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Predicting Future Performance of Improved Soils from Today’s Test Data David A. Saftner, Russell A. Green, & Roman D. Hryciw

Predicting Future Performance of Improved Soils from Today’s Test Data

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Predicting Future Performance of Improved Soils from Today’s Test Data. David A. Saftner , Russell A. Green, & Roman D. Hryciw. Outline. Sand Aging Overview Field Testing Explosive Compaction NEES Vibroseis Testing Laboratory Testing Summary. 10. 15. 20. 5. 0. +4. +2. 0. -2. - PowerPoint PPT Presentation

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Page 1: Predicting Future Performance of Improved Soils from Today’s Test Data

Predicting Future Performance of Improved Soils from Today’s Test

Data

David A. Saftner, Russell A. Green, & Roman D. Hryciw

Page 2: Predicting Future Performance of Improved Soils from Today’s Test Data

Sand Aging Overview Field Testing

Explosive Compaction NEES Vibroseis Testing

Laboratory Testing Summary

Outline

Page 3: Predicting Future Performance of Improved Soils from Today’s Test Data

Sand Aging OverviewPractical Application

+4

+2

0

-2

-4

-6

-8

-10

-12

-14

10 15 2050Le

vel:

mP

DCPT qc (MPa)

Post-vibrocompaction(~2 weeks after)

Pre-vibrocompaction

Post-vibrocompaction(~6 weeks after)

Minimum Allowable qc

(from Debats and Sims 1997)

Page 4: Predicting Future Performance of Improved Soils from Today’s Test Data

Proposed Mechanisms: Mechanical – micro-level particle

rearrangement Chemical – precipitation and

cementation Dissolution of bubbles – blast gas or

air

Biological – microorganisms

Sand Aging Overview

Page 5: Predicting Future Performance of Improved Soils from Today’s Test Data

Methods of dealing with aging: Scheduling time to allow aging to

occur Site specific aging metrics based on

test improvement projects Several proposed relationships:

Sand Aging Overview

Page 6: Predicting Future Performance of Improved Soils from Today’s Test Data

Methods of dealing with aging: Schmertmann et al. (1986)

Based on observations of a dynamic compaction test site prior to main site improvement project

Sand Aging Overview

Time between improvement and CPT (days)

Factor by which to multiply qc

5 1.3510 1.215 1.1520 1.1230 1.0640 1.0350 1.0160 1.00

Page 7: Predicting Future Performance of Improved Soils from Today’s Test Data

Methods of dealing with aging: Mesri et al. (1990)

Sand Aging Overview

cD CCC

RRc

c

tt

qq

/

)(

(qc)R = tip resistance at a reference time after the end of primary consolidation

t = time of aged tip resistance measurementtR = reference time following primary consolidationCD = parameter reflecting densification methodC = secondary compression indexCc = compression index

Page 8: Predicting Future Performance of Improved Soils from Today’s Test Data

Methods of dealing with aging: Charlie et al. (1992)

Sand Aging Overview

)log(*1)()(

_1

_ NKqq

weekc

weeksNc

K = empirical constant based on the chartN = number of weeks since disturbance

Page 9: Predicting Future Performance of Improved Soils from Today’s Test Data

Methods of dealing with aging: Joshi et al. (1995)

Sand Aging Overview

bt taPP )(1

a b

Dry state 0.9 0.06Distilled water 0.75 0.15

Sea water 0.7 0.17

Pt, P1 = penetration resistance on tth and 1st day following disturbance, respectively

t = aging period in daysa, b = constants depending on environmental conditions with average values shown in the table above

Page 10: Predicting Future Performance of Improved Soils from Today’s Test Data

Field Testing

Blast site

Vibroseis site

Paleo-liquefaction features

Page 11: Predicting Future Performance of Improved Soils from Today’s Test Data

Field TestingPaleo-liquefaction feature

Page 12: Predicting Future Performance of Improved Soils from Today’s Test Data

Field Testing

Clay

Loose ~GWTSand

DenseSand

LooseGravellySand

1.5m

3m

5m

10m

14m

Lower Liquefiable Layer

Upper Liquefiable Layer

Page 13: Predicting Future Performance of Improved Soils from Today’s Test Data

Field TestingCone Penetration Test

Friction SleevePressure

Transducer

Vision Cone Camera

Accelerometer

Page 14: Predicting Future Performance of Improved Soils from Today’s Test Data

Explosive Compaction

Page 15: Predicting Future Performance of Improved Soils from Today’s Test Data

Explosive Compaction

20’

CPTPre-BlastOne WeekOne Month2.5 Months

3.5 Months

CPTuSCPTDMT

VisCPT

One Year

A

A’

Page 16: Predicting Future Performance of Improved Soils from Today’s Test Data

1.15m (45.9”)

Explosive Compaction

Clay

Loose ~GWTSand

DenseSand

LooseGravellySand

1.5m

3m

5m

10m

14m

6.1m (20’)

0.1m (4.5”)

0.6m (22.5”)

12m

View A-A’

Page 17: Predicting Future Performance of Improved Soils from Today’s Test Data

Explosive Compaction

Page 18: Predicting Future Performance of Improved Soils from Today’s Test Data

Explosive Compaction0 5 10 15 20 25 30 35 40

0

20

Tip resistance, qc (MPa)

Pre-Blast Range (7 tests)One Week Range (6 tests)2.5 Month Range (3 tests)

Upper Liquefiable Layer

Lower Liquefiable Layer

2

4

6

8

10

12

14

16

18

Dep

th, z

(m)

Page 19: Predicting Future Performance of Improved Soils from Today’s Test Data

Explosive Compaction

Dep

th, z

(m)

One Week Range (6 tests)2.5 Month Range (3 tests)

0 2 4 6 8 10 12 141.5

2

2.5

3

3.5

4

4.5

5

Tip resistance, qc (MPa)

One Week Range (6 tests)2.5 Month Range (3 tests)

Page 20: Predicting Future Performance of Improved Soils from Today’s Test Data

Explosive Compaction0 2 4 6 8 10

1.5

2

2.5

3

3.5

4

4.5

5

Tip resistance, qc (MPa)

One Week Average (6 tests)1 Month Average (3 tests)2.5 Month Average (3 tests)1 Year Average (3 tests)

Dep

th, z

(m)

Page 21: Predicting Future Performance of Improved Soils from Today’s Test Data

Explosive Compaction

100 120 140 160 180 200 2202

2.5

3

3.5

4

4.5

5

5.5

6

6.5

3.5 Hrs16 Hrs1 Week1 Month2.5 Months3.5 Months

Shear Wave Velocity, Vs (m/s)

Dep

th, z

(m)

Page 22: Predicting Future Performance of Improved Soils from Today’s Test Data

NEES Vibroseis

Page 23: Predicting Future Performance of Improved Soils from Today’s Test Data

NEES Vibroseis

CPTPre-BlastOne WeekOne Month9 Months

SCPTDMT

VisCPT

7.5’

Page 24: Predicting Future Performance of Improved Soils from Today’s Test Data

NEES Vibroseis

Page 25: Predicting Future Performance of Improved Soils from Today’s Test Data

NEES Vibroseis

Upper Liquefiable Layer

Dep

th, z

(m)

0 5 10 15 20 25 30 35 400

1

2

3

4

5

6

7

8

9

10

Tip resistance, qc (MPa)

Post-shake range (3 tests)One month range (3 tests)

Upper Liquefiable Layer

Dep

th, z

(m)

Page 26: Predicting Future Performance of Improved Soils from Today’s Test Data

NEES Vibroseis0 5 10 15 20 25 30 35 40

0

1

2

3

4

5

6

7

8

9

10

Tip resistance, qc (MPa)

Pre-shake average (4 tests)Post-shake average (3 tests)One month average (3 tests)One year average (3 tests)

Upper Liquefiable Layer

Dep

th, z

(m)

Page 27: Predicting Future Performance of Improved Soils from Today’s Test Data

Laboratory Testing

0.010.11100

10

20

30

40

50

60

70

80

90

100

Grain Size Distribution in Upper and Lower Liquefiable Layers

upper liquefiable layer (depth = 4m)

lower liquefiable layer (depth = 11m)

Diameter (mm)

% P

assi

ng

Page 28: Predicting Future Performance of Improved Soils from Today’s Test Data

Laboratory Testing

Page 29: Predicting Future Performance of Improved Soils from Today’s Test Data

Sand aging is important because of dependence on in-situ testing when developing QA metrics

Following explosive compaction, CPT qc and Vs showed time-dependent increases

Following vibroseis shaking, Vs showed slight time-dependent increases but little change to CPT qc

Summary

Page 30: Predicting Future Performance of Improved Soils from Today’s Test Data

Comparison of several field disturbance techniques and laboratory testing performed on the same site/soil is unique in aging literature

Synergistic laboratory/field components of this research will allow development of a metric that predicts future in-situ test results using today’s data

Summary

Page 31: Predicting Future Performance of Improved Soils from Today’s Test Data

EERI, FEMA, & NEHRP NSF & NEES Professors Jerry Lynch, Richard

Woods & Kyle Rollins Jan Pantolin & Yongsub Jung Mulzer Crushed Stone, Inc Spartan Specialties, Ltd

Acknowledgements