Using Imaging Techniques to Analyze the Microstructure of Asphalt Concrete Mixtures: Literature Review

Abstract

The performance of asphalt concrete (AC) mixtures depends highly on their internal structure and the interaction of the mixture components under different loading conditions. Imaging techniques provide effective tools that can assess the microstructure and failure mechanisms of materials. Imaging techniques have been used in recent research studies to examine and analyze the evolution of the internal structure of AC mixtures resulting from traffic and environmental loading. Increasing knowledge of the microstructural properties and mechanical behaviour of AC mixtures could improve the design process and enable researchers to develop more accurate prediction models for the long-term performance of pavements. This paper reviews three imaging techniques which were used to characterize the microstructure of AC mixtures. These three imaging techniques are digital camera imaging, scanning electron microscope (SEM) imaging, and X-ray computed tomography (CT) scan. Extensive insight has been presented into these imaging techniques, including their principles, methods, sample preparation, and associated instruments. This review provides guidelines for future research on using these imaging techniques to analyze the microstructure of AC mixtures and assess their long-term performance.

1. Introduction

Asphalt concrete (AC) is a paving material widely used in North America and worldwide. There are more than 4 million kilometres of paved roads in North America, and more than 93% of them are asphalt roads. More than 55% of North American asphalt roads are in poor or fair condition and require major maintenance or rehabilitation which costs millions of dollars annually from the budget of transportation agencies in North America [1].

Understanding the failure mechanism of AC requires consideration of the heterogeneous nature of the mixture. Mechanistic models for analyzing AC mixtures focus on macroscopic behaviour [2]. These models were developed based on continuum mechanics theory, which assumes that AC is a homogeneous mixture. Its geometric and physical characteristics (such as aggregate shape, spatial position, aggregate texture, and gradation) are ignored in this theory. This assumption is made due to the difficulty in quantifying and measuring AC microstructures and the randomness of the aggregate distribution in the mixture. However, the internal geometry and the physical properties of AC mixture components can significantly affect the overall performance of pavement structures [3].

Microstructure characteristics have an impact on the design of AC with satisfying performance [4]. Most previous studies analyzed the microstructure of asphalt mixtures using experimental tests such as the ignition method to estimate asphalt binder content, the wash gradation method to estimate the aggregate gradation, and stranded test methods to estimate the air voids percent in compacted AC mixture sample [5,6,7]). These experimental tests are time-consuming and labor-intensive and involve high variability in results.

The need for fast, user-friendly, and practical methods of analyzing the microstructure of AC mixtures has become the concern of many researchers. In the mid-1990s, imaging techniques were used as an effective method of analyzing the microstructure of AC mixtures. Imaging techniques provide a non-destructive analysis of the AC mixtures, which allows for repeated measurements of the same sample without altering or damaging it. The non-destructive nature of this testing allows the same sample to be monitored after exposure to environmental conditioning and evolution of its internal structure. The high resolution and accuracy of image-based analysis provide detailed information on particle size, shape, distribution, and interconnectivity, along with the detection of anomalies or defects.

Imaging techniques allow researchers to gain a deeper understanding of the microstructural characteristics and behaviour of asphalt mixtures. This knowledge is essential for the development of more effective mix designs and the identification of factors affecting mixtures’ durability and performance. The potential for automation and standardization of image-based analysis enhances comparability and reproducibility across studies or laboratories. While imaging techniques should be used in conjunction with traditional tests, they offer valuable insights and advantages for initial screenings, quality control, and research in asphalt mixture analysis.

This paper provides a review of the use of digital camera, scanning electron microscope (SEM), and X-ray computed tomography (CT) scanning in the analysis of the micro-structure of AC mixtures. The paper provides guidance for researchers and practitioners on the use of these imaging techniques to analysis AC mixtures, as well as selection of the appropriate methodology and analysis methods. The paper contributes to the advancement of knowledge and understanding in the field and fosters further innovation in asphalt mixtures analysis and design.

2. Digital Camera Imaging

Digital cameras produce digital images of objects. A digital image is a composition of pixels that gives image data of a sample surface as it directly computes the absorption and dispersion characteristics of the area under assessment. A pixel is the smallest element in an image, and the number of bits per pixel (bpp) controls how many distinct colors can be represented by one pixel. Extracting significant information from digital images can be carried out using digital image processing (DIP) techniques, which are various mathematical procedures.

2.1. Principle and Method

Digital cameras differ mainly in terms of sensor resolution (up to 50.6 megapixels), capture speed, and angle of view. DIP is a subcategory of digital signal processing that uses computer functions and algorithms to extract significant information from digital images [8]. DIP includes several processes; one of them is digital image segmentation (DIS). DIS involves subdividing the image into homogeneous regions based on a precisely defined quality related to the aim of the segmentation. DIS is the first step in distinguishing the sub-regions in an image that contain objects of interest from the other parts of the image in order to determine what is called the region of interest (ROI). DIP can also generate 3-D images of the surface of AC samples using several 2D images taken from different angles, which can be used to describe the surface texture [9].

2.2. AC Microstructure Analysis Using Digital Camera

The simplicity, availability, ability to capture high-quality images, and low cost of digital cameras have motivated researchers to use them to analyze the internal structure of AC mixtures. By generating digital samples from optically scanned surface images of laboratory-prepared asphalt specimens, Xu G et al. [10] captured the microstructure of stone-based materials. Through image processing techniques, Sun P et al. [11] extracted valuable information regarding the distribution and size of pores within AC mixtures, as well as their connectivity. Moreover, the use of fractal analysis techniques can provide insight into the complexity and heterogeneity of crack patterns within AC mixtures [12].

Digital cameras have been used to acquire quantitative measurements of AC microstructure with consideration of the random nature of aggregate distribution on micro and macro scales [13,14]. By using 2D digital images and DIP techniques, Sefidmazgi et al. [15] could estimate the aggregates’ size, gradation, distribution, orientation, and other properties such as asphalt content (AC%), air void percent (AV%), and other properties of AC mixtures. Mineral aggregates compose more than 90% by volume of AC mixtures. Two types of mineral aggregates are used in AC mixtures: coarse aggregates and fine aggregates. Some coarse aggregate characteristics, related to the shape of aggregate particles, can be extracted from top and side views in digital images of free or loose aggregate samples [16,17].

Aggregate gradation affects the microstructure of AC mixtures. Gradation of coarse aggregate can be accurately estimated from digital images of vertical and/or horizontal cross-sections of compacted AC samples [18]. Planner areas of aggregate particles in digital images are compared with the area of sieve openings to estimate the percentages of retained particles on each sieve. The estimated gradation from this technique is known as a planner gradation. To estimate the original gradation, some assumptions about the aggregate shapes have to be made and applied through shape correction factors. Coarse aggregate orientation and aggregate-size distributions can also be estimated from the same cross-section images using the scaled dimensions of the particles and the distances between them [19,20].

Estimated aggregate-size distributions are generally biased because of the random cut of aggregates [21]. However, the measured dimensions can also be corrected to obtain accurate estimates [22]. One of the challenges in the cross-section images is the contact between two or more aggregate particles, where they can be recognized as one larger aggregate, see Figure 1. To separate these contacted aggregates, the watershed algorithm was the image processing [23].

Air void percentage, which is typically 4% to 6% of the volume of an AC mixture, affects the service level and service life of AC mixtures. Higher or lower air void content than optimum will adversely affect the performance of the AC mixture. Integrated models have been built to estimate volumetric indices from the correlations between the known air void content and the extracted parameters from digital images of AC mixture cross-sections, such as planner areas and dimensions of the coarse aggregates and the spaces between them. Cellular phone images and the DIP technique have been applied by Obaidat et al. [13] to analyze AC microstructure and estimate volumetric indices. Air void content is influenced by traffic load and environmental conditions. After exposure to traffic loading, aggregate distribution and orientation change, which affects the existing air void distribution and adds more air voids and micro-cracks to the mix [24].

Klimczak et al. [25] presented a methodology for efficient and reliable recognition of AC microstructure via image processing, which enables further numerical analysis. Digital camera imaging was used to reduce the cost of finite element mesh generation for models and find solutions to heat flow and elasticity problems.

2.3. Discussion

The digital camera imaging technique is an effective means of analyzing AC microstructure. The geometric and volumetric characteristics of AC mixtures can be estimated via the processing of 2D images of cross-sections from compacted AC samples. The advantages of this technique are simplicity, availability, accessibility, accuracy, and low cost. However, obtaining accurate results through digital camera imaging requires careful consideration of factors such as sample preparation, lighting conditions, and image processing algorithms.

On the other hand, this technique is a destructive testing technique as the AC sample needs to be cut into parts to obtain horizontal and/or vertical cross-sections that represent the internal structure of the AC mixture. These cross-sections must be cut slowly to achieve smooth and flat sections without any damage and to be sure that the details of the microstructure can be captured clearly.

Digital camera images provide information about the AC microstructure at a planner level as this imaging technique cannot penetrate the sample surface. The dimensions of the objects shown in the images are projected or planner dimensions, not the real dimensions. Therefore, some assumptions and/or shape correction factors are needed in this technique.

Digital camera imaging is an effective technique to characterize the coarse aggregate properties within a compacted AC sample. However, the fine aggregate properties cannot be accurately estimated using this technique, especially at lower image resolutions. Further research is needed to better characterize the fine aggregate properties using digital camera images. The percentage of air voids in the total mix (VTM%), the percentage of air voids in the mineral aggregate (VMA%), and the percentage of air voids filled with asphalt (VFA%) can be estimated from digital images by using the integrated models in the literature.

However, it is challenging to distinguish the air void portions by using a 2D image for a cross-section of the AC sample. An efficient method of evaluating the morphology of air voids in AC microstructure is still needed. Moreover, this technique is limited to evaluating geometric and volumetric indices of AC mixtures with no emphasis on investigating the impact of environmental fluctuations on the behaviour of AC mixtures. Table 1 summarizes the features of using digital camera imaging techniques to analyze AC microstructure.

3. Scanning Electron Microscope (SEM)

A scanning electron microscope (SEM) is a multi-functional tool that can be used to observe and analyze heterogeneous materials, such as AC mixtures, at a very small scale (micrometre to nanometre scale) [26]. An SEM with a 1.5 KV operating voltage can observe up to 1-nanometre features in a sample [27]. Samples must be conductive, or coated with a conductive material, and ground-isolated to be analyzed via SEM [27].

3.1. Principles and Method

SEM utilizes a focused beam of accelerated electrons in an ultra-high vacuum to avoid scattering. The beam can penetrate up to 3 μm into the sample surface and provides signals via secondary electrons (SEs) and backscatter electrons (BSEs). SEs offer surface profile information while BSEs reveal sample composition [28,29,30,31].

The size of the sample that can be accommodated in SEM is determined by the dimensions of the specimen chamber or stage within the instrument. SEM instruments typically have a chamber with a diameter or width ranging from a few centimetres to several tens of centimetres. For standard SEM analysis, the sample size is usually smaller, ranging from a few millimetres to a few centimetres in diameter or width.

SEM requires clean, dry, and conductive samples, which imposes some limitations on the evaluation of some materials [32]. A non-conductive sample can be coated with a conductive material to reduce sample charging and enable SEM images to be taken, but this technique is not always effective in producing good SEM images [32]. In the 1980s, the environmental scanning electron microscope (ESEM) was introduced to image wet, uncoated, and dirty samples without any surface preparation [33,34]. ESEM is a variation of the scanning electron microscope (SEM), which enables electron micrographs to be acquired from specimens that are wet, uncoated, or both. ESEM creates a gaseous environment within the specimen chamber, which provides flexibility for imaging specimens that would require special preparation or coating in traditional SEM. ESEM has limited availability compared to SEM due to technical expertise requirements, higher maintenance and operating costs, and application-specific use for studying wet or uncoated samples.

3.2. AC Microstructure Analysis Using SEM

SEM provides high-resolution images of the internal structures of AC mixture samples, allowing mineral distribution and void characteristics to be analyzed in detail. Additionally, SEM is capable of generating 3D images of the sample’s surface using focused ion beam milling and tomography techniques [35]. Moreover, energy-dispersive X-ray spectroscopy analyses can be conducted using SEM to identify the elemental composition of individual minerals in AC samples [36].

For the testing of asphalt mixtures, ESM samples are typically prepared as thin slices with a diameter or length of up to 50 mm and a depth of up to 10 mm. The ESM sample can be disc-shaped or prism-shaped, depending on the size and configuration of the original asphalt mixture specimen; see Figure 2. It is important to ensure that the prepared samples are representative of the desired areas or features of interest within the asphalt mixture for accurate SEM analysis. The samples require additional treatment to be conductive, coated with a conductive material, and ground-isolated.

SEM was used to study the mechanical behaviour and microstructure features of interfacial transition zones in AC mixtures containing recycled concrete aggregate. Results showed that SEM images revealed the presence of various types of mineral phases and voids, which contributed to the mechanical behaviour and durability of the AC mixtures [37].

Underwood and Kim [38] used SEM to estimate fine aggregate matrix (FAM) gradation. FAM refers to a combination of bituminous binder, air voids, filler, and fine aggregates. SEM was also used to estimate the amount of adsorbed bituminous binder, which is a critical parameter in AC mix design and affects the stiffness, cost, and durability of AC [39,40]. Ameri et al. [41] utilized SEM analysis to examine the surface texture of natural limestone and electric arc furnace steel slag aggregates. The results showed that steel slag aggregate had higher porosity and roughness compared to limestone aggregate. Incorporating coarse steel slag aggregate into warm mix asphalt (WMA) mixtures improved properties such as Marshall stability, resilient modulus, tensile strength, moisture resistance, and resistance to permanent deformation. Based on the findings of this research, the use of coarse steel slag aggregate in WMA mixtures was recommended.

SEM images provide valuable insights into the fracture mechanism of AC mixtures. These images can show the initiation and progression of micro-cracks within AC caused by adhesion failure between coarse aggregates and the fine aggregate matrix (FAM), as shown in Figure 3. Additionally, SEM can reveal cohesion failure within the asphalt binder. To characterize the impact of binder aging on AC, SEM can be employed when examining changes in microstructure, such as the formation of cracks or the degradation of binder–aggregate bonds. By comparing SEM images of aged and unaged samples, researchers can assess the effects of binder aging on the structural integrity and performance of AC. Using SEM images, Fini et al. [42] found that after binder aging, the sample surface changed from a smooth surface to a tough surface with micro-cracks.

Kim et al. [43] used SEM to study fracture healing mechanisms in sand asphalt mixtures. They tested three types of bituminous binders: Texaco AC-5, Texaco AC-20, and styrene-butadiene rubber latex-modified Texaco AC-5, and they could explain the incompatibility of latex with Texaco AC-5.

By using an optical microscope and SEM, Max et al. [44] evaluated the self-healing efficiency and thermal stability of double-walled microcapsules mixed into small-scale AC samples. The used microcapsules did not break when subjected to high temperatures and had adequate thermal stability. SEM image analysis showed that the size of micro-cracks in the mixtures was reduced under healing conditions for a period of 0 to 6 days. After 6 days, the healing efficiency was 70–83% at room temperature and 50–70% at high temperatures.

Dong et al. [45] used SEM to evaluate the impact of freeze–thaw (F–T) cycles on AC cross-sections. After F–T cycling, the SEM images indicated that the mixture’s surface hadchanged from a smooth to a rougher surface. SEM images also showed that the micro-cracks within samples increased in size with the increase of F–T cycles, which led to damage to the sample structure. Williams and Miknis [46] used the environmental scanning electron microscope (ESEM) to evaluate the impact of F–T cycling on aggregates coated with binder, and they found that F–T cycling caused an increase in cracking and stripping between the asphalt binder and the aggregate particles.

The presence of cracks is one of the factors that affect the performance of AC mixtures. Fan et al. [47] used SEM to analyze the microstructure of AC before and after microbially induced calcite precipitation (MICP) treatment. The SEM images revealed that the MICP treatment resulted in calcium carbonate deposition that filled the cracks and improved the bonding of the AC mixture at both interfaces, which contributed to the mechanical behaviour and durability of the treated AC mixture. Moreover, SEM images showed that the MICP treatment reduced the width of cracks by filling them with precipitated calcium carbonate [47].

SEM reveals the self-healing properties of asphalt binder. Yuefeng et al. [48] investigated the use of ethylene methacrylic acid (EMAA), an ionic copolymer, as a self-healing agent to improve the performance of AC mixtures. Various parameters and testing methods, including SEM analysis, were employed to examine the impact of EMAA addition. Results showed that EMAA significantly enhances stiffness, self-healing capacity, and cracking resistance in both virgin and modified binders and mixtures. However, caution is advised when using EMAA as a modifier for virgin binders and mixtures due to poor cracking resistance [48].

3.3. Discussion

SEM has the capacity to analyze the micro-structure of AC samples and provide a better understanding of fracture and self-healing mechanisms in the samples. The high-resolution images from SEM can reveal important details about the arrangement and distribution of aggregates, asphalt binder, and air voids, as well as any defects or damage present. By examining the microstructure, SEM can help identify the initiation and propagation of micro-cracks, new air voids, and surface roughness.

However, SEM utilizes an electron beam that cannot deeply penetrate the sample surface; therefore, it can only analyze the sample structure up to a shallower depth. SEM is a destructive imaging technique and appropriate for small-size sample screening. SEM requires contact with the sample, which makes this technique unsuitable for investigating the properties of bituminous materials at high temperatures. The application of the SEM technique for the investigation of AC microstructure is limited due to operational, sample preparation, and cost requirements. Table 2 summarizes the features of the SEM imaging technique to analyze AC microstructure.

4. X-ray Computed Tomography (CT Scan)

A computed tomography (CT) scan is a non-destructive imaging method that permits the visualization of the internal structure of the sample using X-rays. This technology can penetrate the specimen and explore the connections between subsurface structures and bulk rheology. X-ray CT scanning is a well-known technology in medicine, but it is also beneficial in civil engineering applications such as analyzing the microstructure of AC mixtures.

4.1. Principle and Method

CT scanning uses a narrow X-ray beam that circles one slice of the sample at a certain depth by slowly rotating the sample 360° and taking a series of 2D images from different angles, as depicted in Figure 4a. These 2D images are used to create a full 2D image of the sample cross-section, which shows a part of the internal structure of the sample at that depth. This process is repeated to produce many slices throughout the whole sample [49]. These scans can be stacked one on top of the other to create a 3D reconstructed image of the sample as shown in Figure 4b.

Typical CT devices used for medical applications can generate an image with a resolution of 1–2 mm for large samples (decimetre- to metre-size objects). While advanced CT systems in special institutions, such as the Canadian Light Source in Saskatchewan, Canada, can provide a resolution of 100–200 μm for smaller-size samples (centimetre- to decimetre-size objects) [50]. CT scanners use X-rays to reveal the internal structure of a material based on its components’ densities. X-ray systems produce ionizing radiation and need to be completed and operated by a radiology technologist.

4.2. Microstructure Analysis of AC Mixtures Using X-ray (CT Scan)

The X-ray CT technique can effectively capture high-resolution images of the internal microstructure of AC mixtures. These images can be segmented using image analysis techniques, allowing various components within the AC mixture to be differentiated from one another [51].

The performance of the AC mix is affected by the distribution and orientation of aggregates and their associated air voids. Masad et al. [18] used the X-ray scan technique to study the distribution of air voids in AC samples. Imaging results showed that the distribution of air voids in samples compacted by a Superpave gyratory compactor (SGC) was non-uniform and most of the voids were concentrated at the top and bottom parts of the sample.

Wang et al. [52] used X-ray CT scanning and stereology methods to evaluate the void systems in three WesTrack mixtures, fine (T06 and T07), fine plus (T08 and T09), and coarse (T10 and T11). The study quantified the spatial variation of void content, void size distribution, and parameters indicating damage level and interaction. The findings revealed that the coarse-graded mix had more severe inherent damage, consistent with field observations. The quantified parameters can be applied in fatigue and rutting modelling using continuum damage mechanics. Figure 5 shows some statistical results from that study.

Wang et al. [53] developed new methods to quantify damage parameters using stereology principles, virtual sectioning techniques, and X-ray CT scans. The study found that average spacing among damaged surfaces, comprehensive damage tensor quantity, and spacing size ratio provided effective representation of the damage state. However, the specific damaged surface area was found to be less indicative of the damaged material behaviour.

The performance of AC mixtures is influenced by their permeability. Al-Omari et al. [54] used the X-ray CT scan technique to evaluate the correlation between the permeability of AC mixtures and 3D models of internal voids. A simple permeability equation was developed to calculate the permeability of AC mixtures based on volumetric indices from X-ray CT scans, which showed a good correlation with laboratory and field permeability measurements for the same mixtures

Hu et al. [55] used X-ray CT scanning to investigate the influence of aggregates on air voids in asphalt mixtures, focusing on compaction energy. X-ray CT imaging revealed that the proportion of coarse and fine aggregates played a significant role in air void morphology and distribution. Adjusting the aggregate proportion above a specific size had a noticeable impact on air voids. The complexity of the three-dimensional model and the shape of air voids were influenced by aggregate characteristics. Higher aggregate complexity resulted in larger and more complex air voids. Compaction reduced air void complexity. Overall, aggregates were identified as a crucial factor affecting air voids in asphalt mixtures, irrespective of compaction method and energy.

Kutay et al. [56] used an X-ray CT scan, along with the fast wavelet transform (FWT) and artificial neural network (ANN), to analyze the internal structure of asphalt mixtures. The FWT effectively separates aggregates and estimates gradation, while the ANN accurately extracts aggregate properties from challenging images. The developed techniques enable comparisons of compaction methods, investigation of the effects of gradation and aggregate shape, and potential applications in micromechanical modelling.

X-ray CT scanning was utilized to observe the initiation and propagation of F–T damage within AC mixtures. Lamothe et al. [57] utilized X-ray CT scanning to investigate physical impacts on EB14 HMA asphalt, an asphalt mixture with a nominal aggregate size of 0/14 mm (EB14, Quebec standard), under different saturation conditions. X-ray imaging allowed freezing points to be determined and deformations during temperature cycles to be examined. The results showed that water-saturated samples exhibited more significant deformations compared to brine-saturated samples. Additionally, X-ray analysis facilitated the measurement of the linear coefficient of thermal expansion (LCTE), which indicated different expansion and contraction behaviours in the samples. The findings highlight the importance of X-ray imaging in studying the effects of saturation and temperature variations on asphalt properties.

Using a CT scan, Xu et al. [22] analyzed the internal structure of asphalt mixtures under freeze–thaw cycles. The tested mixtures were a conventional dense-graded asphalt mixture with 80/100 penetration-grade binder (CM), a stone mastic asphalt mixture with 80/100 penetration grade binder (SMA), and an open-graded asphalt mixture with rubberized asphalt binder of 80 grade (OGFC). Changes in void contents, number, and shape were quantified to understand deformation and micro-crack formation. The study revealed that the internal structure evolves through void expansion, coalescing, and new void formation. The asphalt mixture exhibited different internal structure changes due to aggregate size distribution. The study emphasized the significance of design air void content and saturation, as higher levels negatively impact internal structure stability and promote microcrack propagation. Figure 6 shows the change in air void content for each mixture with the increasing of F–T cycles.

Zhang C and Zhang Z [58] developed an intelligent collection system for coarse aggregate attitude using 3D printing and wireless sensing. In this study, X-ray CT scanning was used to monitor the movements of the coarse aggregate particles at different compaction stages and to validate the proposed system.

The use of X-ray CT scanning and image processing techniques in AC microstructure analysis has shown possibilities for numerical simulations that can accurately predict the mechanical performance of AC. Li et al. [59] used X-ray CT scanning and digital image processing to analyze void characteristics in an epoxy–porous asphalt mixture. The results showed a strong correlation between the mesoscale CT scanning data and macroscale measurements, indicating the effectiveness of this approach. The connected void fraction was found to be the most influential parameter in predicting the sound absorption coefficient. This technology has potential for studying drainage characteristics and road surface skid resistance in pavement research.

Chen W et al. [60] employed X-ray CT scan analysis to assess the performance of asphalt mixtures containing steel slag as a substitute for limestone aggregates. The study determined that the optimal steel slag content was 75%, resulting in enhanced high-temperature stability, water stability, shear resistance, dynamic modulus, freeze–thaw resistance, and uniaxial penetration strength. However, there was a slight reduction in low-temperature cracking resistance and a minor increase in volume expansion.

4.3. Discussion

The non-destructive X-ray CT scanning technique can capture high-resolution images of the internal microstructure of AC, which can be used to perform numerical simulations to investigate the mechanical properties of AC. The use of X-ray CT scanning can significantly reduce the time and cost required for analyzing the performance of AC mixtures, compared to destructive testing methods.

The X-ray CT scan systems can generate 2D and 3D images for the microstructure of AC mixtures, see Figure 7, in a short time without cutting samples. However, this technique uses X-rays that produce dangerous ionizing radiation and requires a radiology technologist. This technique needs access to special institutions due to the expensive instruments and safety precautions involved. CT scan image processing has some challenges; one of them is contact between coarse aggregate particles, where they appear as a large single particle in the image. Another challenge is noise and/or poor contrast in the images due to having individual components in the AC mixture with similar density values. Special filters were used in the literature to overcome these limitations.

Several studies used the CT scanning technique to investigate internal damage to AC mixtures due to traffic loading and explained the changes to the microstructure of the mixture. However, fewer studies use the CT scanning technique to investigate internal structure evolution and changes due to F–T cycling. CT scanning can provide information about the performance of AC mixtures under environmental loading in cold regions and help in selecting better materials for sustainable pavement structures. Table 3 summarizes the features of the CT scanning technique to analyze AC microstructure.

5. Summary and Recommendations

AC mixtures deteriorate due to traffic loading and environmental factors. The mechanical behaviour of AC mixtures is affected by many factors, such as asphalt binder type and content, mineral aggregate properties and gradation, and void content and distribution. Several studies have investigated the use of digital cameras, SEM, and X-ray CT scanning imaging techniques to analyze the performance and microstructural changes of AC mixtures. Image quality and intensity inhomogeneity can significantly affect the efficiency of an imaging technique.

Both the digital camera and SEM techniques are destructive techniques which need a sample to be cut to produce enough smooth and levelled cross-sections that represent the whole AC mixture. On the other hand, X-ray CT scanning is a non-destructive technique, and samples can be scanned in their original shape. Digital camera imaging can be used to extract gradation and size distribution of coarse aggregate as well as volumetric parameters of the mixture (VTM%, VMA%, and VFA%) using integrated models.

The SEM technique can be utilized to evaluate the properties of FAM and asphalt binder content in AC mixtures. The X-ray CT scanning technique is capable of generating a 3D image of the internal structure of AC, which can be used to evaluate the geometric and volumetric properties of the mixture.

Digital camera imaging cannot penetrate the sample surface, and the extracted parameters are planner projections as the images are for sample cross-sections. Correction factors and/or some assumptions are needed to estimate AC microstructure properties accurately. SEM can penetrate up to 3 μm into the sample surface. However, there are some challenges in the SEM technique that make it limited, such as the need for an ultra-high vacuum, the volatility of the asphalt binder in the vacuum chamber, and the complexity of sample preparation. X-ray CT scanning uses X-rays that can penetrate through the whole sample and generate a 3D image of the internal structure. However, there are some challenges in the X-ray CT scan technique, such as dealing with noise in the images and difficulties in aggregate segmentation. Some filters and processing methods are used to improve the quality of X-ray CT scan images and reduce noise.

Digital camera imaging is simple, accessible, safe, and inexpensive. The SEM technique needs operational and safety training and is only accessible in limited laboratories. X-ray CT scanning is not easily accessible, needs expensive instrumentation, and can only be conducted by a radiology technologist to ensure safety. Table 4 shows the advantages and disadvantages of each technique.

In conclusion, imaging techniques, such as X-ray CT scanning, digital camera, and SEM, can be used to analyze and understand the microstructure and behaviour of AC mixtures. These techniques provide high-resolution data on aggregate characteristics, detect anomalies or defects, and enhance understanding of microstructural behaviour under different loading conditions with acceptable accuracy. These imaging techniques can assist in developing more durable and long-lasting AC mixtures.

These image techniques can be used in conjunction with traditional tests for AC mixtures to provide a comprehensive understanding of the behaviour of AC mixtures. Imaging techniques can assist in initial screenings, quality control, and research, but certain performance-related parameters may still require validation via traditional testing methods.

Author Contributions

Writing–original draft, M.A.; Writing–review & editing, H.S.; Supervision, H.S. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data was created. The data in the article is based on the literature. Details of the data are available from the corresponding author.

Acknowledgments

The authors would like to acknowledge Al-al-Bayt University—Jordan and the Natural Sciences and Engineering Research Council of Canada (NSERC) for funding this research.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

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Figures and Tables

Figure 1. An example of contact between two aggregate particles.

Figure 2. The shape and size of asphalt concrete samples for SEM test.

Figure 3. Coarse aggregates and FAM in an AC mixture.

Figure 4. Schematic of an X-ray CT scanning device: (a) device components; (b) reconstructed 3D image of an asphalt sample.

Figure 5. Some statistical results from Wang et al. [52]. (a) The average mean solid path (mm) for all mixes. (b) The average percent void content of mixes. (c) The average void size (area, mm2). (d) The average specific damaged surface area of mixes.

Figure 6. Air void content changes with increasing F–T cycles for CM, SMA, and OGFC mixtures [22].

Figure 7. 2D and 3D images of the microstructure of AC mixture generated via X-ray CT scan. (a) 3D reconstruction of AC sample (b). 2D image of AC sample.

Table 1

Digital Camera Imaging.

Imaging Technique	Digital Camera
Sample Size	A destructive technique.AC cores are cut to produce horizontal circular sections of D of at least 50 mm or vertical rectangular sections of W of at least 50 mm, and height depends on the compaction. No. of the sections has to be representative of the sample.
Sample Penetration	Cannot penetrate the sample surface
Type of Produced images	2D (Direct) and 3D (by image processing)
Source	Light
Imaging Tools	A Digital camera. A Digital system (computer). Image processing software/code.
Image Resolution	Depending on the digital camera type can reach up to 50.6 megapixels (4 microns)
Produced Data	Coarse aggregate gradation, size, and distribution.Volumetric properties of air voids and asphalt. Cracks and changes due to F–T cycles.

Table 2

Scanning Electron Microscope (SEM).

Imaging Technique	Scanning Electron Microscope
Sample Size	A destructive techniqueAC cores are cut into small and thin slices with a depth up to 10 mm and a length up to 50 mm Figure 2
Sample Penetration	Can penetrate up to 3 micrometres into the sample surface based on the electron beam energy
Type of Produced images	2D (Direct) and 3D (3D images are formed by the image processing software of the attached computer)
Source	Electron beam
Imaging Tools	SEM or ESEM; attached computerImage processing software
Image Resolution	up to 1 nm (0.001 microns)
Produced Data	Coarse and fine aggregate gradation, size and distribution Air void volumes Cracks and changes due to F–T cycles

Table 3

Computed tomography (CT) scan.

Imaging Technique	X-ray CT Scan
Sample Size	A non-destructive techniqueNo need to cut the AC cores
Sample Penetration	Can penetrate the sample and reveal all details of the internal structure
Type of Images Produced	2D and 3D (generated by built-in image processor)
Source	X-ray beam (photon current)
Imaging Tools	X-ray CT scanning machineAttached computer Image processing software
Image Resolution	Up to 100–200 microns
Produced Data	3D simulation of AC cores can provide all the properties of AC mixtures Coarse and fine aggregate gradation, size and distribution The volume of air voidsSurface textureCracks and changes due to F–T cycles

Table 4

The advantages and disadvantages of each imaging technique.

Imaging Technique	Advantages	Disadvantages
Digital camera imaging	High-resolution imaging Cost-effectiveness Ease of use Rapid data acquisition	No depth perception Subjectivity in image interpretation Limited quantitative analysis Sensitivity to lighting conditions
Scanning electron microscope (SEM)	High-resolution imaging Three-dimensional visualization of the surface Elemental analysis Microstructural characterization	Costly and complexity Complex sample preparation Vacuum requirements Limited sample size
X-ray CT scan	High-resolution imaging Three-dimensional visualization Quantitative analysis Material differentiation	Limitations on access to equipment and facility Radiation exposure Image artifacts such as noise, streaking, and beam hardening

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