Innovation of aggregate angularity characterization using gradient approach based upon the traditional and modified Sobel operation

Abstract This study aims to employ the modified Sobel-Feldman operation to quantify aggregate angularity using the gradient approach, which has been used in previous studies. In detail, the modified Sobel-Feldman operation lies in expanding the conventional kernel size of the operation from 3 × 3 to 5 × 5, and up to 13 × 13 using the average gradient vectors of two neighboring pixels (2-pixel) instead of one single pixel (1-pixel) to calculate the angularity index. To achieve this goal, image processing was conducted to obtain the outlines of aggregate images. The gradient vectors of the surface pixels were obtained using the conventional and modified Sobel-Feldman operation. Then the angularity index (AI) of aggregates were calculated based on the change in gradient vectors of neighboring pixels with a rotational angle of 10°. The angularity index calculated using the conventional and modified Sobel-Feldman operations were compared. The sensitivity of the approach to image resolution was also discussed in this study. The profile images of both real aggregates and computer generated aggregate models were used for this investigation. The results show that the conventional Sobel-Feldman operation results have a higher Angularity Index than that of larger sized kernels. The AI values calculated from the 2-pixel method were more stable compared to those using the 1-pixel method. The modified Sobel-Feldman operation has lower sensitivity to image resolution than the conventional operation. Overall, the gradient approach using the 7 × 7 sized Sobel-Feldman operation with the 2-pixel method is the best way to calculate the angularity index. The findings of this study can potentially be adopted by commercial angularity index measurement apparatuses such as the aggregate imaging system (AIMS).

[1]  F. Bergholm,et al.  Evaluation of image analysis methods used for quantification of particle angularity , 2013 .

[2]  Erol Tutumluer,et al.  Investigation of Aggregate Properties Influencing Railroad Ballast Performance , 2013 .

[3]  Erol Tutumluer,et al.  Image Analysis Technique for Aggregate Morphology Analysis with Two-Dimensional Fourier Transform Method , 2012 .

[4]  E. Masad,et al.  Correlation of Fine Aggregate Imaging Shape Indices with Asphalt Mixture Performance , 2001 .

[5]  Y. Hochberg.  A sharper Bonferroni procedure for multiple tests of significance , 1988 .

[6]  Erol Tutumluer,et al.  Test Methods for Characterizing Aggregate Shape, Texture, and Angularity , 2005 .

[7]  M. Kutay,et al.  Comparison of 2D and 3D image-based aggregate morphological indices , 2011 .

[8]  Sesh Commuri,et al.  Inclusion of aggregate angularity, texture, and form in estimating dynamic modulus of asphalt mixes , 2012 .

[9]  Zhanping You,et al.  Automated real aggregate modelling approach in discrete element method based on X-ray computed tomography images , 2017 .

[10]  Erol Tutumluer,et al.  Quantification of Coarse Aggregate Angularity Based on Image Analysis , 2002 .

[11]  Erol Tutumluer,et al.  VIDEO IMAGE ANALYSIS OF AGGREGATES , 2000 .

[12]  Erol Tutumluer,et al.  Evaluation of image analysis techniques for quantifying aggregate shape characteristics , 2007 .

[13]  Erol Tutumluer,et al.  Characterisation of unbound aggregate materials considering physical and morphological properties , 2017 .

[14]  Eyad Masad.  THE DEVELOPMENT OF A COMPUTER CONTROLLED IMAGE ANALYSIS SYSTEM FOR MEASURING AGGREGATE SHAPE PROPERTIES , 2003 .

[15]  E. Tutumluer,et al.  Aggregate Morphology Affecting Strength and Permanent Deformation Behavior of Unbound Aggregate Materials , 2008 .

[16]  Thomas Bennert,et al.  Coarse Aggregate Angularity and Its Relationship to Permanent Deformation of Gravel-Aggregate Hot-Mix Asphalt in New York State , 2011 .

[17]  Linbing Wang,et al.  Unified Method to Quantify Aggregate Shape Angularity and Texture Using Fourier Analysis , 2005 .

[18]  Fredrik Bergholm,et al.  Digital sieving-Matlab based 3-D image analysis , 2012 .

[19]  Norbert H. Maerz,et al.  Technical and Computational Aspects of the Measurement of Aggregate Shape by Digital Image Analysis , 2004 .

[20]  Pc Knodel,et al.  Quantification of Particle Shape and Angularity Using the Image Analyzer , 1991 .

[21]  Aybike Ongel,et al.  Investigation of Noise and Durability Performance Trends for Asphaltic Pavement Surface Types: Three-Year Results , 2009 .

[22]  M. Emin Kutay,et al.  3D Micromechanical Simulation of Compaction of Hot Mix Asphalt Using Real Aggregate Shapes Obtained from X-Ray CT , 2010 .

[23]  Erol Tutumluer,et al.  Application of LADAR in the Analysis of Aggregate Characteristics , 2012 .

[24]  Erol Tutumluer,et al.  Evaluation of Aggregate Imaging Techniques for Quantification of Morphological Characteristics , 2013 .

[25]  Yong-Rak Kim,et al.  Experimental Testing and Finite Element Modeling to Evaluate Effects of Aggregate Angularity on Bituminous Mixture Performance , 2010 .

[26]  Erol Tutumluer,et al.  Evaluation of Aggregate Size and Shape by Means of Segmentation Techniques and Aggregate Image Processing Algorithms , 2013 .

[27]  Zhanping You,et al.  Aggregate representation for mesostructure of stone based materials using a sphere growth model based on realistic aggregate shapes , 2016 .

[28]  Markus Hadwiger,et al.  Real-time volume graphics , 2006, Eurographics.