Appendix A Literature Review
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- Maryann Lawson
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1 Appendix A Literature Review 1. Introduction Morphological characteristics of aggregates have significant influence on the performance of construction materials such as HMA and hydraulic PCC. Consequently, quantification of the morphological characteristics of aggregates is essential for quality control of both aggregates and construction. There are two main types of methods to characterize aggregate morphological properties: direct methods and indirect methods. In the indirect methods, experimental tests are conducted to measure strength, deformation, and uncompacted air void content, which are correlated to the shape, angularity, and texture properties of aggregates. These methods usually cannot provide separate measurements of shape, angularity, and texture, which contribute to the deformation and strength of mixtures differently. Unlike the direct methods, the direct methods and especially the imaging techniques provide an efficient means to rapidly measure aggregate shape, angularity, and texture properties. Due to the advancement of digital image techniques and availability of low-cost image processing techniques, imaging techniques are used to directly characterize aggregate properties using various apparatuses such as CCD cameras and laser scanners (Kuo et al, 1996; Wang et al., 1997; Jahn, 2000; Tutumluer et al., 2000; Masad, 2001; Masad et al., 2007; Wang et al., 2005). The imaging techniques differ significantly in the analysis method and provide different parameters of morphological characteristics. This review discusses 13 prevalent imaging techniques for the quantification of aggregate morphological characteristics. 2. Imaging Techniques Based on whether aggregates are moving during image capture, imaging techniques for aggregate morphological characterization can generally be divided into two different methods: dynamic digital image method and static digital image method.
2 Imaging Techniques Early image method Dynamic video method Statistic video method Table A-1. Camera setup and features of the imaging techniques. VDG-40 videograder Computerized particle analyzer (CPA) Micrometrics Optisizer (PSDA) Video Imaging System (VIS) Buffalo Wire Works System Camsizer Winshape UIAIA Portable Image Analysis System (PIAS) Laser-based Aggregate Analysis System (LAAS) AIMS II Camera Setup Digitizer with microcomputer One line-scan CCD camera Two digital cameras Two orthogonal cameras Three orthogonal positioned cameras Image Information Morphological Calculation Methods Shape Angularity Texture 2-D Shape N/A N/A 2-D 2-D 2-D Length and width Gradation and aspect ratio Shape, and gradation N/A N/A N/A N/A N/A N/A 2-D Shape N/A N/A 2-D Grading N/A N/A 2-D 3-D Sphericity and aspect ratio Grading and aspect ratio 2-D Sphericity and aspect ratio N/A Minimum average curve radius method Change of outline slope One digital camera 2-D Fourier transform method One CCD camera 3-D Aspect ratio Wavelet method One camera with microscope 2-D FTI system One CCD camera 3-D Sphericity and aspect ratio Sphericity and aspect ratio Gradient method N/A N/A Erosion and dilation technique Wavelet method Two-dimensional Fourier transform method
3 Table A-2. Advantages and disadvantages of the imaging techniques. Imaging Techniques Aggregate Size Range Advantages Disadvantages Early image method #8 1 Measure shape No angularity or texture addressed VDG-40 videograder # Measure shape of large Assume idealized ellipsoid as aggregate quantity particle shape; cannot measure CPA # angularity and texture; use one camera magnification to take images of all aggregate sizes PSDA # Separate vibratory feed systems VIS # and backlights required to scan Dynamic digital image method Statistic digital image method Buffalo Wire Works System PSDA Camsizer #50 1/2 Winshape #4 1 UIAIA #4 1.5 PIAS #200 1 LAAS #10 4 AIMS II #200 1 FTI system #50 3/4 (Based on Masad et al., 2007) fine and coarse samples Measure shape of aggregates 2-D shape information; no # angularity or texture addressed Measure shape of large Expensive aggregate quantity Assume idealized ellipsoid as Use two cameras to capture particle shape images at different No texture addressed magnifications based on aggregate sizes Measure shape of large aggregate quantity with three dimensions measured Measure shape of large aggregate quantity with three dimensions measured Measure shape, angularity, and texture Use different scans to image based on aggregate sizes Measure three dimensions of aggregates Capture images at different resolution using microscope based on aggregate sizes Measure three dimensions of aggregates with 3-D image data No texture addressed Use one camera magnification to take images of all sizes Separate vibratory feed systems and backlights required to scan fine and coarse samples Use one camera magnification to take images of all aggregate sizes Calculation based on twodimensional outlines of aggregates Use the same scan to analyze aggregates of different sizes Expensive Use the same scan to analyze aggregates with different sizes Table A-1 presents the camera setups, the dimensions that the system can measure, and morphology calculation methods of the 13 imaging techniques. Table A-2 shows the advantages and disadvantages of these imaging techniques.
4 2.1 Early Imaging Methods The early imaging techniques applied relatively low-cost digitizers and microcomputers to rapidly measure aggregate properties in terms of shape, surface area, and roughness (Barksdale et al., 1991). These methods often used AutoCAD and spreadsheet programs. Early imaging techniques, such as the one by Prowell and Brown (2005), are usually viable for particles larger than #80 sieve. With these methods, aggregates are usually placed in clear trays, and photos are taken to produce 2-D images. The images are then analyzed with a digitized table. The thicknesses of aggregates are measured using a Vernier caliper. Shape factors are determined as elongation ratio and flatness ratio. 2.2 Dynamic Digital Image Method VDG-40 Videograder The VDG-40 videograder is capable of imaging aggregates ranging in sizes from #16 to at least 1.5 in. Aggregates are placed into a hopper and extracted by an electromagnetic vibrator into a long feed channel toward a separator drum. The VDG-40 uses a line-scan CCD camera to capture 2-D images of aggregates. It then calculates the lengths and widths of the aggregates. Every aggregate will be imaged as it falls in front of the backlight. An assumption is made that every particle is elliptical in order to calculate the third dimension from the captured 2-D images. Currently there is no standard specification for sample size. A minimum of 50 particles is recommended for achieving a statistically stable result. It is worth noting that the VDG-40 images and analyzes every particle in the sample. The results are found to have a good correlation with manual measurements of flat and elongated aggregates (Weingart et al., 1999; Tutumluer et al., 2000) Computerized Particle Analyzer The computerized particle analyzer (CPA) can image and analyze aggregates ranging from #140 to 1.5 in. (Tyler, 2001). During a CPA test, aggregates are placed into a metal hopper and transported on the conveyor from the bottom of the hopper to the edge, where they fall off a curtain between the imaging device and backlight. It is worth noting that CPA can be used both in laboratories and on a production line for industry. This system uses two methods to process images for shape analysis of aggregates: the size method and the shape method. The size method chooses the largest chord of a particle as its
5 diameter based on its image, whereas the shape method uses the diameter of a circle with equivalent circumference. The results will finally be compiled into a size distribution with the percentage of particle counts for every size fraction in a custom software package Micrometrics Optisizer Particle Size Distribution Analyzer The Micrometrics Optisizer particle size distribution analyzer (PSDA) system can process aggregates with sizes ranging from #200 mesh sieve size up to 1.5 in. In the Micrometrics Optisizer PSDA system, aggregates are placed into a feed cone and gradually deposited onto a vibrating channel, which disperses and transports aggregates so that they can fall in front of a backlight; aggregates should be separated into coarse aggregates and fine aggregates. This system uses a CCD camera to capture 2-D images of falling particles twice per second (Raunch et al., 2002). Due to the slow imaging rate, only a portion of aggregates could be captured for further analysis of particle size gradation. The PSDA system has two ways to analyze aggregate shapes: the spherical method and the cubic method. The spherical method takes the profile area of the aggregate as the same area of a projected circle of a sphere. The volume can be calculated using the equivalent radius of this circle. Conversely, the cubic method converts the aggregate profile into a square of equal area, and the volume is calculated using the equivalent side dimension from the square Video Image System The video image system (VIS) can image and analyze aggregates with sizes ranging from #16 to 1.5 in. Like the Micrometrics Optisizer system, the VIS system uses a sample holder to accommodate a large number of aggregates that is fed onto a vibrating chute, and uses a linescan CCD camera to capture images of falling aggregates. These 2-D images can be further analyzed via the VIS software package to generate reports with the shape properties of aggregates Buffalo Wire Works Particle Size Distribution Analyzer Buffalo Wire Works PSDA has the capability of analyzing particles raging from #200 sieve size to 1.5 in. Similar to the previously discussed imaging techniques, the Buffalo Wire Works PSDA uses a line-scan CCD camera to capture images of falling particles in front of the backlight. These 2-D images can be further analyzed to calculate the form factor defined as follows: Form factor = (Perimeter circle /Perimeter) 2 Eq. A-1
6 where Perimeter is the perimeter of an aggregate profile, and Perimeter circle is the perimeter of an equivalent circle that has the same area as the aggregate profile. The VDG-40, CPA, Micrometrics Optisizer, VIS, and Buffalo Wire Works PSDA all use a line-scan CCD camera to evaluate aggregates as they fall in front of backlighting. Differences between the five imaging systems are physical configurations and software packages. Like the Micrometrics Optisizer and VIS, the Buffalo Wire Works PSDA does not capture images for every particle and requires separate feed systems and backlights to scan coarse and fine aggregates Camsizer The Camsizer system images and analyzes aggregates ranging from #50 to 3/4 in. using two optically matched CCD cameras to automatically capture images at different resolutions as aggregates fall in front of the backlighting (Retsch Technology-Camsizer, 2011). Aggregates fall off at the end of the hopper and are transported using a vibrating tube and finally fall between the light source and the camera. When aggregates are falling, this system takes images of coarse aggregates by one camera and fine aggregates by the other Winshape The Winshape system is generally used for coarse aggregate analysis. It uses two orthogonally positioned synchronized cameras to capture images as aggregates passing on a mini-conveyor (first version) or on a rotating circular lighting table (the latest version). It has the capability to quickly analyze a lot of aggregates and output the size distribution curves and shape measurement summary according to size classes. It reports angularity using the minimum average curve radius method. Both Camsizer and Winshape use two CCD cameras to capture images of aggregates. However, the two techniques are different in their instrumental setups. The Camsizer generates 2-D data to determine shape, whereas Winshape can generate 3-D shape information as the two cameras capture images from two orthogonal views University of Illinois Aggregate Image Analyzer UIAIA uses three orthogonally positioned cameras to capture projections of aggregates as they are individually placed on the conveyer belt. The 3-D shape of each particle can be established based on three projections captured with three cameras. The cameras are triggered by a sensor that detects an aggregate passing a certain position on the belt. This system can
7 distinguish flat and elongated aggregates and automatically calculate angularity and texture for coarse aggregates. However, aggregates with a similar color to the conveyer belt cannot be imaged. In the UIAIA system, shape is described using sphericity, flatness ratio, and elongation ratio; angularity is described by the angularity index (AI) method as illustrated in Figure A-1. The AI method traces the changes of slope of the 2-D profile outline of the particle and uses a weighted average value of its angularity determined from three views (front, top, side). Eq. A-2 shows the calculation of angularity for each image; Eq. A-3 is used to calculate AI. Figure A-1. Illustration of an n-sided polygon approximating the outline of a particle (Rao et al., 2002). AI = Eq. A-2 Eq. A-3 where e is the dummy index starting from zero and at an interval of 10 ; P(e) is the probability that change in angle α from e to e+10; Area front, Area top, and Area side represent areas of profiles of the front view, top view and side view, respectively. Surface texture is analyzed using an erosion and dilation technique (Masad et al., 2000), shown in Figure A-2, in which Figure A-2(a) is the original aggregate image. Aggregates gradually lost their surface irregularity during the erosion-dilation process. The corresponding area lost is reported as a percentage of the area in the original image. The percentage of lost area is defined as texture and is defined by Eq. A-4; surface texture (ST) is defined by Eq. A Angularity = e Pe ( ) ( Angularity Area ) + ( Angularity Area ) + ( Angularity Area ) Area + Area + Area e= 0 front top side front top side A A A 1 2 Texture= 100% 1 Eq. A-4
8 ST = ( Texture Area ) + ( Texture Area ) + ( Texture Area ) front top side Area + Area + Area front top side where A 1 and A 2 are areas of aggregates before and after erosion-dilation cycles, respectively. Eq. A-5 Figure A-2. Illustration of erosion and dilation technique (Masad et al., 2000) Portable Image Analysis System The Portable Image Analysis System (PIAS) uses an integral pocket computer camera to capture 2-D images of both coarse and fine aggregates with different resolutions depending on aggregate sizes (Wang et al., 2008). Acquired gray images are transformed into binary images to detect aggregate profile outlines and calculate the morphological characteristics of aggregates, including a shape factor, an angularity factor, and a texture factor using the fast Fourier transform method through MATLAB. There are 360 points selected from each aggregate profile and transformed into radial coordinates, with the origin of the coordinate system as the gravity center. Coordinates of 180 points are analyzed using a Fourier series. Among Fourier coefficients, shape is represented by changes of aggregate profiles at low frequencies, followed by angularity at medium frequencies, and then texture represented by changes at high frequencies (Wang et al., 2004). Shape factor, angularity factor, and texture factor are defined using the following equations a n b n α Eq. A-6 s = + 2 n= 1 a0 a a n b n α Eq. A-7 a = + 2 n= 5 a0 a a n b n α Eq. A-8 t = + 2 n= 26 a0 a 0 where α s, α a, and α t are shape factor, angularity factor, and texture factor, respectively; a 0 is the
9 zero-frequency coefficient; a n and b n are real parts and imaginary parts of Fourier coefficients corresponding to frequency n, respectively; and n = 1, 2,, 180. It has been proven that PIAS can effectively assess coarse aggregate morphology using the MATLAB program and images with high enough resolutions (Wang et al., 2009). However, PIAS cannot detect aggregate thickness because captured aggregate profiles are twodimensional Statistic Digital Image method Laser-Based Aggregate Scanning System The laser-based aggregate scanning system (LASS) images and analyzes aggregates ranging from #10 to 4 in. LASS captures images of aggregate particles using a laser line scanner with a 120-mm scan width (Haas et al., 2002). The laser scanner moves on a horizontal gantry and passes over particles scattered on the aggregate platform to image a 3-D surface of aggregates (Kim et al., 2003). By reconstructing a 3-D surface of aggregates from laser scanner images, grain size distribution, angularity, and texture can be determined using the wavelet method Aggregate Image Measurement System AIMS II can image and analyze aggregates of a wide size range (i.e., from #200 to 1 in.). AIMS II uses a digital camera with an autofocus microscope to automatically capture images with different resolutions depending on aggregate size. This system measures three dimensions of aggregates to calculate sphericity and aspect ratios, including flatness ratio and elongation ratio. It can also calculate angularity of aggregates of all sizes using the gradient method and texture of coarse aggregates using the wavelet method (Masad et al., 2001). The influence of shape on angularity is normalized via the division of measurements by the equivalent ellipse dimensions. Figure A-3 illustrates the difference in gradient between particles. Angularity is defined by Eq. A-9. Texture is defined by Eq. A-10 at a given level using the wavelet method. 360 θ Angularity = θ = 0 R Pθ R R EEθ EEθ Eq. A-9 3 N 1 Texture =, 3 N i = 1 j = 1 ( ( )) 2 Di, j xy Eq. A-10 where R Pθ is the radius of the particle at a directional angle of θ; R EEθ is the radius of an
10 equivalent ellipse at the same θ; N denotes the level of decomposition; i takes values of 1, 2, and 3 for the three images of texture; and j is the wavelet coefficient index. Figure A-3. Illustration of the difference in gradient between particles (Chandan et al., 2004). 3. Summary Imaging techniques are capable of rapidly measuring aggregate shape, angularity, and texture characteristics separately. Even though some apparatuses of the imaging techniques are expensive, the unit costs of incremental tests are low. The imaging techniques typically have the capability of automatically quantifying morphological characteristics with accurate and reliable results. However, the following issues should be taken into consideration for further improvements. (1) The imaging techniques vary in analysis methods, in which results are sometimes incomparable to each other. For instance, some imaging techniques analyze 2-D images while others utilize 2-D projections to reconstruct 3-D surfaces of aggregates. (2) Some imaging techniques can only analyze coarse aggregates, whereas other techniques can analyze aggregates of wide size ranges, including both coarse and fine aggregates. Consequently, it is difficult to evaluate the merits of the different imaging techniques. (3) Some imaging techniques are sensitive to various influence factors, such as temperature, backlight (or background), and electric devices, which might lead to unreliable results. (4) Measurements using imaging techniques require instrumental setups and well-trained operators.
11 References Barksdale, R. D., Kemp, M. A., Sheffield, W. J., and Hubbard, J. L. (1991). Measurement of Aggregate Shape, Surface Area, and Roughness. Transportation Research Record TRB, National Research Council, Washington, D.C., Chandan, C., Sivakumar, K., Fletcher, T., Masad, E. (2004). Geometry Analysis of Aggregate Particles Using Imaging Techniques. Journal of Computing in Civil Engineering, 18(1), Haas, C. T., Rauch, A. F., Kim, H., and Browne, C. (2002). Automation of Aggregate Characterization Using Laser Profiling and Digital Image Analysis. International Center for Aggregates Research (ICAR) Report, Aggregate Foundation for Technology, Research, and Education, Washington, D.C. Jahn, D. (2000). Evaluation of Aggregate Particle Shapes Through Multiple Ratio Analysis. Proceedings of the 8th Annual Symposium of the International Center for Aggregates Research, Denver, CO. Kim H., Haas, C. T., Rauch, A.F., and Browne, C. (2003). Wavelet-Based Three-Dimensional Descriptors of Aggregate Particles. Transportation Research Record: Journal of the Transportation Research Board, No Transportation Research Board of the National Academies, Washington, D.C., Kuo, C. Y., Frost, J. D., Lai, J. S., and Wang, L. B. (1996). Three-Dimensional Image Analysis of Aggregate Particles from Orthogonal Projections. Transportation Research Record TRB, National Research Council, Washington, D.C., Masad, E. and Button, J. (2000). Unified Imaging Approach for Measuring Aggregate Angularity and Texture. Journal of Computer-Aided Civil and Infrastructure Engineering, 15(4), Masad, E. (2001). Review of Imaging Techniques for Characterizing the Shape of Aggregates Used in Asphalt Mixes. Proceedings of the 9th Annual Symposium Of The International Center for Aggregates Research, Austin, TX. Masad E., Olcott, D., White, T., Tashman, L. (2001). Correlation of Fine Aggregate Imaging Shape Indices with Asphalt Mixture Performance. Transportation Research Record: Journal of the Transportation Research Board, No TRB, National Research Council, Washington, D.C.,
12 Masad, E., Al-Rousan, T., Button, J., Little, D., and Tutumluer, E. (2007). NCHRP Report 555: Testing Methods for Characterizing Aggregate Shape, Texture, and Angularity. Transportation Research Board of the National Academies, Washington, D.C. Prowell, B. D., Zhang, J., and Brown, E. R. (2005). NCHRP Report 539: Aggregate Properties and the Performance of Superpave-Designed Hot Mix Asphalt. Transportation Research Board of the National Academies, Washington, D.C. Rao C., Tutumluer, E., and Kim, I. T. (2002). Quantification of Coarse Aggregate Angularity Based on Image Analysis. Transportation Research Record: Journal of the Transportation Research Board, No Transportation Research Board of the National Academies, Washington, D.C., Raunch, A. F., Haas, C. T., Browne, C., and Kim, H. (2002). Rapid Test to Estimate Grading of Unbounded Aggregate Products: An Evaluation of Automated Devices to Replace and Augment Manual Sieve Analyses in Determining Aggregate Gradation. International Center for Aggregates Research (ICAR) Report, Aggregate Foundation for Technology, Research, and Education, Washington, D.C. Retsch Technology Camsizer website. (2011). Tutumluer, E., Rao, C., and Stefanski, J. (2000). Video Image Analysis of Aggregates. Final Project Report, FHWA-IL-UI-278, Civil Engineering Studies UILU-ENG , University of Illinois Urbana-Champaign, Urbana, IL. Tyler, W. S. (2001). Particle Size and Shape Analyzer (CPA), Product brochure, Mentor, OH. Wang, L. B., Lai, J. S. and Frost, J. D. (1997). Fourier Morphological Descriptors of Aggregate Profiles. In Proceedings of Second International Conference on Image Technology: Techniques and Applications in Civil Engineering, Wang, L. B., Frost, J. D., and Lai, J. S. (2004). Characterization of Air Void Distribution in Asphalt Mixes Using X-Ray CT. Journal of Materials in Civil Engineering, 14(2), Wang, L. B., Wang, X. R., Mohammad, L., and Abadie, C. (2005). Unified Method of Quantify Aggregate Shape Angularity and Texture Using Fourier Analysis. Journal of Materials in Civil Engineering, Vol. 17, No. 5, Wang, L. B., Lane, D. S., Lu, Y., and Druta, C. (2008). Portable Image Analysis System for
13 Characterizing Aggregate Morphology. Final contract report VTRC08-CR11, Virginia Transportation Research Council (VTRC). Wang, L. B., Lane, D. S., Lu, Y., and Druta, C. (2009). Portable Image Analysis System for Characterizing Aggregate Morphology. Transportation Research Record: Journal of the Transportation Research Board, No Transportation Research Board of the National Academies, Washington, D.C., Weingart, R. L. and Prowell, B. D. (1999). Specification Development Using the VDG-40 Videograder for Shape Classification of Aggregates. Proceedings of the 7th Annual Symposium of the International Center for Aggregates Research, University of Texas, Austin, TX.
14 Appendix E Photographs of Coarse Aggregates in Set 1 Blast furnace slag 3/4 in. E-1
15 Blast furnace slag 1/2 in. Blast furnace slag 3/8 in. E-2
16 Blast furnace slag #4 Copper ore 3/4 in. E-3
17 Copper ore 1/2 in. Copper ore 3/8 in. E-4
18 Copper ore #4 Dolomite 3/4 in. E-5
19 Dolomite 1/2 in. Dolomite 3/8 in. E-6
20 Dolomite #4 Glacial gravel crushed 3/4 in. E-7
21 Glacial gravel crushed 1/2 in. Glacial gravel crushed 3/8 in. E-8
22 Glacial gravel crushed #4 Glacial gravel rounded 3/4 in. E-9
23 Glacial gravel rounded 1/2 in. Glacial gravel rounded 3/8 in. E-10
24 Glacial gravel rounded #4 Iron ore 3/4 in. E-11
25 Iron ore 1/2 in. Iron ore 3/8 in. E-12
26 Iron ore #4 Limestone 3/4 in. E-13
27 Limestone 1/2 in. Limestone 3/8 in. E-14
28 Limestone #4 E-15
29 Appendix F FTI Analysis Results 1. Shape Blast Furnace Slag 3/4 Sphericity Flatness Ratio Elongation Ratio Blast Furnace Slag 1/2 Sphericity Flatness Ratio Elongation Ratio bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs Blast Furnace Slag 3/8 Sphericity Flatness Ratio Elongation Ratio Blast Furnace Slag #4 Sphericity Flatness Ratio Elongation Ratio F-1
30 bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs bfs F-2
31 Copper Ore 3/4 Sphericity Flatness Ratio Elongation Ratio Copper Ore 1/2 Sphericity Flatness Ratio Elongation Ratio co E E E-01 co co E E E-01 co co E E E-01 co co E E E-01 co co E E E-01 co co E E E-01 co co E E E-01 co co E E E-01 co co E E E-01 co co E E E-01 co co E E E-01 co co E E E-01 co co E E E-01 co co E E E-01 co co E E E-01 co co E E E-01 co co E E E-01 co co E E E-01 co co E E E-01 co co E E E-01 co co E E E-01 co co E E E-01 co co E E E-01 co co E E E-01 co co E E E-01 co co E E E-01 co co E E E-01 co co E E E-01 co co co co co co F-3
32 Copper Ore 3/8 Sphericity Flatness Ratio Elongation Ratio Copper Ore #4 Sphericity Flatness Ratio Elongation Ratio co co co co co co co co co co co co co co co co co co co co co co co co co co co co co co co co co co co co co co co co co co co co co F-4
33 Dolomite Flatness Elongation Dolomite Flatness Elongation Sphericity Sphericity 3/4 Ratio Ratio 1/2 Ratio Ratio dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt F-5
34 Dolomite Flatness Elongation Dolomite Flatness Elongation Sphericity Sphericity 3/8 Ratio Ratio #4 Ratio Ratio dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt dlt Glacial Gravel Crushed 3/4 Sphericity Flatness Ratio Elongation Ratio Glacial Gravel Crushed 1/2 Sphericity Flatness Ratio Elongation Ratio F-6
35 ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc F-7
36 Glacial Gravel Crushed 3/8 Sphericity Flatness Ratio Elongation Ratio Glacial Gravel Crushed #4 Sphericity Flatness Ratio Elongation Ratio ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc ggc F-8
37 Glacial Gravel Rounded 3/4 Sphericity Flatness Ratio Elongation Ratio Glacial Gravel Rounded 1/2 Sphericity Flatness Ratio Elongation Ratio ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr F-9
38 Glacial Gravel Rounded 3/8 Sphericity Flatness Ratio Elongation Ratio Glacial Gravel Rounded #4 Sphericity Flatness Ratio Elongation Ratio ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr ggr F-10
39 Iron Ore 3/4 Sphericity Flatness Ratio Elongation Ratio Iron Ore 1/2 Sphericity Flatness Ratio Elongation Ratio io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io F-11
40 Iron Ore 3/8 Sphericity Flatness Ratio Elongation Ratio Iron Ore #4 Sphericity Flatness Ratio Elongation Ratio io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io io F-12
41 Limestone Flatness Elongation Limestone Flatness Elongation Sphericity 122 Sphericity 3/4 Ratio Ratio 1/2 Ratio Ratio lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst Limestone Flatness Elongation Limestone Flatness Elongation Sphericity Sphericity 3/8 Ratio Ratio #4 Ratio Ratio lst lst F-13
42 lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst lst Angularity and Texture Blast Furnace Slag Blast Furnace Slag Angularity Texture 3/4 1/2 Angularity Texture bfs E E-05 bfs E E-05 bfs E E-05 bfs E E-06 bfs E E-05 bfs E E-06 bfs E E-05 bfs E E-05 F-14
43 bfs E E-07 bfs E E-07 bfs E E-06 bfs E E-05 bfs E E-06 bfs E E-06 bfs E E-06 bfs E E-06 bfs E E-05 bfs E E-05 bfs E E-06 bfs E E-06 bfs E E-06 bfs E E-05 bfs E E-06 bfs E E-07 bfs E E-06 bfs E E-05 bfs E E-07 bfs E E-06 bfs E E-06 bfs E E-06 bfs E E-06 bfs E E-06 bfs E E-06 bfs E E-06 bfs E E-05 bfs E E-06 bfs E E-06 bfs E E-06 bfs E E-05 bfs E E-06 bfs E E-07 bfs E E-06 bfs E E-06 bfs E E-06 bfs E E-06 bfs E E-07 bfs E E-06 bfs E E-05 bfs E E-06 bfs E E-06 bfs E E-06 bfs E E-06 bfs E E-06 bfs E E-06 bfs E E-06 bfs E E-06 bfs E E-06 bfs E E-06 bfs E E-05 bfs E E-05 Average 2.74E E-06 bfs E E-06 Standard deviation 6.59E E-06 bfs E E-05 Average 2.44E E-06 Standard deviation 5.18E E-05 Blast Furnace Slag 3/8 Angularity Texture Blast Furnace Slag #4 Angularity Texture bfs E E-07 bfs E E-05 bfs E E-06 bfs E E-04 bfs E E-06 bfs E E-05 bfs E E-06 bfs E E-05 bfs E E-06 bfs E E-05 bfs E E-06 bfs E E-05 bfs E E-06 bfs E E-05 bfs E E-05 bfs E E-06 F-15
44 bfs E E-06 bfs E E-06 bfs E E-05 bfs E E-05 bfs E E-06 bfs E E-05 bfs E E-06 bfs E E-04 bfs E E-05 bfs E E-06 bfs E E-06 bfs E E-06 bfs E E-05 bfs E E-05 bfs E E-06 bfs E E-06 bfs E E-05 Average 1.47E E-05 bfs E E-06 Standard deviation 4.99E E-04 bfs E E-06 bfs E E-06 bfs E E-06 bfs E E-06 bfs E E-07 bfs E E-05 Average 2.19E E-06 Standard deviation 4.48E E-05 F-16
45 Copper Ore 3/4 Angularity Texture Copper Ore 1/2 Angularity Texture co E E-06 co E E-06 co E E-05 co E E-06 co E E-06 co E E-05 co E E-05 co E E-06 co E E-05 co E E-06 co E E-06 co E E-07 co E E-06 co E E-06 co E E-06 co E E-07 co E E-06 co E E-07 co E E-06 co E E-06 co E E-07 co E E-06 co E E-06 co E E-06 co E E-06 co E E-07 co E E-05 co E E-06 co E E-06 co E E-06 co E E-06 co E E-06 co E E-06 co E E-06 co E E-06 co E E-07 co E E-07 co E E-07 co E E-06 co E E-06 co E E-06 co E E-07 co E E-06 co E E-06 co E E-06 co E E-06 co E E-07 co E E-07 co E E-06 co E E-07 co E E-06 co E E-06 co E E-06 co E E-07 co E E-06 co E E-06 co E E-06 co E E-05 co E E-06 co E E-05 Average 2.69E E-06 co E E-06 Standard deviation 4.52E E-06 co E E-06 co E E-06 Average 9.76E E-06 Standard deviation 1.14E E-06 F-17
46 Copper Ore 3/8 Angularity Texture Copper Ore #4 Angularity Texture co E E-06 co E E-05 co E E-06 co E E-06 co E E-06 co E E-06 co E E-06 co E E-07 co E E-06 co E E-06 co E E-06 co E E-06 co E E-06 co E E-06 co E E-06 co E E-04 co E E-05 co E E-06 co E E-06 co E E-05 co E E-07 co E E-05 co E E-06 co E E-05 co E E-06 co E E-04 co E E-06 co E E-05 co E E-07 co E E-07 co E E-07 co E E-05 Average 7.47E E-06 co E E-04 Standard deviation 1.09E E-06 co E E-03 co E E-04 co E E-04 co E E-04 co E E-04 co E E-04 co E E-05 co E E-05 co E E-04 co E E-06 co E E-05 co E E-05 co E E-05 Average 4.25E E-04 Standard deviation 7.44E E-04 F-18
47 Dolomite 3/4 Angularity Texture Dolomite 1/2 Angularity Texture dlt E E-06 dlt E E-07 dlt E E-06 dlt E E-07 dlt E E-06 dlt E E-06 dlt E E-06 dlt E E-06 dlt E E-06 dlt E E-06 dlt E E-06 dlt E E-07 dlt E E-06 dlt E E-06 dlt E E-06 dlt E E-06 dlt E E-06 dlt E E-07 dlt E E-07 dlt E E-06 dlt E E-07 dlt E E-06 dlt E E-06 dlt E E-06 dlt E E-07 dlt E E-06 dlt E E-06 dlt E E-07 dlt E E-06 dlt E E-06 dlt E E-05 dlt E E-07 dlt E E-05 dlt E E-06 dlt E E-06 dlt E E-07 dlt E E-06 dlt E E-05 dlt E E-06 dlt E E-06 dlt E E-06 dlt E E-06 dlt E E-06 dlt E E-06 dlt E E-06 dlt E E-07 dlt E E-06 dlt E E-07 dlt E E-06 dlt E E-07 dlt E E-06 dlt E E-06 dlt E E-06 dlt E E-07 dlt E E-06 dlt E E-07 dlt E E-06 dlt E E-07 dlt E E-07 dlt E E-06 dlt E E-06 dlt E E-06 dlt E E-06 Average 5.99E E-06 dlt E E-06 Standard deviation 1.13E E-06 Average 1.58E E-06 Standard deviation 3.07E E-06 Dolomite 3/8 Angularity Texture Dolomite #4 Angularity Texture dlt E E-06 dlt E E-07 dlt E E-06 dlt E E-06 F-19
48 dlt E E-06 dlt E E-06 dlt E E-06 dlt E E-06 dlt E E-06 dlt E E-05 dlt E E-06 dlt E E-05 dlt E E-06 dlt E E-05 dlt E E-06 dlt E E-03 dlt E E-06 dlt E E-05 dlt E E-06 dlt E E-05 dlt E E-07 dlt E E-05 dlt E E-06 dlt E E-06 dlt E E-07 dlt E E-05 dlt E E-06 dlt E E-05 dlt E E-07 dlt E E-05 dlt E E-06 dlt E E-05 dlt E E-05 dlt E E-04 dlt E E-07 dlt E E-04 dlt E E-06 dlt E E-05 dlt E E-07 dlt E E-05 dlt E E-07 dlt E E-05 dlt E E-07 dlt E E-05 dlt E E-06 dlt E E-05 dlt E E-07 dlt E E-05 dlt E E-06 dlt E E-05 dlt E E-06 dlt E E-05 dlt E E-09 dlt E E-05 dlt E E-07 dlt E E-05 dlt E E-07 dlt E E-06 dlt E E-06 dlt E E-05 Average 8.74E E-06 dlt E E-05 Standard deviation 9.73E E-06 dlt E E-05 dlt E E-05 dlt E E-06 dlt E E-06 Average 1.57E E-05 Standard deviation 2.05E E-04 Glacial Gravel Crushed 3/4 Angularity Texture Glacial Gravel Crushed 1/2 Angularity Texture ggc E E-06 ggc E E-06 ggc E E-05 ggc E E-06 ggc E E-05 ggc E E-05 F-20
49 ggc E E-05 ggc E E-06 ggc E E-06 ggc E E-08 ggc E E-07 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-07 ggc E E-07 ggc E E-07 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-07 ggc E E-06 ggc E E-07 ggc E E-06 ggc E E-06 ggc E E-07 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-07 ggc E E-07 ggc E E-07 ggc E E-07 ggc E E-07 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-07 ggc E E-08 ggc E E-06 ggc E E-06 ggc E E-07 ggc E E-06 ggc E E-06 ggc E E-07 ggc E E-06 ggc E E-06 ggc E E-05 ggc E E-07 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-07 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-05 ggc E E-05 Average 1.46E E-06 ggc E E-06 Standard deviation 2.51E E-06 Average 2.31E E-06 Standard deviation 4.25E E-06 Glacial Gravel Crushed 3/8 Angularity Texture Glacial Gravel Crushed #4 Angularity Texture ggc E E-06 ggc E E-07 ggc E E-06 ggc E E-05 ggc E E-06 ggc E E-05 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-05 ggc E E-07 ggc E E-05 F-21
50 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-05 ggc E E-06 ggc E E-05 ggc E E-06 ggc E E-06 ggc E E-05 ggc E E-07 ggc E E-06 ggc E E-06 ggc E E-05 ggc E E-05 ggc E E-06 ggc E E-06 ggc E E-05 ggc E E-07 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-06 ggc E E-05 ggc E E-07 ggc E E-06 ggc E E-06 ggc E E-05 ggc E E-06 ggc E E-07 ggc E E-07 ggc E E-06 ggc E E-05 Average 1.47E E-06 ggc E E-06 Standard deviation 1.98E E-05 ggc E E-07 Average 2.48E E-06 Standard deviation 2.66E E-05 F-22
51 Glacial Gravel Rounded 3/4 Angularity Texture Glacial Gravel Rounded 1/2 Angularity Texture ggr E E-07 ggr E E-06 ggr E E-06 ggr E E-06 ggr E E-07 ggr E E-07 ggr E E-07 ggr E E-06 ggr E E-06 ggr E E-06 ggr E E-07 ggr E E-05 ggr E E-07 ggr E E-06 ggr E E-07 ggr E E-06 ggr E E-06 ggr E E-06 ggr E E-06 ggr E E-07 ggr E E-06 ggr E E-07 ggr E E-06 ggr E E-06 ggr E E-07 ggr E E-06 ggr E E-06 ggr E E-05 ggr E E-07 ggr E E-06 ggr E E-07 ggr E E-06 ggr E E-06 ggr E E-07 ggr E E-08 ggr E E-07 ggr E E-07 ggr E E-06 ggr E E-06 ggr E E-06 ggr E E-06 ggr E E-07 ggr E E-06 ggr E E-06 ggr E E-06 ggr E E-06 ggr E E-06 ggr E E-06 ggr E E-06 ggr E E-07 ggr E E-07 ggr E E-06 ggr E E-07 ggr E E-06 ggr E E-06 ggr E E-06 ggr E E-07 ggr E E-06 ggr E E-06 ggr E E-06 ggr E E-07 Average 9.71E E-06 ggr E E-07 Standard deviation 1.11E E-06 Average 8.44E E-06 Standard deviation 1.13E E-06 F-23
52 Glacial Gravel Rounded 3/8 Angularity Texture Glacial Gravel Rounded #4 Angularity Texture ggr E E-05 ggr E E-06 ggr E E-06 ggr E E-07 ggr E E-06 ggr E E-06 ggr E E-07 ggr E E-05 ggr E E-07 ggr E E-05 ggr E E-07 Average 2.48E E-05 ggr E E-06 Standard deviation 2.04E E-05 ggr E E-07 ggr E E-07 ggr E E-07 ggr E E-06 ggr E E-06 ggr E E-06 ggr E E-06 ggr E E-07 ggr E E-06 ggr E E-07 ggr E E-06 ggr E E-06 ggr E E-06 ggr E E-06 ggr E E-06 ggr E E-06 ggr E E-07 ggr E E-06 ggr E E-07 ggr E E-06 ggr E E-07 ggr E E-06 Average 1.09E E-06 Standard deviation 1.94E E-05 F-24
53 Iron Ore 3/4 Angularity Texture Iron Ore 1/2 Angularity Texture io E E-06 io E E-07 io E E-05 io E E-06 io E E-06 io E E-08 io E E-07 io E E-06 io E E-06 io E E-06 io E E-07 io E E-06 io E E-06 io E E-06 io E E-06 io E E-07 io E E-06 io E E-06 io E E-06 io E E-07 io E E-06 io E E-06 io E E-06 io E E-08 io E E-06 io E E-06 io E E-06 io E E-05 io E E-07 io E E-06 io E E-07 io E E-07 io E E-08 io E E-06 io E E-07 io E E-06 io E E-06 io E E-06 io E E-06 io E E-05 io E E-06 io E E-07 io E E-07 io E E-06 io E E-06 io E E-06 io E E-06 io E E-05 io E E-06 io E E-06 io E E-06 io E E-05 io E E-06 io E E-06 io E E-06 io E E-06 io E E-07 io E E-07 io E E-07 io E E-05 io E E-06 Average 9.93E E-06 io E E-07 Standard deviation 1.04E E-06 Average 1.01E E-06 Standard deviation 1.12E E-06 F-25
54 Iron Ore 3/8 Angularity Texture Iron Ore #4 Angularity Texture io E E-06 io E E-05 io E E-07 io E E-06 io E E-06 io E E-06 io E E-07 io E E-05 io E E-06 io E E-05 io E E-07 io E E-05 io E E-06 io E E-05 io E E-05 io E E-05 io E E-07 io E E-04 io E E-05 io E E-05 io E E-07 io E E-05 io E E-06 io E E-05 io E E-06 io E E-04 io E E-05 io E E-05 io E E-05 io E E-05 io E E-05 io E E-04 io E E-05 io E E-05 io E E-03 io E E-05 io E E-05 io E E-04 io E E-05 io E E-05 io E E-04 io E E-04 io E E-06 io E E-05 io E E-05 io E E-05 io E E-05 io E E-05 io E E-05 io E E-05 io E E-05 io E E-04 Average 1.09E E-05 Average 2.16E E-05 Standard deviation 2.36E E-04 Standard deviation 2.96E E-05 F-26
55 Limestone 3/4 Angularity Texture Limestone 1/2 Angularity Texture lst E E-07 lst E E-07 lst E E-05 lst E E-07 lst E E-07 lst E E-07 lst E E-06 lst E E-06 lst E E-06 lst E E-06 lst E E-07 lst E E-06 lst E E-06 lst E E-06 lst E E-06 lst E E-06 lst E E-06 lst E E-06 lst E E-06 lst E E-06 lst E E-06 lst E E-06 lst E E-06 lst E E-07 lst E E-06 lst E E-06 lst E E-06 lst E E-06 lst E E-06 lst E E-07 lst E E-06 lst E E-07 lst E E-06 lst E E-05 lst E E-06 lst E E-06 lst E E-06 lst E E-07 lst E E-07 lst E E-06 lst E E-07 lst E E-07 lst E E-06 lst E E-06 lst E E-06 lst E E-05 lst E E-06 lst E E-06 lst E E-06 lst E E-06 lst E E-06 lst E E-06 lst E E-07 lst E E-06 lst E E-06 lst E E-06 lst E E-06 lst E E-06 lst E E-07 lst E E-06 Average 9.85E E-06 Average 1.90E E-06 Standard deviation 1.39E E-06 Standard deviation 4.54E E-06 F-27
56 Limestone 3/8 Angularity Texture Limestone #4 Angularity Texture lst E E-07 lst E E-06 lst E E-06 lst E E-06 lst E E-06 lst E E-06 lst E E-06 lst E E-06 lst E E-06 lst E E-05 lst E E-07 lst E E-05 lst E E-06 lst E E-06 lst E E-06 lst E E-06 lst E E-06 lst E E-04 lst E E-06 lst E E-05 lst E E-07 lst E E-06 lst E E-06 lst E E-06 lst E E-05 lst E E-06 lst E E-05 lst E E-06 lst E E-07 lst E E-05 lst E E-06 lst E E-05 lst E E-06 lst E E-05 lst E E-05 lst E E-04 lst E E-05 lst E E-04 lst E E-05 lst E E-05 lst E E-04 lst E E-05 lst E E-06 lst E E-04 lst E E-06 lst E E-04 lst E E-05 lst E E-06 lst E E-06 lst E E-05 lst E E-05 lst E E-05 lst E E-06 lst E E-05 lst E E-05 lst E E-05 Average 3.32E E-05 lst E E-03 Standard deviation 6.33E E-05 lst E E-06 lst E E-06 Average 1.25E E-05 Standard deviation 2.06E E-04 F-28
57 Broadway Original Aggregate Angularity Texture B E E-06 B E E-06 B E E-07 B E E-06 B E E-07 Average 8.64E E-06 Standard deviation 8.41E E-06 Broadway after 15 min. of Micro-Deval test Angularity Texture B E E-06 B E E-06 B E E-06 B E E-07 B E E-06 Average 8.94E E-06 Standard deviation 8.17E E-06 Broadway after 45 min. of Micro-Deval test Angularity Texture B E E-05 B E E-05 B E E-05 B E E-06 B E E-06 Average 1.27E E-05 Standard deviation 9.10E E-06 F-29
58 Maymead Original Aggregates Angularity Texture M E E-06 M E E-06 M E E-06 M E E-06 M E E-05 Average 1.58E E-06 Standard deviation 2.70E E-06 Maymead after 15 min. of Micro-Deval test Angularity Texture M E E-05 M E E-06 M E E-06 M E E-06 M E E-05 Average 1.17E E-06 Standard deviation 1.43E E-06 Maymead after 45 min. of Micro-Deval test Angularity Texture M E E-05 M E E-06 M E E-06 M E E-05 M E E-05 Average 2.86E E-05 Standard deviation 3.93E E-05 F-30
59 Salem Original Aggregates Angularity Texture S E E-06 S E E-06 S E E-06 S E E-06 S E E-07 Average 8.94E E-06 Standard deviation 7.63E E-06 Salem after 15 min. of Micro-Deval test Angularity Texture S E E-06 S E E-06 S E E-06 S E E-06 S E E-06 Average 7.82E E-06 Standard deviation 2.96E E-06 Salem after 45 min. of Micro-Deval test Angularity Texture S E E-06 S E E-06 S E E-06 S E E-06 S E E-06 Average 9.91E E-06 Standard deviation 8.02E E-06 F-31
60 Strasburg Original Aggregates Angularity Texture ST E E-06 ST E E-06 ST E E-06 ST E E-06 ST E E-05 Average 1.02E E-06 Standard deviation 9.01E E-06 Strasburg after 15 min. of Micro-Deval test Angularity Texture ST E E-06 ST E E-06 ST E E-06 ST E E-06 ST E E-06 Average 9.26E E-06 Standard deviation 1.06E E-06 Strasburg after 45 min. of Micro-Deval test Angularity Texture ST E E-06 ST E E-06 ST E E-06 ST E E-06 ST E E-06 Average 9.73E E-06 Standard deviation 1.44E E-06 F-32
61 Appendix G Histogram and Normal Quantile Plot of Aggregates in Set 1 Appendix G shows the normal quantile plots using the statistical software JMP for seven types of aggregates in aggregate set 1. An original value does not give a good fit over the normal curve. Data should be normal to be able to use the ANOVA test and t-test in statistical analysis. All the data in the plots are transformed, and the square root of the original value is taken. After the transformation, deviation is minimized at both ends. In these plots, the abbreviations are as follows: BFS = Blast furnace slag; CO = Copper ore; DLT = Dolomite; GGC = Glacial gravel crushed; GGR = Glacial gravel rounded; IO = Iron ore; LST = Limestone. G-1
62 G-2
63 G-3
64 G-4
65 G-5
66 G-6
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