SAMPLING AND TESTING CHIP & SLURRY SEAL AGGREGATES Conference/Greg_Wilkinson... · SAMPLING AND...

Greg Wilkinson

Representative, LEED AP BD+C

Graniterock

SAMPLING AND TESTING CHIP & SLURRY SEAL AGGREGATES

QUALITY IS…

Accuracy Consistency

ACCURACY + CONSISTENCY = QUALITY

•  The process is accurate if it does not contain any bias or systematic error.

•  The process is consistent if the standard deviation is small and the control limits are narrow.

•  Control Charts are used to measure accuracy and consistency of a process over time.

Monitoring Accuracy and Consistency with Control Charts

The process is accurate if it does not contain any bias or systematic error. Systematic error leads to values that are consistently higher or lower than the target value (66.15%)

Monitoring Accuracy and Consistency with Control Charts

The process is consistent if the standard deviation is small and the control limits are narrow.

A smaller standard deviation will bring the control limits closer together. A larger standard deviation will bring the control limits farther apart.

AGGREGATE PROPERTIES •  Particle Size Distribution (Gradation) •  CT Coarse/Medium/Medium Fine & Fine Screenings •  CT Type I, II and III Slurry Seal Aggregates

Aggregate should be capable of meeting gradation

specifications consistently. Densely graded slurry seal aggregates and single-sized screenings are preferred.

PARTICLE SIZE DISTRIBUTION (GRADATION)

OBSERVATIONS…

•  Segregation of the sample can result in inaccurate gradation measurements. Sources of segregation are:

1.  Large stockpiles 2.  Improper sample size reduction (splitting) 3.  Biased Sampling

GRADATION SPC 20 GRADINGS 3/4" 1/2" 3/8" #4 #8

Average Value 100 96 67 8 2.5Standard Deviation 0 2 6 3 0.6High Value 100 99 81 15 3.5Low Value 100 92 56 5 0.6

SPECIFICATIONS SPC Charts

1/2 x #4 ASTM GRCO LCL-UCL

C-33 Spec LCL UCL

3/4" 100 100 100 1001/2" 90-100 90-100 93 993/8" 40-70 47-70 52 77#4 0-15 0-15 1 12#8 0-5 0-5 0.9 4.0

ADVANCED SPC JMP EVALUATION

12

14

16

18

20

22

24

% P

ass

#4

01/2

0/20

08

02/2

0/20

08

03/2

0/20

08

04/2

0/20

08

05/2

0/20

08

06/2

0/20

08

07/2

0/20

08

08/2

0/20

09

09/2

0/20

08

Date

Avg=17.56

LCL=12.71

UCL=22.40

Individual Measurement of % Pass #4

LSL USL

-3s +3sMean

10 20 30

Sigma = 1.61606

CPCPKCPMCPLCPU

Capability2.5782.567

.2.5902.567

Index1.7201.690

.1.7131.697

Lower CI3.4363.443

.3.4643.433

Upper CI

Below LSLAbove USLTotal Outside

Portion0.00000.00000.0000

Percent0.00000.00000.0000

PPM9.2689.2019.141

SigmaQuality

Control Chart Sigma

0

5

10

15

20

25

30

35

LSL

USL

.01 .05.10 .25 .50 .75 .90.95 .99

-3 -2 -1 0 1 2 3

Normal Quantile Plot

SPLIT ONLINE CAMERA SYSTEM

REAL-TIME PROCESS CONTROL WINSPC SOFTWARE

BENEFITS OF REAL-TIME PROCESS CONTROL

•  Operators see the effects of plant configurations and changes

•  Best Practices can be developed to reduce variability and improve compliance with specifications.

AGGREGATE PROPERTIES •  Cleanness •  Exceed Minimum CV Value •  Exceed Minimum SE Value

Screenings that are not clean may not adhere to the asphalt. Slurry seal aggregates that are not clean may absorb

excessive amounts of emulsion.

CLEANNESS VALUE VIDEO

SAND EQUIVALENT CALCULATION

⎟⎟⎠

⎞⎜⎜⎝

⎛=

clayofheightsandofheight

equivalentsand 100

OBSERVATIONS…

•  Sand Equivalent results can be biased by particle shape and gradation.

AGGREGATE PROPERTIES •  Durability and Abrasion Resistance •  Parent aggregate of screenings should not exceed maximum

loss when tested in the L.A. Rattler •  Slurry seal aggregate shall meet minimum durability index

requirements

Abrasion and low durability cause the generation of unwanted and unexpected fines, which impacts gradation and

cleanness. Chipping and fracturing are also symptoms of low durability and abrasion resistance.

DIFFERENCE BETWEEN SAND EQUIVALENT AND DURABILITY INDEX TEST

Sand Equivalent

•  Sample is not washed

•  Sample mixed for 45 seconds

Durability Index (fine)

•  Sample is washed

•  Sample is abraded for 10 minutes

SAMPLE

•  When a small number of individuals of from a population are selected and studied to collect information that is used to draw conclusions about the whole population, this collection of individuals is called a sample.

Bias: Systematic favoritism that is present in the data collection process, resulting in lopsided, misleading results

A GOOD SAMPLE

THE BEST LABORATORY AND BEST TECHNICIANS CANNOT COMPENSATE FOR A

BAD SAMPLE. ALL OF THE TIME SPENT TESTING IS A WASTE IF THE SAMPLE DOES

NOT REPRESENT THE MATERIAL THAT EXISTS OR WILL BE USED.

A GOOD SAMPLE IS A SAMPLE THAT IS REPRESENTATIVE OF THE MATERIAL.

EXAMPLES OF BIAS

•  A salesman has delivered a bag of material from the jobsite that was sampled by the contractor, who reports that the sample is contaminated with over-sized particles.

•  A technician visits a rock quarry every morning at 6:30 am to collect her daily sample. The quarry operators expect her visit every morning at this time, and make sure that the plant is running under ideal conditions.

SAMPLE LOCATIONS

SEGREGATION IN STOCKPILES

SMALL STOCKPILE SAMPLING VIDEO