Evaluation of aggregates surface micro-texture using spectral analysis

Kouchaki, Sareh; Roshani, Hossein; Prozzi, Jorge A; Hernandez, Joaquin B.

doi:10.1016/j.conbuildmat.2017.08.174

Cited by 30 publications

(7 citation statements)

References 26 publications

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“…Along with the light, the room temperature and humidity were also kept constant during the experiments. In a parallel research study conducted by Kouchaki et al it was reported that the developed LLS prototype showed promising results in terms of scanning the surface micro-texture of aggregates ( 31 ).…”

Section: Methodsmentioning

confidence: 99%

Evaluation of a Line Laser Scanner to Improve the Measurement of Average Least Dimension in Chip Seal Design Methods

Kouchaki

Roshani²,

Prozzi

et al. 2018

Transportation Research Record: Journal of the Transportation R

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Chip seal, as the most widespread pavement preventive treatment, is regularly applied on existing pavements that are still in good structural condition to increase the pavement serviceability. The most effective key parameter on chip seal performance is binder application rate, which directly governs chip seal distresses such as bleeding and raveling. How this rate is calculated mainly depends on the value of least dimension (LD) of aggregate particles. However, the available measuring methods of LD value are slow, laborious, and subjective. This study presents the development of a new high-speed line laser scanner (LLS) prototype to measure the LD value of particles more quickly and accurately. The LD values of aggregate particles were also measured using a digital caliper and considered as control data. The repeatability and reliability of the developed LLS prototype were evaluated, as well as the speed of the prototype in calculating the LD values of 100 aggregate particles. The findings indicate that the measurements of the developed prototype are highly correlated with those of the caliper. In addition, it was found that the developed prototype is efficient and capable of calculating the LD values of several particles simultaneously.

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Section: Methodsmentioning

confidence: 99%

Evaluation of a Line Laser Scanner to Improve the Measurement of Average Least Dimension in Chip Seal Design Methods

Kouchaki

Roshani²,

Prozzi

et al. 2018

Transportation Research Record: Journal of the Transportation R

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“…The first set, Set A (Figure 6), was obtained from the chart developed by Rittenhouse for the visual identification of aggregate sphericity ( 21 ). The second set, Set B (Figure 7), was obtained by scanning aggregates in the laboratory using the line-laser scanning system (Figure 8) developed at the University of Texas at Austin ( 22 ), whereby a line-laser is mounted on a specially designed frame, and, by moving along the y -direction, it can provide a continuous scan of the target surface. For the aggregates in these two sets, two common form parameters, elongation ratio and projection sphericity ( 13 ), were calculated for each aggregate.…”

Section: Aggregate Databasesmentioning

confidence: 99%

“…Efforts have been made to characterize form, angularity, and surface texture separately (4,6,8,(12)(13)(14)(15)(16)(17)(18)(19)(20). For example, in the current standards (ASTM D5821, ASTM D4791, and AASHTO T304), form is described in terms of percentages of flat and elongated particles, angularity is measured based on the number of fractured faces, and the angularity of the fine aggregate is inferred from the volume of air voids in a loosely compacted aggregate sample.…”

mentioning

confidence: 99%

Correlation of Aggregate Form Properties with Fourier Frequencies

Prozzi

2019

Transportation Research Record: Journal of the Transportation R

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Aggregate morphology consists of three independent components: form, angularity, and surface texture. As these three components act in different ways in affecting the performance of pavement layer, it is vital to characterize them objectively and accurately. In this study, the coordinates of boundary pixels of aggregate from 2-D images were treated as an equally spaced complex series. The description of the aggregate shape in the frequency domain was obtained by analyzing this complex series with the discrete Fourier transform (DFT). Based on this, the reconstruction of aggregate shape at different frequencies was performed, which validated that the three elements of aggregate shape correspond to different frequencies. In addition, by fitting the relationship between DFT coefficients and form parameters (elongation ratio, sphericity, mean radius, variance of radius), the frequencies contributing to the form properties of aggregate were identified. Moreover, prediction models for the elongation ratio, sphericity, mean, and variance of radius based on the DFT coefficients at the contributing frequencies were developed separately.

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“…Examples of models used for describing textures are Markov random field models [ 37 , 38 ] and fractal models [ 39 , 40 ]. Finally, filter-based methods, also known as transform-based methods, describe textures through frequency analysis based on Fourier transforms [ 41 , 42 ], Gabor filters [ 43 , 44 , 45 ] and wavelets [ 46 , 47 , 48 ].…”

Section: Introductionmentioning

confidence: 99%

A Class-Independent Texture-Separation Method Based on a Pixel-Wise Binary Classification

Soares

Côco

Ciarelli

et al. 2020

Sensors

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Texture segmentation is a challenging problem in computer vision due to the subjective nature of textures, the variability in which they occur in images, their dependence on scale and illumination variation, and the lack of a precise definition in the literature. This paper proposes a method to segment textures through a binary pixel-wise classification, thereby without the need for a predefined number of textures classes. Using a convolutional neural network, with an encoder–decoder architecture, each pixel is classified as being inside an internal texture region or in a border between two different textures. The network is trained using the Prague Texture Segmentation Datagenerator and Benchmark and tested using the same dataset, besides the Brodatz textures dataset, and the Describable Texture Dataset. The method is also evaluated on the separation of regions in images from different applications, namely remote sensing images and H&E-stained tissue images. It is shown that the method has a good performance on different test sets, can precisely identify borders between texture regions and does not suffer from over-segmentation.

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Evaluation of aggregates surface micro-texture using spectral analysis

Cited by 30 publications

References 26 publications

Evaluation of a Line Laser Scanner to Improve the Measurement of Average Least Dimension in Chip Seal Design Methods

Evaluation of a Line Laser Scanner to Improve the Measurement of Average Least Dimension in Chip Seal Design Methods

Correlation of Aggregate Form Properties with Fourier Frequencies

A Class-Independent Texture-Separation Method Based on a Pixel-Wise Binary Classification

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