Micrometric and Nanometric Characterization of Rock Aggregates Using AIMS, AFM, and SEM to Further Discussions on Geotechnical Roughness Classification Systems

Abstract

The characterization of surface roughness in rock aggregates is essential for geotechnical engineering, directly influencing pavement performance and durability. This study compared three roughness analysis techniques: the Aggregate Imaging Measurement System (AIMS), Atomic Force Microscopy (AFM), and Scanning Electron Microscopy (SEM). Samples of volcanic rock (SUL) and plutonic rock (MIN) were analysed using all three techniques, revealing discrepancies in the results. AIMS presented limitations in representing materials with micrometric roughness and substantially coarse grain sizes, such as the MIN aggregate, where dominant minerals influenced the texture due to wavelet processing. AFM proved to be an efficient roughness characterization technique, quantifying parameters such as average roughness (Ra), root mean square roughness (Rq), surface skewness (Rsk), and surface kurtosis (Rku), but was limited to small scanning areas. The results reveal potential inconsistencies in traditional classification systems, particularly their inability to detect variations in rocks with different textures and granulometries. Data obtained from AFM and SEM demonstrated greater sensitivity, revealing features not identified by AIMS, especially in fine-grained materials like the SUL rock. Therefore, integrating micro- and nanoscale analyses is proposed as a complementary approach to overcome the limitations of conventional methods.

Keywords:
Surface roughness; Pavement engineering; Aggregate characterization; Atomic Force Microscopy; Aggregate Imaging Measurement System; Morphological analysis methods

1. Introduction

Aggregates play a central role in civil engineering, being fundamental components in asphalt mixtures and cementitious concretes, where they represent the largest proportion of the material's total volume¹. The morphological properties of aggregates, including shape, texture, and size, directly influence the structural and functional performance of these materials². The geometric complexity resulting from these characteristics significantly impacts structural strength, stability, and the ability to organise asphalt mixtures, aspects crucial for durability and efficiency in applications such as pavement and concrete structures³,⁴. Among morphological parameters, the textural relationships of crystals and, particularly, the roughness of aggregates, play a determining role in wear resistance, adhesion to binders, and moisture susceptibility⁵. Aggregates with angular and rough surfaces are associated with higher binder adhesion and mechanical stability, while smoother surfaces favors void distribution, directly influencing structural and functional performance⁶. In this context, the development and application of robust and precise morphological characterization methods are essential for predicting the mechanical behaviour of asphalt mixtures and concretes⁷,⁸.

Traditionally, aggregate morphological characterization involves analytical laboratory techniques that, although widely used, are often time-consuming, subjective, and operator-dependent⁹,¹⁰. The introduction of digital image analysis tools, such as the Aggregate Imaging Measurement System (AIMS), revolutionized this field by offering efficiency and objectivity in classification. However, AIMS has notable limitations, such as restriction to the bulk scale and difficulty in accurately distinguishing texture, angularity, granulometry and shape properties¹¹-¹⁴. As AIMS relies on image acquisition through a fixed camera with automatic focus¹⁵, the images generated are not always representative of the aggregate. Consequently, given the inadequacy of the image dataset, the wavelet-based processing used to obtain roughness is compromised.

Given these limitations, advanced microscopy techniques, such as Atomic Force Microscopy (AFM), emerge as promising alternatives for evaluating rock aggregate morphology¹⁶-¹⁸. AFM enables topographic characterization at micro- and nanoscale, providing quantitative measurements of surface roughness with high resolution¹⁹,²⁰.This technique can identify morphological variations in extremely small regions, with resolutions below 200 nm, and is also a non-destructive method capable of generating precise three-dimensional (3D) profiles²¹-²⁷. AFM has emerged as a prominent characterization method at micro- and nanoscale across various scientific domains, including biology, physics, chemistry, and materials science²⁸-³¹. Its efficiency in evaluating the morphology of metal alloy materials is particularly notable³²-³⁷. In pavement engineering, AFM has been used to evaluate and characterize asphalt surface microstructures and micromechanical properties³⁸-⁴¹ study modified binders⁴²-⁴⁴, explore the effects of ageing and recycling on asphalt adhesive properties⁴⁵-⁴⁹, evaluate water damage⁵⁰ and binder-aggregate adhesion⁵¹. The potential of AFM for characterizing rock materials has also been explored, including studies on porosity and hydrocarbon reservoir rocks⁵²-⁵⁴ and the identification and morphological characterization of microstructural elements in rocks, such as fractures⁵⁵-⁵⁷. The potential of AFM for characterizing rock materials has also been explored, including studies on porosity and hydrocarbon reservoir rocks⁵²-⁵⁴ and the identification and morphological characterization of microstructural elements in rocks, such as fractures⁵⁵-⁵⁷. In geotechnics, Wang et al.⁵⁸ used SEM to obtain detailed information on the adhesion between aggregates and asphalt binder, advancing the understanding of the interaction mechanisms of these materials.

Scanning Electron Microscopy (SEM) has been widely employed in studies focused on qualitative morphological characterization at the microscale, owing to its capability to generate detailed images from secondary electrons. SEM enables the acquisition of high-resolution images of the morphology and microstructures present on sample surfaces⁵⁹. Kuang et al.⁶⁰ employed SEM to analyze the microstructure of limestone and granite aggregates, focusing on the asphalt-aggregate interface, identifying deeper textures in limestone compared to granite. These results highlighted the greater roughness of limestone and its consequent influence on the aggregate's mechanical behaviour⁶⁰. In geotechnics, Wang et al.⁵⁸ utilized SEM to obtain detailed image information regarding adhesion between aggregates and asphalt binder, contributing to a deeper understanding of these interaction mechanisms.

Although AFM and SEM techniques provide valuable information on roughness and microstructure, studies applying these advanced techniques to the characterization of mineral aggregates remain scarce. Due to the limited scanning area of AFM (areas smaller than 10 × 10 µm²), the combination of AFM and SEM emerges as a relevant approach for multiscale characterization of aggregate morphology⁶¹. This research aims to apply AFM and SEM techniques to characterize the surface roughness of mineral aggregates and compare their results with those obtained using the AIMS method. Based on the results, inconsistencies in the classification systems currently used in geotechnical engineering — which do not consider the micro- and nanoscale evaluation of aggregate morphology — will be identified and discussed. This study seeks to contribute to the advancement of morphological characterization methodologies and, consequently, to improving pavement performance and durability.

2. Experimental

2.1. Materials

Two types of aggregates widely used in civil engineering of volcanic (surface crystallization) and plutonic (deep crystallization) origins, were selected for this study (Figure 1). The samples were collected from quarries located in the Rio Grande do Sul (RS) state, southern Brazil, exhibiting distinct morphological characteristics: one situated at the transition between the Serra Geral and Rio Jacuí Depression (SUL Quarry) and the other in the Lower Southern Rio Grande Plateau (MIN Quarry). Table 1 presents detailed information about the quarries, including their location, lithology, and petrographic characterisation of each aggregate.

Figure 1
Photographic materials use for this study.

Table 1
Petrological characterisation of the aggregates used in the research.

ID	Location	Lithology	Petrological characterization
			Texture	Structure	Mineralogy
SUL	Dilermando	Basalt	Aphanitic equigranular	No main foliation	55% plagioclase; 35% pyroxene; 10% hematite and/or magnetite
MIN	Capão do Leão	Monzogranite	Phaneritic inequigranular	Magmatic flow foliation	22% plagioclase; 45% pyroxene; 25% quartz; 3% biotite

2.2. Methods

2.2.1 AIMS roughness assessment

The digital image processing of aggregates was performed using the Aggregate Imaging Measurement System (AIMS), as specified in the Brazilian technical standard DNIT 432/2020-ME¹⁶. The tests were conducted at the Geotechnics/Pavement Laboratory of the Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia at the Universidade Federal do Rio de Janeiro (COPPE/UFRJ). The method followed three steps:

• Particle Preparation and Positioning: Aggregates were arranged in trays according to particle size. An initial verification was conducted to detect potential analytical acquisition errors.

• Image Acquisition: Using a high-resolution camera, grayscale images were captured for texture analysis.

• Image Processing: Morphological properties were quantified using specialized algorithms employing edge detection and segmentation techniques.

The surface roughness index via AIMS was calculated based on wavelet analysis, which evaluates surface irregularities (Equation 1).

$Text.Sup{.}_n=\frac{1}{3N}{{\displaystyle \sum }}_{t=1}^3{{\displaystyle \sum }}_{f=1}^N\left(D_{t,j}\left(x,y\right)\right)²$ (1)

Where:

• D = decomposition function

• n = image decomposition level

• N = total number of coefficients in an image

• i = texture direction

• j = wavelet index

• x,y = location of coefficients in the domain

The resulting parameter, ranging from 0 to 1000, provides a quantitative measure of roughness, with higher values indicating rougher surfaces. The classification systems proposed by Al Rousan⁶²,⁶³ and Ibiapina et al.⁶⁴ were adopted to interpret the results, as described in Table 2.

Table 2
Threshold values established for surface texture classification according to the methodologies of Al Rousan et al.⁶³ and Ibiapina et al.⁶⁴.

Classification System	Limit Values
Al Rousan et al.63	< 165	165 – 275	275 – 350	350 – 450	> 460
	Polished	Smooth	Low roughness	Moderate roughness	High roughness
Ibiapina et al.64	<260	260-440	440-600	600-825	>825
	Polished	Smooth	Low roughness	Moderate roughness	High roughness

2.2.2. AFM roughness assessment

The micro- and nanoscale analysis using Atomic Force Microscopy (AFM) was performed with the Park Systems NX10 equipment at the Department of Physics at Universidade Federal de Santa Maria (UFSM). This technique was applied to complement the data obtained through AIMS, focusing on morphology, phase contrast, and amplitude. The methodology followed three experimental configurations:

• Operating Mode: Non-contact mode, suitable for preserving both the integrity of the rock material surface and the probe tip.

• Probes: PPPNCHR, with a resonance frequency of 330 kHz and a force constant of 42 N/m.

• Scanning Configuration: Scanning area of 10 × 10 µm, elevation height of 200 nm, resolution of 256 × 256 pixels, and a scanning rate of 0.35 Hz.

The acquired data were processed using the Gwyddion software, enabling the extraction of quantitative parameters of surface roughness at both the micro- and nanoscale levels. The integration of data obtained from AIMS and AFM aimed to evaluate the consistency between the proposed classification systems for rock aggregates, identifying potential incongruences and limitations in the existing systems.

2.2.3. Scanning Electron Microscopy (SEM)

The microstructural analysis performed by Scanning Electron Microscopy (SEM) used a JSM 6360 model, equipped with an energy dispersive X-ray spectroscopy (EDS) system, QUEST model, by ThermoSystem. The tests were conducted in the SEM Laboratory at UFSM.

The rock samples were subjected to a cleaning process to remove impurities before the analysis, following the recommendations of Nobre et al.⁶⁵ and Mallmann et al.⁵⁹. The aggregates were then fixed on the sample holder using double-sided carbon adhesive tape. To ensure the necessary electrical conductivity for the analysis, the surfaces of the materials were coated with a nanometric gold layer via sputtering in a vacuum metal coater.

The images were obtained in topographic mode (Topo), which generates morphological contrasts based on the variation in the yield of secondary electrons emitted by the sample surfaces. The analyses were carried out at magnifications of 100x and 2000x, using an acceleration voltage of 20 kV and a beam with electric current of 20 nA. This procedure allowed the observation of the microstructural characteristics and the evaluation of the surface roughness of aggregates with high resolution.

3. Results and Discussion

3.1. AIMS

The roughness values obtained using the AIMS system are presented in Table 3, considering the classifications proposed by Al-Rousan⁶² and Ibiapina et al.⁶⁴. It can be observed that the volcanic rock (SUL) exhibited a higher roughness value (894.093) compared to the plutonic rock (MIN), which obtained a value of 66.32. Regardless of the classification used, both systems categorize the SUL material as "High Roughness" and the MIN as "Polished."

Table 3
Values obtained for surface roughness by AIMS.

ID	Value	Al Rousan et al.63	Ibiapina et al.64.
SUL	894,093	High Roughness	High Roughness
MIN	66,32	Polished	Polished

Figure 2 shows the images captured by the AIMS system for surface texture, while Figure 3 represents a three-dimensional (3D) profile of the pixel distribution on the aggregate surface. The surface texture was analyzed based on the variation in pixel intensity captured by the camera, revealing that the SUL aggregate material has a higher density of peaks and valleys on the surface compared to the MIN rock. This difference can be observed through the color scale used, where rougher areas exhibit a heterogeneous color distribution. This behavior indicates that the SUL aggregate is likely to provide greater mechanical interaction in asphalt mixtures due to its high roughness, whereas the MIN material, with a smoother surface, may exhibit lower adhesion capacity.

Figure 2
Photographic record of pixels acquired by the AIMS system camera.

Figure 3
Presentation of data obtained by AIMS.

The images obtained via AIMS do not always accurately reflect the actual characteristics of the analyzed aggregate. This occurs due to intrinsic limitations related to the camera focus during image capture, the photographed analysis area, the structure and granulation of the rock material, and subsequent processing, which involves the application of wavelet transformation.

The surface texture of the SUL aggregate is rougher than that of the MIN aggregate due to the geological origin differences between the rocks. The SUL rock, of volcanic origin and fine granulation, exhibits a more complex microstructural fabric with irregular morphology, whereas the MIN rock, of plutonic origin and coarser granulation, shows less morphological variation. The processing of data acquired via AIMS influences the representativeness of the results. In the case of the MIN rock, with its coarse texture, the analysis may be biased by isolated minerals since the camera focus can center on single phases, in addition to the wavelet transformation. This processing highlights the influence of dominant minerals, such as quartz and feldspar, resulting in texture values that are less representative of the aggregate. The fine-grained SUL rock allows the analysis to capture a more integrated combination of different minerals and microtextures present on the aggregate surface. This makes the obtained texture values more representative of the material's overall behavior. The 3D texture analysis reinforces this observation, with the SUL aggregate showing more numerous, intense, and irregular elevation peaks, while the MIN aggregate exhibits a predominantly homogeneous and flat surface.

3.2. AFM

AFM was used to obtain 3D roughness maps of the SUL and MIN samples in three different areas of each sample, providing a detailed and quantitative analysis of the topography. For this characterization, the following parameters were measured:

• Ra (Average Roughness): Represents the average difference between the height of a point and the mean surface level. It is obtained from the arithmetic mean of the height deviations of the surface relative to the sample plane⁶⁶-⁶⁸

• Rq (Root Mean Square Roughness): Calculates the standard deviation of the topography, representing the root mean square of the height deviations from the sample plane. This parameter is frequently used to evaluate surface texture.

• Rsk (Surface Skewness): Indicates the symmetry of the height distribution on the surface. A value close to zero suggests a smooth surface, positive values indicate a predominance of peaks, while negative values reflect a predominance of depressions.

• Rku (Surface Kurtosis): Reflects the concentration of heights around the mean value. Positive values suggest a more concentrated and sharp distribution, while negative values indicate greater dispersion of heights.

The results obtained for the analyzed samples are presented in Table 4.

Table 4
Statistical parameters generated as a result of AFM analysis.

ID	Area	Ra	Rq	Rsk	Rku
SUL	1	35,15	45,29	0,2606	-0,425
	2	0,7809	0,9647	-0,08454	-0,7365
	3	13,55	20,36	1,203	2,014
MIN	1	137,3	170,1	-0,054	-0,6502
	2	109,8	131,5	0,5636	-0,5393
	3	117,3	147,4	-4,65	-0,5388

Based on the results obtained, it is possible to describe the following points:

Ra e Rq (Average and Root Mean Square Roughness):

• The MIN sample showed higher Ra and Rq values in all analyzed areas, indicating greater irregularity and more pronounced surface height compared to the SUL sample.

• The SUL sample exhibited greater variability between the areas, with low values in some regions (Area 2), reflecting the microstructures heterogeneity.

Rsk (Skewness):

• In the SUL sample, Rsk values ranged from negative (-0.08454 in Area 2) to positive (1.203 in Area 3), indicating that the microtextures include both depressions and elevated peaks.

• The MIN sample had predominantly negative values, suggesting surfaces with more depressions than peaks or a more uniform height distribution.

Rku (Kurtosis):

• The kurtosis in the SUL sample showed values close to zero in some areas (indicating a normal height distribution) and positive in others, such as Area 3 (Rku = 2.014), suggesting a higher concentration of peaks on the surface.

• For the MIN sample, all Rku values were negative, indicating greater dispersion of heights and the absence of significant concentration around the mean value.

The fine granulation and heterogeneous composition of the SUL rock provided more representative AFM measurements, capturing detailed information about the different minerals that form its microstructures, even though they are quite heterogeneous between areas. This characteristic explains the greater variability in Rsk and Rku parameters across different areas. The coarse granulation and predominance of scanned areas containing only one mineral phase resulted in higher Ra and Rq values for the MIN rock. However, these results might suggest an apparent roughness influenced by the scanning of a single phase of the rock, reflecting a texture that is less representative of the aggregate.

Figures 4 and 5 present the three-dimensional surface roughness maps obtained by AFM for the SUL and MIN aggregates performed in three distinct areas of each sample. These diagrams allow a detailed visualization of the topographic differences influencing the roughness (Ra and Rq), skewness (Rsk), and kurtosis (Rku) values.

Figure 4
Surface roughness of SUL aggregate.

Figure 5
Surface roughness of MIN aggregate.

The SUL sample graphs indicate a more heterogeneous topography across the analyzed areas. In Area 1, a relatively homogeneous surface is observed, while in Area 2, more pronounced peaks appear frequently, aligning with the increased Ra and Rq values in this region. In Area 3, a profile with sharper peaks is noted, reflected in the higher positive skewness (Rsk = 1.203) and kurtosis exceeding 2.014, indicating the concentration of elevations in small domains. The graphs of MIN aggregate suggest a more uniform texture across the three analyzed areas, although Area 3 shows significant variation with a prominent central peak, as indicated by the negative Rsk and an Rku value close to -0.5388. This topography confirms previously observed characteristics, where AFM detects isolated variations that can be attributed to the dominance of individual minerals in coarse-grained aggregates due to the scanning area limitations imposed by this characterization technique.

These 3D graphs demonstrate how the microstructures of aggregate surfaces, influenced by mineralogical composition and granulation, affect roughness parameters. In the case of SUL, the finer granulation allows the capture of multiple minerals, while in MIN, the more uniform texture is shaped by dominant minerals. The illustrated topography supports the discussion of roughness values and the limitations of the methods in representing the overall complexity of aggregate surfaces.

3.3. MEV

The secondary electron images generated by SEM of the aggregates from the SUL and MIN samples are shown in Figure 6.

Figura 6
Secondary electron images of the SUL (A and C) and MIN (B and D) rock aggregates at 100 and 2000x magnification.

At a 100x magnification, it is possible to observe differences in the morphology and distribution of mineral particles between the aggregates. The SUL aggregate (Figure 6A) shows a rougher surface, which is associated with the substantially fine (aphanitic) granulation of its matrix. In this type of microstructure, the constituent minerals are not visible to the naked eye but become perceptible under SEM, revealing a surface with relatively uniformly distributed roughness. The MIN aggregate (Figure 6B) exhibits coarse (phaneritic) granulation, characterized by minerals that are distinguishable to the naked eye and vary in size. This mineralogical feature results in areas with variable roughness: smooth plateaus associated with the surface of single minerals, such as quartz and feldspar (low roughness), and uneven regions with high roughness, usually related to cleavage planes, fractures, and crystal edges. Additionally, external influences, such as dust accumulation and other contaminants, were observed on the surfaces of both aggregates.

In 2000x magnification, the MIN aggregate (Figure 6D) maintains its heterogeneous surface roughness, with low-roughness areas interspersed with regions of pronounced unevenness, more clearly highlighting the cleavage planes and fractures of the minerals. This observation reinforces the relationship between roughness and mineral grain size, where minerals more susceptible to cleavage and fracture exhibit significant morphological irregularities along crystal edges. The SUL aggregate (Figure 6C), at a 2000x magnification, revealed cleavage planes and mineral fractures, patterns that were not observable at lower magnifications.

3.4. Critical analysis and discussion between AIMS, AFM and SEM results

Based on the results obtained from the AIMS, AFM, and SEM tests, it is evident that further investigation is needed regarding the microstructure and fine-grained materials in the classification systems currently used in geotechnical engineering, highlighting the methodological limitations of AIMS and the relevance of AFM and SEM as complementary approaches for material characterization at different scales.

The AIMS results reveal that, according to the classification systems of Al-Rousan⁶² and Ibiapina et al.⁶⁴, the volcanic aggregate (SUL) exhibits high roughness, while the plutonic aggregate (MIN) is classified as polished. Although these classifications are useful for general comparisons, they do not adequately represent the microstructures of the aggregates. AIMS, relying on two-dimensional images and wavelet processing, is limited by the representativeness of the captured areas, as with other techniques. However, this limitation is particularly evident in coarse-textured rocks, such as the MIN aggregate, where the captured image predominantly reflects the characteristics of coarse minerals, overlooking variations that may significantly impact the material's mechanical behavior. Moreover, the output provided by the AIMS test is made available only after image processing, which prevents the wavelet-based texture analysis technique from being reliably reapplied to the other analyses performed in this study. This limitation, to some extent, hinders the possibility of conducting comparisons under the same experimental conditions. Additionally, AIMS has inherent limitations in image acquisition, as the fixed camera with automatic focus does not ensure sufficient image quality for representativity — regardless of the subsequent processing method. Another factor restricting the use of AIMS is the limited availability of equipment compared to SEM and AFM, making AIMS a less accessible technique.

The AFM results provide a more detailed view of surface texture, with quantitative parameters such as Ra, Rq, Rsk, and Rku, as well as high-resolution morphological models. These parameters reveal nuances of roughness not captured by AIMS. For the SUL aggregate, AFM indicated a more heterogeneous surface (at the micro and nanometric scale) with more pronounced peaks, characteristics that corroborate the high roughness classification obtained by AIMS, but with greater precision. For the MIN aggregate, AFM demonstrated that, despite appearing to have a more uniform surface, variations exist that suggest the presence of microstructures not captured by AIMS due to the method's limited resolution.

Complementing these analyses, the SEM results provided qualitative characterization of the surfaces at the micro-scale, offering insights into the aggregate morphology. For the SUL aggregate, images obtained at 100x and 2000x magnifications revealed a homogeneous surface, with roughness linked to the fine (aphanitic) texture of the parent rock. This roughness is evenly distributed in bulk analyses but begins to highlight mineral-dependent features, such as cleavages and fractures, when observed at the micro-scale. In the MIN aggregate, SEM images revealed a surface with smooth plateaus associated with minerals and regions of pronounced unevenness related to crystal edges. This morphology is emphasized at the 2000x magnification, where mineral cleavages and fractures become more evident as factors contributing to roughness, which are not detected by AIMS and are corroborated by AFM data.

These comparisons emphasize the importance of integrating methods such as SEM and AFM to complement AIMS analyses. Although AIMS is considered an effective method in geotechnics for initial macroscopic evaluation, it lacks the resolution needed to consider microstructures and roughness variations related to micro and nanometric granulation, which may impact properties such as the adhesion of binders to rock aggregates. AFM provides micro and nanoscale analyses with effective roughness values, but it evaluates more restricted surface areas compared to the other techniques, while SEM offers detailed qualitative characterization at the micro-scale. Together, these methods are complementary and essential for a comprehensive understanding of the surface roughness of aggregates. The combined use of these techniques provides a multi-scale (bulk, micro, and nano) view of the aggregate surfaces, allowing the identification of critical properties that directly influence the adhesion to asphalt binders and, consequently, the mechanical performance of asphalt mixtures. This integrated approach highlights the limitations of classification systems based solely on AIMS and represents a contribution to improving aggregate selection criteria and, consequently, the performance and durability of paved structures. Therefore, this study emphasizes the need to revise and enhance the classification systems currently in use, integrating advanced characterization techniques such as AFM and SEM⁶⁹,⁷⁰. The combination of these methods provides a more robust and representative characterization of aggregate surfaces, with direct implications for pavement engineering and other areas of geotechnical engineering.

4. Conclusions

The results of this study highlighted limitations in the aggregate roughness classification systems currently used in geotechnical engineering, with a focus on the AIMS method. Although widely adopted, AIMS is an optical technique limited to the bulk scale and is insufficient to capture the complexity of microstructures, particularly in coarse-textured rocks such as the MIN aggregate. The analysis was influenced by coarse minerals, compromising the representativeness of the obtained data and omitting fine features that can only be analyzed at the micro and nanometric scales. The combined use of SEM and AFM offers a complementary approach for studying the roughness of rock aggregates. SEM enabled detailed qualitative characterization at the micro-scale, revealing morphological variations related to texture and crystal edges, with smooth plateaus and unevenness in fractures and cleavages. AFM provided measurements at micro and nano scales, generating three-dimensional maps and quantitative parameters (Ra, Rq, Rsk, Rku) not generated by traditional methods. Although SEM lacks resolution for nanometric-scale analysis, it generates three-dimensional results. Conversely, while AFM can detect nanometric roughness, standardizing it for micrometric-scale analysis would lead to the loss of relevant data for roughness assessment.

This study contributes to the discussion on rock aggregate characterization methodologies for pavement applications by proposing the integration of micro and nano-scale analyses as new dimensions in aggregate evaluation. It is important to emphasize that the image acquisition process underlying roughness pattern analysis must be representative in both scale and content to allow for accurate processing, regardless of the technique employed (e.g., wavelet, Ra, Rq, Rsk, Rku, or others not considered in this study). This study highlights the need to update the currently adopted standards for classifying aggregate roughness, which are primarily based on data obtained through imaging techniques. These factors are relevant for adhesion to asphalt binders, and previous bulk-based approaches were insufficient to analyze them. The use of techniques such as SEM and AFM can provide valuable insights for developing more refined classification systems that align with the performance and durability requirements of advanced pavement structures.

Data Availability

The entire dataset supporting the results of this study was published in the article itself.

5. Acknowledgments

The authors would like to thank CAPES for funding the research and providing research scholarships, the COPPE Lab at Universidade Federal do Rio de Janeiro, and the Laboratory of Magnetism and Magnetic Materials (LMMM) and the Scanning Electron Microscopy (SEM) Laboratory at UFSM for enabling the execution of the experiments.

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Edited by

• Associate Editor:
  Eliana Muccillo.

• Editor-in-Chief:
  Luiz Antonio Pessan.

Publication Dates

• Publication in this collection
  08 Aug 2025
• Date of issue
  2025

History

• Received
  10 Jan 2025
• Reviewed
  13 Apr 2025
• Accepted
  01 June 2025