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Introduction to Risk Assessment
in Geotechnical Engineering
Prof. Samirsinh P Parmar
Mail: samirddu@gmail.com
Asst. Professor, Department of Civil Engineering,
Faculty of Technology,
Dharmsinh Desai University, Nadiad-387001
Gujarat, INDIA
Content of the presentation
• Prediction from Symposium
• In situ field testing layout
• Load test set-up
• Methods used for prediction
• Predicted design load
• Why predictions went wrong???
• Design methods, factor of safety, tolerance limit etc. factors.
• Reference books for geotechnical investigations.
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
2
Prediction Symposium at TAMU (1994)
 5 square footings ranging in size 1-3 m were constructed
and loaded to 0.15 m of displacement at Texas A&M
University National Geotechnical Experimentation Site
(NGES).
 150 requests received and 31 participants from 9 countries
finally sent their predictions
 Prediction results were:
•Qdesign load=Min(Q25(predicted), Q150(predicted)/3)
•Factor of Safety (FS)=Qfailure load (=Q150)/Qdesign load
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
3
Footing 1
Footing 3 Footing 1
Footing 4 Footing 5
1.5 m
1.5 m
3 m
3 m
3 m
3 m
2 m
2 m
1 m
1 m
1 m 1 m
1 m 1 m
Drilled
shaft
Drilled
shaft
Drilled
shaft
Drilled
shaft
Drilled
shaft
8.5 m
4.25 m
8.5 m
Prediction Symposium at TAMU (1994)
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
4
As-built Sizes of Footings
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
5
In-situ Field Testing
Layout with Auger
Hole Location
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
6
Load Test Setup
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
7
Methods Used for Prediction
Soil Tests Used
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
8
Predicted Loads at 25 and 150 mm Settlements
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
9
Settlement Predictions
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
10
Measured Data
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
11
Prediction of Loads Producing 25 mm settlement (3 m footing)
North footing South footing
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
12
Prediction of Loads Producing 150 mm settlement (3 m footing)
North footing South footing
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
13
Factor of Safety
Predicted Design Load
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
14
Settlement Under
Design Load
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
15
Evaluation of Prediction Methods – For Bearing Capacity
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
16
Evaluation of Prediction Methods – For Settlements
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
17
Choosing the Best Prediction Method
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
18
Prediction Results
For 1 m footing:
• Predicted/Measured load for 25 mm settlement:
Range=0.069-1.294, mean=0.71, Stdev=0.3, measured
value=1
• Predicted/Measured load for 150 mm settlement:
Range=0.115-2.279, mean=0.65, Stdev=0.45, measured
value=1
• Predicted design load: Range=59-1100 kN, Mean=377 kN,
Stdev=229, Measured value=580 kN
• Range of FS predicted = 2.23-29.49, Mean FS = 6.64,
standard deviation of FS=5.23, and measured FS=3
• Settlement under design load: Range of prediction= 0.5-48
mm, Mean=6.1 mm, Stdev=9.5 mm, and measured value=9.5
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
19
Imperial college prediction competition
• Performance of a single driven steel pile
• 34-page information on the ground conditions and as-
constructed pile operations is provided to all participants
• Predictions were judged against extensive field testing results
• 16 predictions from 7 countries – 9 consultants & 4 contractors
• Entrants were asked to provide the total load capacity of pile,
shaft and base capacity components, load-settlement curve
• Unsurprisingly wide scatter of results and conforms that the
capacity of piles can be predicted reliably only to about plus or
minus 60%.
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
20
Footing Settlement Prediction Competition
• School of Civil and Resource Engineering, the University of
Western Australia
(http://www.civil.uwa.edu.au/about/research/research_areas/foo
ting_settlement_prediction_competition)
• Industry professionals are invited to provide footing settlement
predictions for four instrumented footings that have recently
been load tested at a well characterised sand site in Perth,
Australia.
• Participants are asked to predict:
 The settlement of each footing under a load of 100 kN and 180 kN. The
footings were incrementally loaded to 200kN within approximately two
hours, and creep was small during holding periods.
 The settlement at a depth of B/2 below each footing for the same loads
previously mentioned. (where B is the footing width)
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
21
Footing
Number
Width,B
(m)
Thickne
ss
(m)
Embedme
nt (m)
1 1.5 1.0 1.0
2 1.0 1.0 1.0
3 1.0 1.0 0.5
4 0.67 1.0 1.0
Footing 1
Footing 2
Footing 3
Footing 4
• Centre-centre spacing between footings =3m
• Seismic/standard cone penetration test &
dilatometer test performed within 0.5m of the
side of each footing
Footing geometry & in-situ test locations
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
22
Why the predictions went wrong?
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
23
Attributed to one or all??
Natural variability
Irrelevant soil properties
•Errors in field testing
Location of drilled shafts, number of drilled shafts, method of testing
•Errors in laboratory testing
Equipment, instrumentation
Collection of samples in disturbed/undisturbed state
Simulating field conditions
•Statistical errors due to limited sampling
Errors with using simplified transformation models, ex. Mohr
circles, bearing capacity, settlement,
Human errors, ex. lacking the understanding of nature and
applying the basic principles of science to solve engineering
problems
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
24
Factor of safety – a better tool to measure safety??
Effect of variability on design of shallow foundations (Lacasse 2001)
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
25
Site-specific landslide Hazard
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
26
Site-specific landslide Hazard
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
27
A wrong methodology??
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
28
Need for a better design methodology
• High construction costs
• Increased costs of consequence
• rapid urbanization and
• public awareness
 draw the attention of practicing engineers and researchers to
safer, economical and reliable designs
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
29
Basic Design Principle
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
30
Effect of Wrong Predictions (Uncertainty)
• Uncertainty in analysis and design
Recognized significant and varying degrees of uncertainty
inherently involved in the design process
Allowance must be made for these uncertainties in the
designs
Traditional approaches simplify the problem by considering
the uncertain parameters to be deterministic
 use lumped factors of safety (empirical, based on past experience) to
account for the uncertainties propagating in the design decisions
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
31
Ancient way of design
Designs by experience
worked well because changes in construction methods and
materials were at that time very slow (Allen, 1994)
Industrial revolution soon changed the approach based on
experience because materials and construction methodologies
started to change rapidly
To accommodate these rapid changes, civil engineers had to develop more rational
design procedures
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
32
 The basis of the design is to ensure that throughout the structure, when it is
subjected to the ‘working’ or service applied load, the induced stresses are
less than the allowable stresses
 No proper study and implementation of effects of individual sources of
uncertainty
 No proper quantification of safety or risk involved
 Effect of uncertainties arriving from number of disparate sources on the
output is lumped together and a single value of FS is used to account for all
the uncertainties, based on judgment of designer, with no distinction made as
to whether it is applied to material strength and resistances or to load effects
Conventional Analysis
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
33
FS values selected for design reflect past experience and the
consequence of failure
The more serious the consequence of failure or the higher the
uncertainty, the higher the factor of safety
Suggesting the factor of safety without knowing the sources
and amount of uncertainty involved in the evaluation of
design parameters bears the resemblance such as suggesting
the presumptive bearing capacity without having proper
knowledge of the soil shear strength parameters.
Conventional Analysis
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
34
Motivation for an alternative design approach
Comparable
variations in
material
properties!!
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
35
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
36
Motivation
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
37
Motivation
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
38
Factor of safety??
Probability density function of FS=R/S
Practical implications of effect of variability on design of
shallow foundations (Lacasse 2001)
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
39
Does it appropriate to neglect such
high degree of soil property
variations associated with mean
design parameter???
Practical implications of effect of
variability on design of shallow
foundations (Lacasse 2001)
Motivation
Probability density function of FS=R/S
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
40
Identified its importance way back in 1960
Study of individual sources of uncertainty
Rational and unified framework for quantitative analysis of uncertainty
and assessment of risk
Uniform distribution of safety levels among the different components
Useful for formulation of trade-off studies for
 Planning
 Design , and
 Decision making, considering the economical aspects of the study
Engineers are reluctant due to unavailability of a large set of sample data
and mathematical sophistication
Probabilistic approach
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
41
Tolerable
Risks
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
42
Tolerable Risk
‘Risks that people (or society) are willing to accept in
specific situations or from natural and man-made
works’
Tolerable risk refers to a willingness to live with a risk
so as to secure certain benefits and in the confidence
that risk is being properly controlled or managed.
Designs must ensure an acceptable risk or a required
level of safety.
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
43
Levels of acceptable risk, MacGregor (1976):
Avoidable risks connected with daring people: 10-3 per year
Avoidable risks connected with careful people: 10-4 per year
Unavoidable risks: 5 x 10-5 per year
Structural collapse is an unavoidable risk (so, design pf = 5 x 10-5 per
year)
This level of risk corresponds to the target reliability index used in the
calibration of the National Building Code of Canada (NBCC) for
structural design
Tolerable Risk
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
44
Reference books
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
47
Reference books
M.Tech.
Sem-1,
DoCL,
DDU,
Nadiad,
Gujarat,
India.
48
Risk Assessment in Geotechnical Engineering .pptx

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  • 1. Introduction to Risk Assessment in Geotechnical Engineering Prof. Samirsinh P Parmar Mail: samirddu@gmail.com Asst. Professor, Department of Civil Engineering, Faculty of Technology, Dharmsinh Desai University, Nadiad-387001 Gujarat, INDIA
  • 2. Content of the presentation • Prediction from Symposium • In situ field testing layout • Load test set-up • Methods used for prediction • Predicted design load • Why predictions went wrong??? • Design methods, factor of safety, tolerance limit etc. factors. • Reference books for geotechnical investigations. M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 2
  • 3. Prediction Symposium at TAMU (1994)  5 square footings ranging in size 1-3 m were constructed and loaded to 0.15 m of displacement at Texas A&M University National Geotechnical Experimentation Site (NGES).  150 requests received and 31 participants from 9 countries finally sent their predictions  Prediction results were: •Qdesign load=Min(Q25(predicted), Q150(predicted)/3) •Factor of Safety (FS)=Qfailure load (=Q150)/Qdesign load M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 3
  • 4. Footing 1 Footing 3 Footing 1 Footing 4 Footing 5 1.5 m 1.5 m 3 m 3 m 3 m 3 m 2 m 2 m 1 m 1 m 1 m 1 m 1 m 1 m Drilled shaft Drilled shaft Drilled shaft Drilled shaft Drilled shaft 8.5 m 4.25 m 8.5 m Prediction Symposium at TAMU (1994) M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 4
  • 5. As-built Sizes of Footings M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 5
  • 6. In-situ Field Testing Layout with Auger Hole Location M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 6
  • 8. Methods Used for Prediction Soil Tests Used M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 8
  • 9. Predicted Loads at 25 and 150 mm Settlements M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 9
  • 12. Prediction of Loads Producing 25 mm settlement (3 m footing) North footing South footing M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 12
  • 13. Prediction of Loads Producing 150 mm settlement (3 m footing) North footing South footing M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 13
  • 14. Factor of Safety Predicted Design Load M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 14
  • 16. Evaluation of Prediction Methods – For Bearing Capacity M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 16
  • 17. Evaluation of Prediction Methods – For Settlements M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 17
  • 18. Choosing the Best Prediction Method M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 18
  • 19. Prediction Results For 1 m footing: • Predicted/Measured load for 25 mm settlement: Range=0.069-1.294, mean=0.71, Stdev=0.3, measured value=1 • Predicted/Measured load for 150 mm settlement: Range=0.115-2.279, mean=0.65, Stdev=0.45, measured value=1 • Predicted design load: Range=59-1100 kN, Mean=377 kN, Stdev=229, Measured value=580 kN • Range of FS predicted = 2.23-29.49, Mean FS = 6.64, standard deviation of FS=5.23, and measured FS=3 • Settlement under design load: Range of prediction= 0.5-48 mm, Mean=6.1 mm, Stdev=9.5 mm, and measured value=9.5 M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 19
  • 20. Imperial college prediction competition • Performance of a single driven steel pile • 34-page information on the ground conditions and as- constructed pile operations is provided to all participants • Predictions were judged against extensive field testing results • 16 predictions from 7 countries – 9 consultants & 4 contractors • Entrants were asked to provide the total load capacity of pile, shaft and base capacity components, load-settlement curve • Unsurprisingly wide scatter of results and conforms that the capacity of piles can be predicted reliably only to about plus or minus 60%. M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 20
  • 21. Footing Settlement Prediction Competition • School of Civil and Resource Engineering, the University of Western Australia (http://www.civil.uwa.edu.au/about/research/research_areas/foo ting_settlement_prediction_competition) • Industry professionals are invited to provide footing settlement predictions for four instrumented footings that have recently been load tested at a well characterised sand site in Perth, Australia. • Participants are asked to predict:  The settlement of each footing under a load of 100 kN and 180 kN. The footings were incrementally loaded to 200kN within approximately two hours, and creep was small during holding periods.  The settlement at a depth of B/2 below each footing for the same loads previously mentioned. (where B is the footing width) M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 21
  • 22. Footing Number Width,B (m) Thickne ss (m) Embedme nt (m) 1 1.5 1.0 1.0 2 1.0 1.0 1.0 3 1.0 1.0 0.5 4 0.67 1.0 1.0 Footing 1 Footing 2 Footing 3 Footing 4 • Centre-centre spacing between footings =3m • Seismic/standard cone penetration test & dilatometer test performed within 0.5m of the side of each footing Footing geometry & in-situ test locations M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 22
  • 23. Why the predictions went wrong? M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 23
  • 24. Attributed to one or all?? Natural variability Irrelevant soil properties •Errors in field testing Location of drilled shafts, number of drilled shafts, method of testing •Errors in laboratory testing Equipment, instrumentation Collection of samples in disturbed/undisturbed state Simulating field conditions •Statistical errors due to limited sampling Errors with using simplified transformation models, ex. Mohr circles, bearing capacity, settlement, Human errors, ex. lacking the understanding of nature and applying the basic principles of science to solve engineering problems M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 24
  • 25. Factor of safety – a better tool to measure safety?? Effect of variability on design of shallow foundations (Lacasse 2001) M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 25
  • 29. Need for a better design methodology • High construction costs • Increased costs of consequence • rapid urbanization and • public awareness  draw the attention of practicing engineers and researchers to safer, economical and reliable designs M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 29
  • 31. Effect of Wrong Predictions (Uncertainty) • Uncertainty in analysis and design Recognized significant and varying degrees of uncertainty inherently involved in the design process Allowance must be made for these uncertainties in the designs Traditional approaches simplify the problem by considering the uncertain parameters to be deterministic  use lumped factors of safety (empirical, based on past experience) to account for the uncertainties propagating in the design decisions M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 31
  • 32. Ancient way of design Designs by experience worked well because changes in construction methods and materials were at that time very slow (Allen, 1994) Industrial revolution soon changed the approach based on experience because materials and construction methodologies started to change rapidly To accommodate these rapid changes, civil engineers had to develop more rational design procedures M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 32
  • 33.  The basis of the design is to ensure that throughout the structure, when it is subjected to the ‘working’ or service applied load, the induced stresses are less than the allowable stresses  No proper study and implementation of effects of individual sources of uncertainty  No proper quantification of safety or risk involved  Effect of uncertainties arriving from number of disparate sources on the output is lumped together and a single value of FS is used to account for all the uncertainties, based on judgment of designer, with no distinction made as to whether it is applied to material strength and resistances or to load effects Conventional Analysis M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 33
  • 34. FS values selected for design reflect past experience and the consequence of failure The more serious the consequence of failure or the higher the uncertainty, the higher the factor of safety Suggesting the factor of safety without knowing the sources and amount of uncertainty involved in the evaluation of design parameters bears the resemblance such as suggesting the presumptive bearing capacity without having proper knowledge of the soil shear strength parameters. Conventional Analysis M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 34
  • 35. Motivation for an alternative design approach Comparable variations in material properties!! M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 35
  • 39. Factor of safety?? Probability density function of FS=R/S Practical implications of effect of variability on design of shallow foundations (Lacasse 2001) M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 39
  • 40. Does it appropriate to neglect such high degree of soil property variations associated with mean design parameter??? Practical implications of effect of variability on design of shallow foundations (Lacasse 2001) Motivation Probability density function of FS=R/S M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 40
  • 41. Identified its importance way back in 1960 Study of individual sources of uncertainty Rational and unified framework for quantitative analysis of uncertainty and assessment of risk Uniform distribution of safety levels among the different components Useful for formulation of trade-off studies for  Planning  Design , and  Decision making, considering the economical aspects of the study Engineers are reluctant due to unavailability of a large set of sample data and mathematical sophistication Probabilistic approach M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 41
  • 43. Tolerable Risk ‘Risks that people (or society) are willing to accept in specific situations or from natural and man-made works’ Tolerable risk refers to a willingness to live with a risk so as to secure certain benefits and in the confidence that risk is being properly controlled or managed. Designs must ensure an acceptable risk or a required level of safety. M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 43
  • 44. Levels of acceptable risk, MacGregor (1976): Avoidable risks connected with daring people: 10-3 per year Avoidable risks connected with careful people: 10-4 per year Unavoidable risks: 5 x 10-5 per year Structural collapse is an unavoidable risk (so, design pf = 5 x 10-5 per year) This level of risk corresponds to the target reliability index used in the calibration of the National Building Code of Canada (NBCC) for structural design Tolerable Risk M.Tech. Sem-1, DoCL, DDU, Nadiad, Gujarat, India. 44