Aggregate Testing 101 James Cox – Cemex Jennifer Hanley – Gilson Company

2. Aggregate Testing 101James Cox – CemexJennifer Hanley – Gilson Company PRESENTER’S LOGO

3. An introduction to Aggregate Testing Procedures • Sampling, reduction of field sample • Moisture content, gradation, -#200, Finus Modulus • Specific gravity & absorption • Los Angeles (LA) Abrasion • A discussion of how best to store your data, monitor variability, evaluate performance using Process Behavior Measurement (SPC), predict future results and/or the need for change in the production process.

4. Aggregate Testing Procedures • DOT-Department of Transportation • ASTM-American Society of Testing and Materials • AASHTO-American Association of State Highway Transportation Officials

5. Maximum & Nominal Size

6. It All Starts with Sampling • Properly trained personnel • Develop a plan agreeable by all parties • Use power equipment if you have it • Field sample size is important ASTM D 75, AASHTO T 2

7. Minimum Field Sample Size • Based on Nominal Maximum size: • 4 Stone 1 ½” 75lb • 57 Stone 1” 50lb • 67 Stone ¾” 25lb • 89 Stone 3/8” 10lb • Fine Agg -#4mesh 10lb ASTM D 75, AASHTO T 2

8. Reducing Field Sample to Testing Size • Split down to obtain required minimum test sample size ASTM C 702, AASHTO R 76

9. Gradation • Fine aggregate • Most particles smaller than #4 (4.75mm) • Coarse Aggregate • Most particles larger than #4 (2.75mm) • Mixed/Dense graded aggregate • Combination of fine & coarse, base courses

10. Sieve Analysis of Fine & Coarse Aggregate ASTM C 136, AASHTO T 127

11. Sieve Analysis of Fine & Coarse Aggregate ASTM E 11 • Why some sieves in inches and others #? • Sieve sizes are based on dimensions of the mesh size opening or the number of openings per linear inch

12. Sieve Analysis of Fine & Coarse Aggregate

13. Sieve Analysis

14. Sieve Analysis

15. Sieve Analysis Ind % Retained = 100 X Mass Retained ÷ Dry Mass

16. Sieve Analysis Add up successive Ind % Retained to obtain Cumulative % Retained

17. Sieve Analysis % Passing = 100 – Cumulative % Retained

18. Sieve Analysis

19. Sieve Analysis Cumulative % Retained Individual % Retained Cumulative % Passing

20. Total Moisture Content • Weigh wet sample • Dry to constant mass @ 110 +/- 5C, 230 +/- 9F • Weigh dry sample % moisture = 100 × (Wet Wt. – Dry Wt.) ÷ Dry Wt. = 100 x Weight of Water ÷ Dry Wt. ASTM C 566 , AASHTO T 255

21. Total Moisture Content % moisture = 100 x Weight of Water ÷ Dry Wt. Air Water Solids

22. Materials Finer than the 75µm (No 200) Sieve in Mineral Aggregates by Washing ASTM C 117, AASHTO T 11 • Minimum sample size: (based on Nominal Maximum size) • 1 ½” 5000g • ¾” 2500g • 3/8” 1000g • #4mesh 500g • #8mesh 100g

23. Materials Finer than the 75µm (No 200) Sieve in Mineral Aggregates by Washing ASTM C 117, AASHTO T 11

24. Materials Finer than the 75µm (No 200) Sieve in Mineral Aggregates by Washing ASTM C 117, AASHTO T 11 %-#200 = [(original dry mass – dry mass after washing) ÷ original dry mass] X 100 %-#200 = (loss ÷ original dry mass) X 100

25. Finus Modulus (FM) • Index which indicates fineness • Sum of cumulative % retained on specified sieves divided by 100 Specified Sieves 3/8 #4 8 16 30 50 100 FM = 242.2 ÷ 100 = 2.42

26. Specific Gravity & Absorption Fine ASTM C 128, AASHTO T 84 Coarse ASTM C 127, AASHTO T 85 • Coarse • Fine Ratio of the density of one material to a reference material

27. Specific Gravity & Absorption • Coarse

28. Specific Gravity & Absorption • Coarse

29. Specific Gravity & Absorption Oven Dry • Dry Bulk Specific Gravity oven dry wt. SSD wt. – SSD in H2O wt. • Saturated Surface Dry Specific Gravity SSD wt. SSD wt. – SSD in H2O wt. SSD

30. Specific Gravity & Absorption • Fine SSD

31. Specific Gravity & Absorption • Absorption, % SSD wt. - oven dry wt. oven dry wt.

32. Los Angeles Abrasion • Specified grading of sample is placed into the apparatus along with a charge of steel spheres and rotated a specified # of times ASTM C 131, AASHTO T 96

33. Los Angeles Abrasion

34. Los Angeles Abrasion % Loss = 100 X (wt. original sample – wt. retained on #12 sieve) wt. original sample % Loss = 100 X (material passing the #12 sieve) wt. original sample

35. Comparison

36. Collecting, Analyzing & Storing Data • Data validation upon entry • Realtime, share across network • Ease of calculations • Less errors • Standardized worksheets • Rapid analysis • Automation of reporting & alerts

37. Data Validation

38. Feed Back

39. Analyzing Data • Mean, average (X) • Standard Deviation, square root of the variance

40. Standard Deviation

41. Standard Deviation

42. Standard Deviation

43. Standard Deviation Essentially the standard deviation is the average distance of each measurement from the mean of all those measurements

44. Standard Deviation

45. Process Behavior Measurement Mean Mean

46. Process Behavior Measurement

47. Process Behavior Measurement Elbow room Spec., Voice of the Customer

48. QC “There is a very abrupt change in the production # 57 samples today … gave us a signal on the process behavior chart. Did we do anything to cause this change yesterday after plant shut down?” That’s a signal!

49. Maintenance “We found E6X20 T/D # 1,2 screens with 3/4" instead of 1-1/8". Now is back to 1-1/8" p/p screens.”

50. QC “You don’t have to know my plant you just have to listen to the signals” A missed signal is an wasted opportunity!