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PI: Erol Tutumluer RA: Tongyan Pan

Testing of OMP Aggregates for Shape Properties. O’HARE Airport Modernization Research Project. PI: Erol Tutumluer RA: Tongyan Pan. Introduction.

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PI: Erol Tutumluer RA: Tongyan Pan

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  1. Testing of OMP Aggregates for Shape Properties O’HARE Airport Modernization Research Project PI: Erol Tutumluer RA: Tongyan Pan

  2. Introduction • Aggregates make up more than 85% of Portland cement concrete and 90% of asphalt pavements of which coarse aggregate constitute the skeleton and occupies by far the highest weight or volume • Coarse aggregate are believed to significantly affect strength, stability and deformation properties, and therefore, field performances • This project provides testing and analysis to establish an improved aggregate selection and evaluation for O’Hare airport pavements

  3. Shape Properties of An Aggregate Particle Angularity Key Physical Shape Propertiesof an aggregate particle Shape or Form Surface Texture • Surface Area Roughness or irregularity at a micro level in contrast with angularity at a macro level

  4. Introduction (cont’d) • Aggregate shape factors, such as flatness and elongation, angularity, surface texture and surface area influence pavement behavior and performance • Aggregate shape has been related to permanent deformation and fatigue/cracking resistance of the pavement • Based on current knowledge & past experience: • Equi-dimensional preferred over Flat & Elongated (F&E) • Crushed (angular) preferred over rounded • Rougher surface textured preferred over smooth • Larger specific surface area preferred for better bonding/binding with Portland cement/asphalt cement

  5. Research Objectives • Quantify shape, texture, angularity, and surface area properties of the coarse aggregates to be used by OMP in the various layers of new runway, taxiway, and shoulder pavements • Provide OMP with an aggregate shape property database to efficiently rank and utilize the sources of aggregate stockpiles according to shape properties available to them • Establish a means to develop improved and adaptable Portland cement concrete and asphalt mixture design methods and specifications that can accommodate aggregates with a wide range of physical characteristics and aggregate blending alternatives

  6. Project Tasks Task 1: Collect information on the types, geologic origins, and quarry sources of the coarse aggregate designated for use by OMP • This information is crucial for identifying the approved aggregate sources and collecting aggregate bag samples for imaging based shape analysis • Subsequently, the acquisition of aggregate bag samples will be facilitated through OMP

  7. Project Tasks Task 2: Test OMP coarse aggregates using the UIAIA automated procedure to quantify 3-D shape, size, angularity, surface texture and surface area and define proper imaging based morphological indices (i) maximum, intermediate, and minimum dimensions; (ii) flat and elongated (F&E) ratio; (iii) volume (and weight knowing its specific gravity); (iv) a computed Angularity Index (AI) to indicate how many crushed faces are there or how rounded or angular the particle is; (v) a computed Surface Texture (ST) Index to indicate how smooth or rough the aggregate particle surface is; and finally, (vi) a computed Surface Area (SA)

  8. Advanced Transportation Research & Engineering Laboratory (ATREL) - University of Illinois:

  9. University of Illinois Aggregate Image Analyzer - UIAIA • Developed with funding from FHWA & IDOT • Used in various State and Federal pooled fund studies [TPF-5(023)]for evaluating coarse aggregate & linking aggregate shape to pavement performance • In 2005, selected by the NCHRP 4-30 study as one of the 2 promising aggregate image analysis systems • The only system to use three-orthogonally positioned cameras to capture 3-D shape properties

  10. University of Illinois Aggregate Image Analyzer - UIAIA • Conveyor speed of 3 in./second • Particles placed 10 in. apart • Images captured within • 0.1 second in succession Progressive Scan Video Camera Fiber Optic Motion Sensor

  11. National • Instruments • Labview • IMAQ Vision Image Acquisition by Virtual Instrument (VI)

  12. TOP VIEW SIDE VIEW FRONT VIEW TOP VIEW SIDE VIEW FRONT VIEW Image Acquisition by UIAIA Three Orthogonal Views • Capture three orthogonal views of a particle • Top • Front • Side

  13. Main Features • Image Capturing – 1000 particles in 70 minutes • Image Processing / Analysis –2 to 3 sec./particle • Volume Calculation • Flat and Elongated Ratio • Particle Size Distribution (Gradation) • Angularity • Surface Texture • Surface Area

  14. x z y max Y max Z X max Perform Volume Calculation • Determine if a given pixel in the XYZ space appears in all three orthogonal views • Count number of such pixels (“cubic pixels”) • Estimate “cubic pixel or voxel” volume • Convert to volume units through calibration

  15. Coarse Aggregate Shape and Size Indicesfrom Imaging Using UIAIA, developed imaging based shape indices to quantify size and the three key physical shapeproperties of a coarse aggregate particle • Flat and Elongated (F&E) Ratio • Gradation • Angularity Index (AI) • Surface Texture (ST) Index • Surface Area (SA)

  16. Imaging Based Flat & Elongated (F&E) Ratio F&E Ratio= Longest dimension / Shortest dimension Plane of Longestand Shortest Dimensions Shortest dimension, Perpendicular to the Longest Dimension Longest Dimension All 3 views are analyzed for the longest and shortest dimensions

  17. F&E Ratio Repeatability– Limestone Sample 62A F & E Ratio Category Test Method < 3:1 > 3:1 & < 5:1 > 5:1 4.3 Manual UI 56.7 39.0 Manual IDOT 61.5 36.1 2.5 WipShape 66.5 31.4 2.1 GA Tech 72.2 27.5 0.3 UIAIA trial 1 63.4 34.2 2.4 UIAIA trial 2 62.2 34.9 2.9 UIAIA trial 3 62.1 35.5 2.4

  18. Sieve Analysis • Concept — “If two orthogonal dimensions of a particle are greater than a given sieve, then the particle is retained on it” • The intermediate dimension controls the sieve size on which the particle is retained square opening Di = intermediate dimension < diagonal opening Di

  19. 19.0 12.5 9.5 4.75 2.36 1.18 0.6 0.3 0.15 0.075 25.0 Gradation Repeatability – Limestone Sample 67

  20. 4 3 a 3 2 a 2 n = 1 a 1 a n=24 n n-1 Imaging Based Angularity Index (AI) • Extract coordinates of the outline • Approximate the particle to a n-sided polygon • Compute angle at vertices,a1,a2,..an • Determine change in angle at each vertex,b1,b2,..bn • Obtain frequency distribution ofb1,b2,..bn n = no. of sampling points = 24 Angle at Vertex ‘1’= a1 ….. Angle at Vertex ‘n’= an a1-a2 = b1 a1-a2 = b2 ……….. an-a1 = bn

  21. e=170 Angularity, A = S e*P(e) / n e=0 A1*Area1 + A2*Area2 + A3*Area3 Angularity Index = AI = (Area1 + Area2 + Area3) Imaging Based Angularity Index (AI) Frequency Distribution of b -values For, e = 0, 10, 20, 30….170 for Class Interval 0-10, 10-20, 20-30….170-180 Frequency 0-10 170-180 10-20 Theoretically, A = 0 for a circle Class Interval

  22. Crushed Stone Gravel Blend AI for Gravel, Crushed Stone, & Blend 60 50 40 30 Percentage particles by weight 20 10 0 More than 750 0-75 75-150 150-225 225-300 300-375 375-450 450-525 525-600 600-675 AI with n=24

  23. Erosion Dilation Area A1 Area A2 Imaging Based Surface Texture (ST) Index • Erosion cycles followed by the same number of dilation cycles change the original image • The rougher the particle, the less close the rebuilt image is to the original Image Processing Technique: Erosion and Dilation

  24. A1 - A2 = ST 100 * A1 Area(top) Area(front) + ST(top) Area(side) ST(side) ST(front) + × × × STparticle = Area(front) + Area(top) + Area(side) After A2 Before A1 Imaging Based Surface Texture (ST) Index A1 = Area (in pixels) of the 2-D projection of the particle in the image A2= Area (in pixels) of the particle after performing a sequence of “n” cycles of erosion followed by “n” cycles of dilation

  25. Samples Used In ST Index Development & Calibration Rough Crushed Stone Smooth Gravel Limestone Approximately 100 particles each

  26. ST for Gravel vs Limestone Average ST for Gravel = 0.47 6 Average ST for Limestone = 1.57 5 4 3 Surface Texture Index 2 1 0 0 30 60 90 120 150 180 Max Intercept or Feret Dimension L (pixels)

  27. Aggregate Type Angularity Index (AI) Surface Texture (ST) Index Range Mean Range Mean Uncrushed Gravel 250-350 300 0.5-1.20 0.90 Crushed Gravel 300-450 400 1.00-1.50 1.20 Crushed Limestone 400-550 475 1.20-1.80 1.60 Crushed Granite 500-650 550 1.80-2.90 2.20 Typical Ranges of Angularity and Surface Texture Indices

  28. Z (a, b, c) Particle Domain (0, b, c) c (a, 0, c) ò òòò = dA dxdydz b O Y Particle Surface a (a, b, 0) pixel X pixel pixel Surface Area (SA) Computation Summation of the 2-D ∆Si’contained invoxels forming the particle surface gives the surface area of the particle in units of voxels (pixel cuboids)

  29. In the smallest containing rectangular box, • Searching for the voxels: • 1) Belonging to agg. Particle • Intensity of three • projection pixels • 2) On particle surface • Six surrounding voxels Central Voxel to be judged (b) (a) Surface Area (SA) Computation

  30. Project Tasks Task 3: Establish a shape property database for the OMP approved aggregate sources for use in pavement construction and provide recommendations regarding preferred source • Property variations reported by the various UIAIA imaging based indices will be linked to laboratory and field performances of Portland cement concrete and asphalt layers for establishing possible correlations

  31. Performances of Aggregate Materials in Pavement Structural Layers Unbound Aggregate Base Resilient Response

  32. 47 46 45 44 43 42 41 40 0 100 200 300 400 500 Performances of Aggregate Materials in Pavement Structural Layers Shear Properties Ruttingis an aggregate performance indicator controlled by shear strength Material Mean AI f (deg) C (psi) Crushed Stone 436 46.2 15.9 Gravel 322 40.7 12.7 Crushed Stone Blend 200 41.4 17.2 F (degrees) 50-50 Blend Gravel Angularity Index

  33. Performances of Aggregate Materials in Pavement Structural Layers Asphalt Concrete Mix Rutting Performances – NCAT Test Track

  34. 5000 5000 -0.3441x 0.0027x y = 3589.1e y = 628.95e 4500 4500 2 2 R = 0.0234 R = 0.0141 4000 4000 3500 3500 3000 3000 Resilient Modulus (MPa) Resilient Modulus (MPa) 2500 2500 0.5731x y = 910.84e 0.0052x y = 213.32e 2 R = 0.6009 2000 2000 2 R = 0.7408 1500 1500 1000 1000 Gradation TRZ Gradation TRZ Gradation BRZ Gradation BRZ 500 500 Fit (Gradation TRZ) Fit (Gradation TRZ) Fit (Gradation BRZ) Fit (Gradation BRZ) 0 0 1.00 1.50 2.00 2.50 3.00 350 450 550 650 Composite ST Index Composite AI Performances of Aggregate Materials in Pavement Structural Layers Resilient Response of Asphalt Mix Specimens

  35. Performances of Aggregate Materials in Pavement Structural Layers Early Age Cracking of Portland Cement Concrete 12-hour Fracture Energy

  36. Project Tasks Task 4: Evaluate impact of aggregate shape properties on the performances of constructed pavement layers by utilizing the image analyzed coarse aggregate with cataloged shape properties • Such evaluations will require establishing cracking and rutting performance records of the pavement layers to investigate possible linkages between imaging based coarse aggregate shape indices and the pavement performances

  37. Project Deliverables • Technical Notes will be prepared and submitted to the OMP throughout the duration of this project to communicate specific findings and recommendations to OMP engineers as needed • A Technical Report will be prepared at the end of the laboratory study to document results and findings of the experimental work • The Project Tasks will be pursued in the listed order, and the specific delivery of results will be contingent upon availability of OMP aggregate samples and other factors that depend on coordination with OMP

  38. Significance of the Project • Provide OMP with • improved aggregate selection criteria • optimized aggregate resource utilization • construction cost reductions • Identification and quantification of the influence of aggregate properties on end-use performance to identify aggregate issues & optimize performance linked to acceptable limits of aggregate • shape • angularity • texture • surface area

  39. Any Questions?

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