers developed knowledge containing typical dynamic modulus results for several frequently used mixtures. In Sweden such information is not yet available; although some projects were started to develop
produced by different laboratories, using the same asphalt mixture are comparable; they found significant variability for the laboratories evaluated.
The variability for dynamic modules may have several reasons: different machines, different environmental condition, different loads, different testing procedures, etc; these conclusions highlighted how complex and sensitive dynamic modulus test on asphalt mixtures can be.
As a reference; the dynamic modulus master curve calculated from this project, was compared with test results produced in other two studies: Richard Nilsson’s
1,E-03 1,E-01 1,E+01 1,E+03
Reduced Frequency, Hz
Fit curve Oscarsson
Fit curve Nilsson
Figure 22. Master Curve comparison (Test results, Oscarsson and Nilsson).
Simple Performance Tester Results 39
This thesis test results at 35°C and the results from Nilsson (at 30°C) are rather similar with small differences at lower frequencies and high temperature. They might also have used a lower load, or because the dynamic modulus tests were unconfined (Nilsson’s curve is similar when the difference between confined
There are large differences between this thesis test results and the results of
Oscarsson. These differences can have several reasons: not enough data detail available, different testing machine, different air voids, different compaction methods, different temperatures, etc. Similar conclusions can be drawn from
1,E-06 1,E-04 1,E-02 1,E+00 1,E+02 1,E+04 1,E+06
Reduced Frequency, Hz
Figure 23. Phase Angle comparison- (Test results, Oscarsson and Nilsson).
a statistical comparison (regression analysis for the test of effects between subjects) for a reference temperature (20°C) calculated with the IMB SPSS statistic suite version 19.
The statistical analysis provides a better visualization of the differences between thesis test results for dynamic modulus and the data from Nilsson and
the three data sets: thesis data, Nilsson, Oscarsson. One can see that the median for thesis data is very close to the median for Nilsson, while as expected from previous discussion the mean from Oscarsson is not similar.
Another statistic analysis based on stem-and-leaf comparison is presented in
data and Nilsson’s are close together. One can conclude that the results in the thesis are similar enough to other field data, considering the complexity and variability of dynamic modulus test.
Figure 24. Comparison of Dynamic Modulus for different labs at a reference temperature of 20°C.
Figure 25. Data comparison - stem-and-leaf.
Simple Performance Tester Results 41
4.2.2 Flow Number Tests
The resistance of asphalt mixtures to permanent deformation is measured by flow number (dynamic creep test). In response to creep loading, both static and cyclic, asphalt concrete materials develop permanent deformation which accumulates with time or number of load repetitions. This accumulated permanent deformation is the cause of rutting in asphalt pavements. In this thesis the flow number (dynamic creep tests) was performed to determine after how many load cycles the typical sample used in this thesis is damaged
According to the Swedish test recommendations the creep test should be conducted at maximum 40°C for this specific mixture (AG22); for this work, all
Table 3. Flow Number - test results.
Sample Code Micro(µ)Strain Loading Cycles Minimum Strain Rate
FN3 24077 1390 18.2
FN4 30641 1520 16
Flow number was measured for five samples (FN1 to FN4 and DM1); these
modulus measurements that means that the sample has been previously subjected to different temperatures and small loads which may explain the its low strain; it is recommended to use new samples for different tests.
The average failure point for the asphalt mixture was estimated to be around
1400 load cycles, according to the Flow Number test performed according to the NCHRP specification. An example of the results of the Flow Number test
In order to analyze the aggregate structure and air voids distribution of the specimens with the X-ray CT system, two reference loading points were selected; these loading points were specifically chosen such that the sample is not in the failing stage (tertiary stage of the creep curve).
500 1000 1500
Figure 26. Flow Number for different samples.
Accumulated Permanent Strain- Sample FN1
Flow Number = 1 505
Minimum strain rate = Flow number
Figure 27. Flow Number Test Result for Sample FN2.
Simple Performance Tester Results 43
Knowing the approximate number of the cycles when the sample breaks
selected to be close to the limit between primary and secondary stages (around
600 load cycles); while the reference point 2 was intended to be close to the limit between the secondary and tertiary stages, without reaching failure
Figure 28 Creep curve - test stages.
The dynamic modulus results for all CT samples before and after loading, as
• CT1 and CT2 have similar dynamic modulus and big difference in microstrain accumulation. Interestingly enough,
• CT3 and CT4 have similar dynamic modulus (Lower that CT1 and CT2) and big difference in microstrain accumulation.
• CT1 and CT3 have similar microstrain accumulation, knowing that CT3 was subjected to higher number of load cycles. CT2 and CT4 show similar trend.
• CT1 and CT2 have normal behavior in dynamic modulus as it decreases after loading but, unexpectedly, for CT3 and CT4 the dynamic modulus increases after loading. This behavior could eventually be explained by higher segregation in the CT3 and CT4 set. From visual inspection, it was concluded that samples CT1 & CT2 looked coarser and had smoother external surfaces, than samples CT3 & CT4.
1200 1400 200 400 600
Figure 29. Microstrain Samples CT1 to CT4.
Table 4. Samples CT1-CT4 – Dynamic Modulus and Load Cycles.
Dynamic modulus (MPa) before loading after loading
CT1 600 6195 5582 12159
CT2 600 6109 4726 16114
CT3 1200 4951 6204 10645
CT4 1200 4626 6062 15705
The main finding from SPT was that significant variability in dynamic modulus
(before and after loading) and microstrain accumulation from flow number test was identified; most likely, induced by irregularities in the sample geometry
(wall surface) and segregation in the field cores.
X-ray Computed Tomography Results 45
4.3 X-ray Computed Tomography Results
As mentioned before, the main objective of using X-ray CT was to evaluate aggregate structure and air void distribution for each sample before and after
SPT loading; voxel size and resolution for each reconstructed volume are
Table 5. Volume resolution.
Sample Code Voxel size (microns) Resolution (voxels)
CT1 Before Loading 91.000 x 91.000 x 91.000 1453x1843x1414
CT1 After Loading 93.000 x 93.000 x 93.000
CT2 Before Loading 91.000 x 91.000 x 91.000
CT2 After Loading 93.000 x 93.000 x 93.000
CT3 Before Loading 105.000 x 105.000 x 105.000 1286x1664x1334
CT3 After Loading 105.000 x 105.000 x 105.000 1185x1660x1216
CT4 Before Loading 105.000 x 105.000 x 105.000 1303x1962x1338
CT4 After Loading 105.000 x 105.000 x 105.000 1274x1722x1286
During the Post-processing stage, beam hardening which is the most commonly encountered artifact in CT scanning was detected, regardless the use of 5mm cupper filtering plates during testing. Beam hardening causes the edges of an object to appear brighter than the center, even if the material is the same
increase in mean X-ray energy or "hardening" of the X-ray beam as it passes through the scanned object. Because lower-energy X-rays are attenuated more readily than higher-energy X-rays, a polychromatic beam passing through an object preferentially loses the lower-energy parts of its spectrum. The end result is a beam that, though diminished in overall intensity, has a higher average energy than the incident beam.
In X-ray CT images of sufficiently attenuating material, this process generally manifests itself as an artificial darkening at the center of long ray paths, and a
corresponding brightening near the edges. In objects with roughly circular cross sections this process can cause the edge to appear brighter than the interior.
Beam hardening can be a pernicious artifact because it changes the grey level of a material depending upon its location in an image. Thus, the attempt to utilize a single CT number range to identify and quantify the extent of a particular material can become problematic. One measure that is sometimes taken is to remove the outer edges of the image and analyze only the center. Although this technique removes the worst part of the problem, the artifact is continuous and thus even subsets of the image are affected. Furthermore, if the cross-sectional area of the object changes from slice to slice, the extent of the beam-hardening artifact also changes, making such a strategy prone to error.
In this thesis work, when beam hardening was detected, it was decided to crop
1.5 cm form the top and from the bottom of each sample in order to minimize the beam hardening effect.
4.3.1 Air Void Distribution
It is known that the air void distribution strongly influences the mechanical response of HMA mixtures
Considering that the samples for this study are field cores; then uneven air void distribution induced during the construction process may be expected
Nilsson reported that a reduction in air void content caused an increase in asphalt mix stiffness. Air voids in the center of a field core may be significantly lower than those of the entire cylinder (about 0.2% to 1.5%, depending on the type of mix)
The distribution of air voids in the sample CT1 along the vertical direction,
relatively homogeneous between 0-40mm (top), has a high and low peak between 40mm and 60mm (middle) and has a wide increase between 60mm and
120mm (bottom). After loading, the overall air void content of the sample slightly decreased although locally it varies from low at the top to larger at the bottom.
The concentration of air voids before and after loading for sample CT2 is given
sample CT1 and varies widely between 8% and 16%. This non-homogenous air void distribution might be caused by irregularities during the construction process. The trial error process during construction to obtain an 18 cm thick asphalt layer seems to be the main factor for the significant variation of air void content among samples. Furthermore, air void distribution before and after loading does not change; this implies that no re-accommodation of particles may be attributed to the applied load.
X-ray Computed Tomography Results 47
0 20 40 60 80
Sample CT1 Before Loading
Sample CT1 After Loading
100 120 140
Figure 30. Air voids distribution for sample CT1 before and after loading.
0 20 40
Sample CT2 Before Loading
Sample CT2 After Loading
100 120 140
Figure 31. Air voids distribution for sample CT2 before and after loading.
Sample CT3 Before Loading
Sample CT3 After Loading
0 20 40 80 100 120 140
Figure 32. Air voids distribution for sample CT3 before and after loading.
0 20 40
Sample CT4 Before Loading
Sample CT4 After Loading
100 120 140
Figure 33. Air voids distribution for sample CT4 before and after loading.
X-ray Computed Tomography Results 49
void content from top to bottom with almost double content at the bottom. After loading the air void content at the top decreased while on the bottom increased; a small overall increase in air voids through the whole sample was loadinduced.
air void content at top and bottom; also, an increase of air voids content after loading can be observed.
The average air voids content for all the
see that the air void content of sample CT1, which strain level, is close to one percent, decreases after loading. This could be explained according to Tashman
voids tended to contract and the existing microcracks tended to close up as indicated by the negative percent change in the void content at one percent strain” during triaxial compression tests of asphalt mixtures at high temperatures.
Table 6. Total air voids content for samples CT1-CT4 before and after loading.
Before loading (%)
After loading (%) 4.2 10.5 5 5.9
For sample CT2 the air void content before and after loading stayed constant; this behavior appears due to variation in air void content with the sample height and because some of the air voids compact and some increase as presented in
Samples CT3 and CT4 (loaded with 1200 cycles) present an increase in air void content, probably induced by the movement of aggregates under higher cumulative load.
One of the options available in Avizo Fire suite is to measure and classify the dimension of the air voids. The air voids histogram, before and after loading
voids from class [7535-75357] decrease and migrate to all other classes but mostly to class [735-7535] which grew more than twice. This suggests that densification of the material due to re-accommodation of particles has occurred.
Air voids dimension classification histogram before and after loading for
also see that sample CT4 has no middle size air voids.
Sample CT1 Before loading
Sample CT1 After loading
7.5- 75 75- 753
Size void classification (mm
753- 7535 7535- 75357
Sample CT4 Before loading
Sample CT4 After loading
7.5- 75 75- 753
Size void classification (mm
753- 7535 7535- 75357
Figure 34. Air voids size histogram before and after loading for samples
CT1 and CT4.
X-ray Computed Tomography Results 51
Sample CT2 Before loading
Sample CT2 After loading
7.5- 75 75- 753
Size void classification (mm
753- 7535 7535- 75357
Sample CT3 Before loading
Sample CT3 After loading
7.5- 75 75- 753
Size void classification (mm
753- 7535 7535- 75357
Figure 35. Air voids size histogram before and after loading for samples
CT2 and CT3.
The air voids histograms for CT2 (top) and CT3 (bottom) can be observed in
ones in all the samples except CT1.
4.3.1 Aggregate Structure
In order to verify if segregation played a role in the variation of SPT results, the
X-ray CT system was utilized to analyze the field cores.
samples; it can be noticed that CT1 has less air voids, bigger particles, and better aggregate interlock than CT4. In addition, segregation was detected in sample CT4 (fine aggregates localized at the bottom) and concentration of bigger air voids in the middle. Sample CT4 looks like a two-layer system; having a coarse layer on the top and a fine-medium layer at the bottom.
a) Sample CT1. b) Sample CT4.
Figure 36. X-ray CT image.
X-ray Computed Tomography Results 53
A 3D reconstruction of the sample CT1 was done by using Avizo Fire; some
be cropped. Avizo Fire suite was also used to segment the aggregate particles
Figure 37. Distribution of aggregates in a 3D volume of interest of sample CT1.
Finally, the corresponding particle size distribution was calculated for the 3D
asphalt mixture. The 3D volume of interest presents uneven sizes of aggregates: a larger number of small-to-medium size than medium-to-large size ones.
1000- 2000 2000- 3000
Size aggregates classification (mm
3000- 4000 4000- 5000
Figure 38. Example of aggregate size classification in the 3D volume of interest.
The application of X-ray CT and complex 3D image analysis are very powerful applications and it could eventually provide a non-destructive tool for verification of gradation of asphalt mixtures.
SPT results from asphalt field cores, including dynamic modulus (before and after loading) and microstrain accumulation (flow number), exhibited significant variability; most likely, induced by irregularities in the core shape.
The analysis of aggregate structure and air voids distribution performed trough
X-ray CT, clearly identified segregation in the asphalt mixture as a another key factor that induced variability in SPT results.
SPT Dynamic modulus values measured during this work are comparable with data obtained from other research projects that investigated typical Swedish asphalt mixtures.
Significant variability in flow number results was found; in consequence the number of cycles to failure was not enough to induce failure of the sample. Not even visible damage was induced under the applied number of load repetitions.
Beam hardening was detected during the reconstruction process, regardless the use of 5mm cupper filtering plates during scanning; this increased the difficulty of key post-processing tasks including quantification of air voids and segmentation of aggregate particles.
The X-ray CT technology provides valuable information about the internal structure of asphalt mixtures, including aggregate particle size distribution and analysis of air voids structure (size, distribution and connectivity). In summary,
X-ray CT is a very powerful and promising technology that allows enhancing understanding of asphalt mixture failure mechanisms, which may eventually generate further development of asphalt mixture design procedures and/or optimization of pavement construction methods.
Research should continue to further develop and refine the promising X-ray CT analysis; this technique may generate fundamental knowledge about the effects of internal structure of asphalt mixtures on asphalt field performance; this could eventually lead to the design and construction of rutting and cracking resistant asphalt mixtures.
Specifically, the following areas also need further development:
• X-ray CT scanning technique.
• X-ray CT reconstruction software
• X-ray CT post-processing
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The appendix contains important information that complements the thesis.
A Simple Performance Tester (SPT)
A.1 SPT- test procedures
General procedure steps for SPT
To ensure the tests and the results are ran properly, the following checklist should be followed:
1. Run a dummy samples to ensure the machine is properly calibrated and installed.
2. The compressed air pressure supplied to the machine has to be in the required ranges; Use a air pressure gauge to verify.
3. Check the sample dimensions using high accuracy measuring instruments to verify the angles and to inspect wall surface weaviness. These verifications are very important as imperfect samples will highly affect the tests results.
4. Careful choose the glue (epoxy) used works yours materials, it hardens quickly (approximately 5 minutes) and resists at the temperatures to be used during the testing.
5. The horizontal faces of the studs have to be parallel when glued (so that the supports for holding the LVDTs are parallel and the LVDT is perfectly vertical).
6. When fixing the LVDT sensors check that the cables do not touch the chamber or the sample because otherwise errors can be introduced.
7. The LVDTs have to be adjusted properly for each test (close to zero or close to maximum range). This can be achieved via the screws in the sensors. One has to be careful as they are rather sensitive.
Dynamic Modulus test procedures
For Dynamic modulus tests these steps should be followed:
1. Ensure that the sample is perfectly centered on the loading plates on both sides.
2. The metallic ball has to be centered and aligned with the upper plate.
3. Place and adjust the LVDTs using the ranges shown in the Dynamic
Modulus software. Make sure all the LVDT sensors are as close as possible to 0. If you run into issues you can rotate the sample and try to switch between places in which you mount the sensors. When you are finished you should set the sensor readings from the software GUI to zero.
4. Make sure you properly condition the sample to the test temperature (for temperatures > 10°C, approximately 2 hours; for temperatures bellow 0°C minimum 8 hours in the environmental chamber).
5. The sample diameter and height must be precisely measured in several locations (6 diameter measurements and 3 for height) with a precision of
6. To ensure proper function of the equipment the sample has to be conditioned in the SPT environmental chamber for at least 20 minutes before starting the tests. In this time the operator can setup the test.
7. In the software give proper names and comments to the tests you are running, including the sample code or number, the date and time, the test specifics, etc.
8. Choose the frequencies you require from the machine in the GUI.
9. Run the test.
10. Inspect the results and verify that they are meeting the specifications
Flow Number (repeated load test) procedures
For this test, the following steps should be followed:
1. The tests have to be run at a temperature range between 30°C and 40°C. It is recommended that the samples are placed in the external environmental chamber for at least 4 hours.
2. Experience might help to determine the needed deviatoric stress and the contact stress. In this project trial tests were performed to establish the appropriate stresses (30kN for deviatoric stress and 70kN for contact stress).
3. End friction reducers should be placed above and below the specimen.
Usually, latex membranes sheets separated by silicon grease are used.
4. Because the samples are heated they start to soften and one has to make sure that they use the proper amount of glue so that the studs do not move. Try to rotate the sample so that the studs are glued to aggregates in the mix and as little as possible to the mastic
5. The LVDTs must be setup at maximum negative range (-0.5mm)
6. Setup parameters in the software interface (contact and deviator stress and termination settings: maximum microstrain or test duration)
7. Run the test and make sure you observe it because if the sample breaks the
LVDTs fall and can be damaged. In case of emergency stop the test.
A.2 SPT- Settings
Table 7. Flow Number Assumptions and SPT system features.
Representative volume element
Consistent pulse loading
Consistent rest period
Uniform Stress state
SPT System Feature
100 mm diameter h/d ratio of 1.5
Load standard error
Tolerance on maximum load
Specimen size, h/d=1.5
Smooth parallel ends and loading platens
Table 8. Data quality statistics requirements for Dynamic Modulus measured with SPT.
Data Quality Statistics
Peak to Peak Strain
Load standard error
Deformation standard error
In direction of applied load
75 to 125
µstrain unconfined tests
85 to 115
µstrain confined tests
Table 9. Statistical analysis of influence in sample rotation.
21360323.55 12 1780027
F p-värde F-krit
2 22281.51 0.01 0.98 3.88
Totalt 21404886.57 14
Because F is much smaller then F-crit value, the differences between measurements is not statistically significant.
A.3 Dynamic Modulus Master Curve equations
In the following, the workbook equations developed by NCHRP is presented and the parameters and master curve are obtained. The workbook is used in conjunction with the Simple Performance Test System to develop dynamic modulus master curves. It has the capability to solve a modified version of the
Mechanistic-Empirical Design Guide master curve equation, Equation 1. log
γ log( log
E* = dynamic modulus
= reduced frequency, Hz
Max = limiting maximum modulus, ksi
Min = limiting minimum modulus, ksi
= fitting parameters
The reduce frequency is computed using the Arrhenius equation, Equation 2. log
= reduced frequency at the reference temperature
= loading frequency at the test temperature
= reference temperature,
T = test temperature,
= activation energy (treated as a fitting parameter)
Substituting Equation 2 into Equation 1 yields the form of the master curve equation that is fitted using this workbook.
The shift factors for each temperature are given by Equation 4. log
a(T) = shift factor at temperature T
= reference temperature,
T = test temperature,
= activation energy (treated as a fitting parameter)
The maximum limiting modulus is estimated from mixture volumetric properties using the Hirsch model and a limiting binder modulus of 1 GPa
(145,000 psi), Equations 5 and 6.
* | max
4 , 200 , 000
435 , 000
4 , 200 , 000
435 , 000 (
435 , 000 (
435 , 000 (
= limiting maximum mixture dynamic modulus
VMA = Voids in mineral aggregates, %
VFA = Voids filled with asphalt, %
Figure 39. Dynamic Modulus [ksi] vs. Reduced Frequency [deg] shifted - at different temperatures.
Table 10. The SPT results and Master Curve calculation.
Average DM1 DM2
B X-ray CT Results
Figure 40. X-ray CT image - sample CT3.
Figure 41. Aggregates in the 3D volume of interest- sample CT1.
* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project