Quality in the Concrete Paving Process · Quality in the Concrete Paving Process II - 39 Module 8-...

Quality in the Concrete Paving Process

Module 2 Module 8 - 1


Workshop Introduction


Module 8- 2

Instructors

Dennis Dvorak

Jim Grove

Mike Praul

FHWA Resource Center

(708) 283-3542

[email protected]

ATI Inc. / FHWA

Office of Infrastructure, Washington D.C. (515) 294-5988

[email protected]

FHWA Maine Division

(207) 512-4917

[email protected]

mailto:[email protected]




Module 8- 3

Agenda Workshop Introduction

Module 1: Quality Assurance Concepts

Module 2: Concrete Materials

Module 3: Concrete Properties and Testing

Module 4: QC and Agency Acceptance

Module 5: Pre-Paving and Mix Production

Module 6: Paving

Module 7: Utilizing Quality Concepts

Module 8: Quality in Field Practice


Module 8- 4

What is this Workshop about?

Common Sense Tools


Module 8- 5

Goal: Plant Seeds

What do you do today? What are you striving to accomplish? What should the agency test and inspect? What should the contractor test and inspect? How should we accept concrete pavement? What new tools and technologies can you use

to build better pavements?


Module 8- 6

Utilizing Quality Concepts


Module 8- 7

Learning Objectives

By the end of this session, you will be able to • Describe how PWL is used to assess quality

• Understand the impact of variability

• Understand the basics of control charts

• Understand the information provided by a heat signature plot

• Recognize the time savings by using the maturity for opening strength


Module 8- 8

Normal Distribution

Properly obtained statistical sample for an entire lot of most construction material will form a Normal Distribution Curve


Module 8- 9

68 – 95 – 99.7 Rule

This Empirical Rule states that: • 68% of all possible samples are clustered about

the mean within ± 1 standard deviation • 95.5% are within ± 2 standard deviations • 99.7% are within ± 3 standard

deviations

–1Sn 1S

n 2S

n –2S

n 3S

n –3Sn

68 95.5

99.7

x


Module 8- 10

Testing Targets and Limits Based on Normal Distribution

Specifications normally identify targets and/or limits for individual quality characteristics

Specification limits should be based on the principle of normal distribution

6.5 5.5 7.0 5.0 8.0 7.5 6.0

Lower Spec Limit

Upper Spec Limit

Target

Air Content (%)


Module 8- 11

Standard Deviation

Sample standard deviation (s)

s =

∑(Xi - X)2

n - 1


Module 8- 12

PWL

Using the sample data, with its mean and standard deviation, we can determine the quality level of the sample


Module 8- 13

PWL

Estimates the percentage of material within specification limits • Assumes normal distribution • Area equals 1.0 or 100%

1.0

or 100%


Module 8- 14

PWL

Efficiently captures mean and standard deviation in one quality measure

X 3s 2s 1s -3s -2s -1s

X - mean

s - standard deviation


Module 8- 15

Single Specification PWL

LSL

PWLL

or X

USL

PWLU

X


Module 8- 16

Double Specification PWL

- 100 = PWLL

LSL

USL

PWLU

X

USL

LSL

PWL

+


Module 8- 17

Estimating PWL in Four Steps

1. Obtain random samples 2. Compute

• Mean ( x ) • Standard deviation (s)

3. Compute quality index (Q) 4. Convert Q to “estimated” PWL


Module 8- 18

3s 2s s -3s -2s -s

Compute Q

QL = X – LSL

s

USL - X

s QU =

LSL

USL

X

QL QU


Module 8- 19

QL → PWLL

Convert Q to PWL

Lookup estimated PWL from Q for a specified number of samples (n)

X 3s 2s 1s -3s -2s -1s

PWLL

LSL


Module 8- 20

Convert Q to PWL

Lookup estimated PWL from Q for a specified number of samples (n)

X 3s 2s 1s -3s -2s -1s

QU → PWLU PWLU USL


Module 8- 21

Convert Q to PWL

+ - 100 = PWLL

LSL

USL

PWLU

USL

X

LSL

PWL


Module 8- 22

Approaches to Using Pay Factors

Quality Assurance specifications typically use a pay factor formula to determine pay factors

Under each approach, the pay factors are directly related to the PWL


Module 8- 23

Using PWL to Compute Pay Factors

Pay Factors: • Recoup losses expected from

poor quality work • Reward increased performance

from increases in product consistency


Module 8- 24

Pay Factor Formula

A pay factor formula presents a mathematical equation that typically derives a linear schedule of pay from the PWL

AASHTO provides a recommended equation:

Pay Factor (PF) = 0.55 + 0.5 (PWL) (PWL is expressed as a decimal value in this equation)


Module 8- 25

Maine Example

Data analysis of materials testing from 1978-1998

Per cent “passing” tests ranged from 87%-92% every year

Confirms that our industry, without incentives, operates around 90 PWL


Module 8- 26

Why Incentives?

Motivate contractors to improve quality • Fairness • Positive approach • Factor into bidding

Differentiate contractors that produce “desirable” and “undesirable” quality work

When incentives are included, they should be sufficient to encourage contractor innovation


Module 8- 27

Payment Plan with 5% Incentive

Estimated PWL

AQL PF=0.5PWL + 55 Pay F

acto

r (%

)

RQL

Payment Plan with 5% Incentive


Module 8- 28

Pay

Incentive Pay

Disincentive

AQL=90

100 95 105

90 80 100

Pay factors

Estimated PWL

Payment Plan with Incentive


Module 8- 29

AQL

Estimated PWL

AQL PF=0.5PWL + 55 Pay F

acto

r (%

)

PF=100

RQL

Payment Plan without Incentive


Module 8- 30

Pay

Disincentive

AQL=90

90 80 100

100 95 105

Pay factors

No

Incentive

Estimated PWL



Module 8- 31

Pay

Disincentive

AQL=90

90 80 100

100 95 105

Pay factors

98

Estimated PWL



Module 8- 32

Sources of Variability


Module 8- 33

Validity of Sampling Data

Required for Statistical analysis:

1. “Multiple” (n > 3) samples are used 2. All samples are “Randomly” obtained 3. Material is produced and samples are obtained

under “Controlled Conditions”


Module 8- 34 II - 34

How well does this individual sample result

represent the probable air content for every

material sample from the entire lot of concrete?

A technician randomly obtains one sample of concrete from a 50 yard lot and determines the air content of this sample.


Module 8- 35

Air Content (%)

6.5 5.5 7.0 5.0 8.0 7.5 6.0

Lower

Spec

Limit

Upper

Spec

Limit

Target



The same technician now randomly obtains two separate samples of n = 5 from the same 50 yard lot of concrete and determines the air content of each sample.

Which of the two samples is more

representative of the probable air content of

the entire lot of concrete??


Module 8- 37

Air Content (%) Sample # 1

6.5 5.5 7.0 5.0 8.0 7.5 6.0

Lower

Spec

Limit

Upper

Spec

Limit

Target


Module 8- 38

6.5 5.5 7.0 5.0 8.0 7.5 6.0

Lower

Spec

Limit

Upper

Spec

Limit

Target

Air Content (%) Sample # 2



One way to obtain a more complete picture of the true variability of the air content of the 50 yard lot of concrete would be to test one sample from each of the 50 yards (creating 50 sublots)

Based upon the previous plots, what would you

project as the result of an n=50 sample size?

How does your projection compare with the actual

results?


Module 8- 40

True Variability

6.5 5.5 7.0 5.0 8.0 7.5 6.0

Lower

Spec

Limit

Upper

Spec

Limit

Target

Air Content (%)


Module 8- 41

True Variability

6.5 5.5 7.0 5.0 8.0 7.5 6.0

Lower

Spec

Limit

Upper

Spec

Limit

Target

Air Content (%)


Module 8- 42

True Variability

6.5 5.5 7.0 5.0 8.0 7.5 6.0

Lower

Spec

Limit

Upper

Spec

Limit

Target

Air Content (%)


Module 8- 43

Testing Variability

Inherent in the procedures and apparatus

Influenced by the technicians


Module 8- 44

Testing Variability

Procedure 95% Lower Limit Test Result 95% Upper Limit

Sieve analysis (% passing ½”) 24% 28% 32%

Slump 2” 2 ½” 3”

Air content 4.9% 5.5% 6.1%

Rodded unit weight for aggregate

114.5 lb/ft3 120 lb/ft3 125.5 lb/ft3

Compressive strength 3,390 lb/in2 3,600 lb/in2 3,810 lb/in2

Flexural strength 602 lb/in2 700 lb/in2 798 lb/in2


Module 8- 45

Air Content Testing Example

Specified air content – 5.0% to 6.5%

Two tests from the same wheelbarrow of concrete

• Contractor test result – 4.9%

• Agency test result – 5.5%

What is the air content?

What would happen if the

test results are reversed?


Module 8- 46


Two testers conducted air content testing

Used concrete from same wheel barrow

Consistent pattern of 0.5% difference in test results


Module 8- 47


Tester 1

Meter 1 => 4.5%

Meter 2 => 4.5%

Tester 2

Meter 2 => 5.0%

Meter 1 => 5.0%


Module 8- 48


Tester 1

Plate =>5.2%

Bar => 5.9%

Tester 2

Plate => 5.1%

Bar => 6.1%


Module 8- 49

Statistical Process Control (SPC) Monitor QC measurements and react

• Concentrate on identifying change

• Do not focus on specification limits

• Changes in materials and/or processes (unusual test results)

4.50

5.00

5.50

6.00

6.50

7.00

7.50

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Air

Co

nte

nt (%

)

Test #

Air Content


Module 8- 50

Chance Causes vs. Assignable Causes

Construction materials are subject to a certain degree of variability, stemming from two primary sources: Chance cause –

A source of variation that is inherent in any production process and which cannot be eliminated as it is due to random, expected causes.

Assignable cause – An identifiable, specific cause of variation in a given process or measurement. A cause of variation that is not random and does not occur by chance.


Module 8- 51

Control Chart Basics Control charts are process and contractor specific Average of test results plotted as the centerline Upper and lower control limits

• Usually plotted at 3 times the standard deviation (3s) of representative test data

• Define the limits of chance cause variability

137.0

138.0

139.0

140.0

141.0

142.0

143.0

144.0

145.0

146.0

1.1 1.2 1.3 1.4 1.5 2.1 2.2 2.3 2.4 2.5 2.6 3.1 3.2 3.3 3.4 3.5 3.6 4.1 4.2 4.3 4.4 4.5 5.1 5.2 5.3 5.4 5.5

Uni

t Wei

ght

(lb/

ft3)

Sample ID

Unit Weight Control Chart

Unit Weight

Target

Average

Upper Control Limit (3s)

Lower Control Limit (-3s)


Module 8- 52

Control Chart Basics

3s limits are not specification limits!

Reflect the voice of the process so that we can identify assignable cause variability

137.0

138.0

139.0

140.0

141.0

142.0

143.0

144.0

145.0

146.0

1.1 1.2 1.3 1.4 1.5 2.1 2.2 2.3 2.4 2.5 2.6 3.1 3.2 3.3 3.4 3.5 3.6 4.1 4.2 4.3 4.4 4.5 5.1 5.2 5.3 5.4 5.5

Uni

t Wei

ght

(lb/

ft3)

Sample ID


Unit Weight

Target

Average




Module 8- 53

Control Chart Basics Revise 3s limits when process changes result in

changes to the chance cause variability • 3s limits that are too tight chasing phantom

assignable cause variability

• 3s limits that are too loose mask assignable cause variability

137.0

138.0

139.0

140.0

141.0

142.0

143.0

144.0

145.0

146.0

1.1 1.2 1.3 1.4 1.5 2.1 2.2 2.3 2.4 2.5 2.6 3.1 3.2 3.3 3.4 3.5 3.6 4.1 4.2 4.3 4.4 4.5 5.1 5.2 5.3 5.4 5.5

Uni

t Wei

ght

(lb/

ft3)

Sample ID


Unit Weight

Target

Average




Module 8- 54


Recognizing assignable cause variability

A. One test result is outside of the 3s limits

140.0

141.0

142.0

143.0

144.0

145.0

146.0

147.0

148.0

149.0

150.0

1-1

1-2

1-3

1-4

1-5

1-6

2-1

2-2

2-3

2-4

2-5

2-6

3-1

3-2

3-3

3-4

3-5

3-6

4-1

4-2

4-3

4-4

4-5

4-6

5-1

5-2

5-3

5-4

Un

it W

eig

ht

(lb

/ft3

)

Sample ID

Example Control Chart

Unit Weight

Target

Average




Module 8- 55



B. Six consecutive test results are all increasing or decreasing

140.0

141.0

142.0

143.0

144.0

145.0

146.0

147.0

148.0

149.0

150.0

1-1

1-2

1-3

1-4

1-5

1-6

2-1

2-2

2-3

2-4

2-5

2-6

3-1

3-2

3-3

3-4

3-5

3-6

4-1

4-2

4-3

4-4

4-5

4-6

5-1

5-2

5-3

5-4

Un

it W

eig

ht

(lb

/ft3

)

Sample ID


Unit Weight

Target

Average




Module 8- 56



C. Nine consecutive test results are on the same side of the average value

140.0

141.0

142.0

143.0

144.0

145.0

146.0

147.0

148.0

149.0

150.0

1-1

1-2

1-3

1-4

1-5

1-6

2-1

2-2

2-3

2-4

2-5

2-6

3-1

3-2

3-3

3-4

3-5

3-6

4-1

4-2

4-3

4-4

4-5

4-6

5-1

5-2

5-3

5-4

Un

it W

eig

ht

(lb

/ft3

)

Sample ID


Unit Weight

Target

Average




Module 8- 57



D. Fourteen consecutive test results are alternating up and down

140.0

141.0

142.0

143.0

144.0

145.0

146.0

147.0

148.0

149.0

150.0

1-1

1-2

1-3

1-4

1-5

1-6

2-1

2-2

2-3

2-4

2-5

2-6

3-1

3-2

3-3

3-4

3-5

3-6

4-1

4-2

4-3

4-4

4-5

4-6

5-1

5-2

5-3

5-4

Un

it W

eig

ht

(lb

/ft3

)

Sample ID


Unit Weight

Target

Average




Module 8- 58

Control Charts

Control charts do not

• Eliminate variability

• Tell you where your problem lies

• Tell you how to correct the problem

Some control charts

• Help distinguish between the inherent chance causes of variability and assignable causes


Module 8- 59

Limits

Air Content Ahead of the paver

Remove

Remove

Disincentive

Disincentive Specification Limit

Engineering Limit

Action Limit


Module 8- 60

Dual Axis Plot Example

Air content plotted on the left vertical axis Plot unit weight on the right vertical axis

136

137

138

139

140

141

142

143

144

145

1463.5

4.0

4.5

5.0

5.5

6.0

6.5

7.0

7.5

8.0

8.5

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Un

it W

eig

ht (l

b/f

t3)

Air

Co

nte

nt (%

)

Test #

Air Content and Unit Weight

air content ahead of paver unit weight


Module 8- 61

Dual Axis Plot Example Unit weight data varies as expected with the air

content

Except for test #20

136.0

137.0

138.0

139.0

140.0

141.0

142.0

143.0

144.0

145.0

146.03.5

4.0

4.5

5.0

5.5

6.0

6.5

7.0

7.5

8.0

8.5

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Un

it W

eig

ht (l

b/f

t3)

Air

Co

nte

nt (%

)

Test #

Air Content and Unit Weight

air content ahead of paver unit weight



Quality in Field Practice


Module 8- 63

Data from Arizona DOT Project Mobile Concrete Lab

• Tested from paving on L303 section between Thomas to Camelback

• Testing from July 9 to July 19 • This Module presents test data • Review principles discussed yesterday and this

morning

Agency Acceptance Goal • Does the concrete match the mix design?

QC Goal • Has the concrete changed?


Module 8- 64

Concrete Mixture Mixture Design

• Cement Type II (low alkali), Class F Fly Ash • Night time paving • High temperatures (chillers to cool concrete)

Total Cementitious Content: 564lbs

Fly Ash: 20%


Module 8- 65

Data from an Example Project

Date Sample Day Sample IDs

9/24/2013 1 1-1, 1-2, 1-3, 1-4

9/25/2013 2 2-1, 2-2, 2-3, 2-4, 2-5

9/26/2013 3 3-1, 3-2, 3-3, 3-4, 3-5

9/27/2013 4 4-1, 4-2, 4-3, 4-4

9/28/2013 5 5-1, 5-2, 5-3


Module 8- 66

Gradation

Optimized combined gradation • Workability • Durability • Dimensional stability


Module 8- 67

Shilstone Coarseness Factor Chart

20

30

40

50

020406080100

Wo

rkab

ilit

y F

acto

r

Coarseness Factor

I - segregation

II - ideal

V - rocky mixes

III - MSA<0.5"

IV - too many fines

Workability


Module 8- 68

0.45 Power Chart

¾”½”3/8”#4#8#30 1"#200

0.0

1.0

0

10

20

30

40

50

60

70

80

90

100

0.0 4.0

Com

bin

ed P

erc

ent

Passin

g (

%)

Sieve Size Raised to 0.45 Power

0.45 Power Chart

Maximum Density Line

Combined % Passing

No. 57

Sand

Density


Module 8- 69

Percent of Aggregate Retained on Each Sieve

0

5

10

15

20

25

30

35

2" 1 1/2" 1" 3/4" 1/2" 3/8" 4 8 16 30 50 100 200

Co

mb

ined

% R

eta

ined

Sieve Size

8-18 Chart

Gaps in the gradation


Module 8- 70


Workability

Note: From one sample tested by FDOT


Module 8- 71

0.45 Power Chart

Density


Module 8- 72




Module 8- 73


Workability 20

30

40

50

020406080100

Work

abili

ty F

acto

r

Coarseness Factor

I - segregationII - ideal

V - rocky mixes

III - MSA<0.5"

IV - too many fines


Module 8- 74

0.45 Power Chart

Density

¾”½”3/8”#4#8#30 1"#200

0.0

1.0

0

10

20

30

40

50

60

70

80

90

100

0.0 4.0

Com

bin

ed P

erc

ent

Passin

g (

%)

Sieve Size Raised to 0.45 Power

0.45 Power Chart

Maximum Density Line

Combined % Passing


Module 8- 75



0

5

10

15

20

25

30

35

2" 11/2"

1" 3/4" 1/2" 3/8" 4 8 16 30 50 100 200

Co

mb

ine

d %

Re

tain

ed

Sieve Size

8-18 Chart


Module 8- 76

Monitor Concrete Consistency

Look for changes Use different tests to help identify the source

of the change • Water • Air • Temperature • Cementitious

Establish limits of change


Module 8- 77

Slump

Measure of consistency Quick and easy test Very consistent concrete

0

1

2

3

4

5

1-1 1-2 2-1 2-2 3-1 3-2 3-3 3-4 4-1 4-2 5-1 5-2

Slu

mp

, in

Slump Average Slump Max Slump


Module 8- 78

Air content • Target air content • Limited Variability observed • Total air is different from the air void

system

Air Content

2%

3%

4%

5%

6%

7%

8%

1-1 1-2 2-1 2-2 3-1 3-2 3-3 3-4 4-1 4-2 5-1 5-2

Air

Co

nte

nt,

%

Sample ID

Air Content

Upper Limit

Average Air Content


Module 8- 79

Unit Weight Variability

• Uniformity o Batching tolerances

• Air and water content • Simple and easy to run

143

144

145

146

147

148

149

150

151

1-1 1-2 2-1 2-2 3-1 3-2 3-3 3-4 4-1 4-2 5-2

Un

it W

eig

ht,

pcf

Unit Weight, pcf Average Lower Limit Upper Limit


Module 8- 80

Air/Unit Weight Normally they will run parallel

• Unit weight changes if air content changes • Unit weight changes if water (slump) changes

When they diverge • Change in materials or proportions

2%

3%

4%

5%144

145

146

147

148

149

150

1-1 1-2 2-1 2-2 3-1 3-2 3-3 3-4 4-1 4-2 5-2

Air

Co

nte

nt,

%

Un

it W

eig

ht,

pcf

Sample ID

Unit Weight

Air Content


Module 8- 81

MCL Observations

Air content • Variability observed

Section 1 Section 2


Module 8- 82

MCL Observations

Unit weight • Variability observed

Section 2 Section 1


Module 8- 83

MCL Observations

Air content and unit weight • Vary together • Unit weight changes appear to be from air content

only (likely changes in water)


Module 8- 84

Slump

Measure of consistency Quick and easy Used for QC

Slump

Measurements at

the plant

0

1/2

1

1 1/2

2

2 1/2

0 7 16 24 34

Slu

mp

, In

che

s

Time, Minutes


Module 8- 85

Slump Measure of consistency Quick and easy Used for QC

Slump

Measurements at

the plant

0

1/2

1

1 1/2

2

2 1/2

3

0 7 12 20 29 36

Slu

mp

, In

che

s

Time, Minutes


Module 8- 86

3%

4%

5%

6%

7%

8%

1-1 1-2 1-3 2-1 2-2 2-3 2-4 2-5 3-1 3-2 3-3

Air

Co

nte

nt,

%

Sample ID

Air content • Target air content • Total air is different from the air void system

Air Content

Average

Air Content


Module 8- 87

136

138

140

142

144

146

1-1 1-2 1-3 2-1 2-2 2-3 2-4 2-5 3-1 3-2 3-3

Un

it W

eig

ht,

pcf

Unit Weight

Variability • Uniformity

o Batching tolerances-Normally within 3 lbs ±

• Air Content and Water content • Design Unit Weight

Average Unit Weight


Module 8- 88

Air/Unit Weight

Normally they will run parallel • Unit weight changes if air content changes • Unit weight changes if water (slump) changes

When they diverge • Change in materials or proportions

3%

4%

5%

6%

7%

8%

9%137

138

139

140

141

142

143

1-1 1-2 1-3 2-1 2-2 2-3 2-4 2-5 3-1 3-2 3-3

Air

Co

nte

nt,

%

Un

it W

eig

ht,

pcf

Sample ID

Unit Weight

Air Content


Module 8- 89

Unit Weight vs. Air Content Before Paver

Contractor QC Data




Module 8- 91

0.40

0.45

0.50

1-1 1-3 2-1 2-3 2-5

Wat

er

Ce

nm

en

titi

ou

s R

atio

Sample ID

Microwave Water Content

Mix Design Target

Average w/cm ratio


Module 8- 92

0.30

0.35

0.40

0.45

0.50

1-2 2-1 3-1 4-3Wat

er

Ce

nm

en

titi

ou

s R

atio

W/Cm Ratio Mix Design Target

Microwave Water Content

Mix Design Target


Module 8- 93

Concrete Temperature

75

80

85

90

95

100

1-1 1-2 2-1 2-2 3-1 3-2 3-3 3-4 4-1 4-2 5-1 5-2

Tem

pe

ratu

re,

F

Sample ID

Concrete Temp, F

Upper Limit (ConcTemp)

Air Temp, F

Is the concrete below the specified upper limit?

Affects hydration rate • Workability • Compatibility


Module 8- 94

Heat Signature

Cementitious system

Strength development

• Time of set

• Sawing window

Incompatibilities

Uniformity

Shift relates to initial temperature

Peak Heat of Hydration

•Saw cutting started-9hrs

•Commonly 2± hrs around the

peak


Module 8- 95

Heat Signature

Peak: About 13 hours


Module 8- 96

Heat Signature Sample Day 1 & 4 Separated by Sample

Days

AM PM


Module 8- 97

Heat Signature TOLLWAY Data

FRAP Mix: 1331

8/30/13

(Different Source of Fly Ash)

PV Mixture

(No Slag) FRAP Mix: 1320

8/23/13

FRAP Mix: 1320

8/27/13


Module 8- 98

Hardened Concrete Properties

Strength • Not a QC test • Not real time

Permeability • Tremendous affect on the life of the pavement

CTE • Very important in the pavement design


Module 8- 99

Compressive Strength Contractors Data Concrete or testing variability?

2500

3000

3500

4000

4500

5000

5500

6000

6500

7000

1 2 3 4 5 6 7 8 9 10111213141516171819202122232425

Co

mp

ress

ive

Str

en

gth

, P

SI

Sample ID

28 Day 28 Day Strength Requirement Average

143

144

145

146

147

148

149

150

151

1-1 1-2 2-1 2-2 3-1 3-2 3-3 3-4 4-1 4-2 5-2

Un

it W

eig

ht,

pcf

Unit Weight, pcf Average Lower Limit Upper Limit


Module 8- 100

Compressive Strength

Does air content match strength changes?

2000

3000

4000

5000

6000

7000

8000

1-1(5/14/13)

2-1(5/15/13)

3-1(5/16/13)

4-1(5/20/13)

Co

mp

ress

ive

Str

en

gth

, P

SI

7 Day 28 Day 56 Day

28 Day StrengthRequirement

5.2% 5.1% 6.0% 5.0%

Air

Content


Module 8- 101

2000

2500

3000

3500

4000

4500

5000

5500

6000

6500

7000

1-1 2-1 3-1

Co

mp

ress

ive

Str

en

gth

, P

SI

Sample ID


56 day

28 day

7 day

28-day Requirement

56 day

28 day

7 day


Module 8- 102

2000

2500

3000

3500

4000

4500

5000

5500

6000

6500

7000

1-1 2-1 3-1 4-1

Co

mp

ress

ive

Str

en

gth

, P

SI

Sample ID


Minimum

Compressive

Strength

requirement at

28 Days

56 day

28 day

7 day

28 day

56 day

7 day


Module 8- 103

Compressive Strength Would less strength actually be better?

0

1000

2000

3000

4000

5000

6000

7000

8000

0 7 Day 28 Day 56 Day

Co

mp

ress

ive

Str

en

gth

, P

SI

Sample Age

1-1 (5/14/13)

2-1 (5/15/13)

3-1 (5/16/13)

4-1 (5/20/13)

28 Day StrengthRequirement


Module 8- 104


y = 0.0036x + 128.29R² = 0.7682

y = 0.003x + 127.97R² = 0.893

141142143144145146147148149

3500 4500 5500 6500 7500 8500

Un

it W

eig

ht,

pcf

Compressive Strength, psi

y = -1E-05x + 0.1142R² = 0.6272

y = -8E-06x + 0.1005R² = 0.4239

4.0%

4.5%

5.0%

5.5%

6.0%

6.5%

3500 4500 5500 6500 7500 8500

Air

Co

nte

nt,

%


y = -0.0008x + 5.3563R² = 0.9474

y = -0.0006x + 4.9034R² = 0.8317

0.5

0.75

1

1.25

1.5

1.75

2

3500 4500 5500 6500 7500 8500

Slu

mp

, in


7 Day Compressive Strengths

28 Day Compressive Strengths


Module 8- 105

Permeability Surface resistivity Test

• Easy and quick test

Rapid Chloride Permeability Test • Takes more time and effort- old stand by

0.0

4.0

8.0

12.0

16.0

20.0

24.0

1-1-1 1-1-2 2-1-1 2-1-2 3-1-1 3-1-2 3-3-1 3-3-2 3-4-1 3-4-2

Surf

ace

Re

sist

ivit

y, K

Oh

m-c

m

7 Days

28 Days

56 Days

Moderate

High

Low


Module 8- 106




y = 12270x-0.836

R² = 0.7159

0

5

10

15

20

25

30

35

1000 1500 2000 2500 3000 3500 4000 4500

Surf

ace

Re

sist

ivit

y,KO

hm

-cm

RCPT, Columbs

HighModerateLow

Low

Moderate

High


Module 8- 107

Permeability Surface Resistivity Test


Rapid Chloride Penetrability Test • Takes more time and effort- old stand by

0

4

8

12

16

20

24

28

32

36

40

1-1 1-3 1-7 2-6 2-11 3-3 3-5 2-10 PV

Surf

ace

Re

sist

ivit

y, K

Oh

m-c

m

at 14 Days

at 28 Days

at 56 Days

Moderate

High

Low

FRAP Mixture PV Mixture

No Slag in

PV Mixture


Module 8- 108

0

5

10

15

20

25

30

35

40

1-1 1-4 2-1 2-4 3-1 3-3 3-5 4-1 4-3 5-1 5-3

Avg

. Su

rfac

e R

esi

stiv

ity

(kΩ

-cm

)

FHWA7 Day 28 Day 56 Day

Moderate

High

Low

V. Low




Cured in lime water


Module 8- 109

0

5

10

15

20

25

30

35

40

1-1 1-4 2-1 2-4 3-1 3-3 3-5 4-1 4-3

Avg

. Su

rfac

e R

esi

stiv

ity

(kΩ

-cm

)

MDOT28 Day 56 Day

Moderate

High

Low

V. Low




Cured in moist room


Module 8- 110

Heat Signature vs. Surface Resistivity

y = 1351.9e-0.046x

R² = 0.9141

5.0

7.0

9.0

11.0

13.0

15.0

17.0

19.0

21.0

23.0

25.0

90 92 94 96 98 100 102 104 106

56

Day

Su

rfac

e R

esi

stiv

ity,

Oh

m-c

m

Maximum Temperature of the Heat Signature Curve, F


Module 8- 111

Construction Monitoring/Acceptance HIPERPAV

• Assess early age cracking potential

Maturity • Measures real world conditions • For opening strength only • Not 28 day strength

MIT-SCAN-2 • Dowel bar location and alignment

MIT-SCAN-T2 • Pavement thickness


Module 8- 112

HIPERPAV Software tool for assessing cracking risk

Placement on

7/12/13 @ 11:00 p.m..


Module 8- 113

Field Maturity


Module 8- 114

0

1000

2000

3000

4000

5000

0 1000 2000 3000 4000 5000 6000 7000

Co

mp

ress

ive

Str

en

gth

, PSI

Maturity, °C-Hrs

Specimens Cast on7/15/13

Maturity Maturity curve

• Temperature and time factor can be used to determine in-place pavement strength

Opening strength = 3000 psi

+

Pavement age = 7 days

Maturity number = 1600 C-Hrs


Module 8- 115

0

500

1000

1500

2000

2500

0 24 48 72 96 120 144 168 192

Mat

uri

ty (

°C-H

rs)

Time(hrs)

Field (7/13/13)

Field (7/16/13)

Standard Cured Cylinders

Maturity Maturity readings

• Sensors in the maturity and thereby strength for opening pavement determine in-place

Time Saving

Maturity of Pavement

Maturity of Standard

Cured Cylinder

3000

psi


Module 8- 116

MIT-SCAN-2


Module 8- 117

Identification of Problems Dowel bar inserter

MIT SCAN 2 o 25 joints scanned (both lanes) o 6 of 25 joints have no dowels o 3 of the 6 joints had dowels approximately 2 ½’ away

from the joint Tie bar


Module 8- 118

MIT-SCAN-2 27 Joints Tested No alignment issues Center

Line

Edge of

Pavement


Module 8- 119

MIT-SCAN-2

948+35 948+50 948+65 948+80 948+95

Cut Cut Cut


Module 8- 120

MIT-SCAN-2

948+35 948+50 948+70 949+10 949+25

Cut


Module 8- 121

MIT-SCAN-T2


Module 8- 122

MIT-SCAN-T2 Thickness

• Design thickness = 9.0” • Average thickness (T2)= 9.7”

8.08.38.58.89.09.39.59.8

10.010.310.510.811.0

1 2 3 4 5 6 7 8 9 10 11

Pav

em

en

t Th

ickn

ess

, In

che

s

Pavement Thickness Average Thickness Design Thickness


Module 8- 123

MIT-SCAN-T2

y = 0.987x + 0.0608R² = 0.9802

8.5

8.7

8.9

9.1

9.3

9.5

9.7

9.9

10.1

10.3

10.5

8.5 8.7 8.9 9.1 9.3 9.5 9.7 9.9 10.1 10.3 10.5

Scan

T2

me

asu

rem

en

ts, i

nch

es

Core Thickness, inches


Module 8 - 124

Thank you for your attention.

.

Any final comments or questions?

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