Allowing for Uncertainty in Site Response Analysis Dr Jason Dowling Dept of Civil Engineering The...
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Allowing for Uncertainty in Site Response Analysis Dr Jason Dowling Dept of Civil Engineering The University of British Columbia The 5 th Tongji-UBC Symposium on Earthquake Engineering “Facing Earthquake Challenges Together” May 4-8 2015, Tongji University Shanghai, China
Allowing for Uncertainty in Site Response Analysis Dr Jason Dowling Dept of Civil Engineering The University of British Columbia The 5 th Tongji-UBC Symposium
Allowing for Uncertainty in Site Response Analysis Dr Jason
Dowling Dept of Civil Engineering The University of British
Columbia The 5 th Tongji-UBC Symposium on Earthquake Engineering
Facing Earthquake Challenges Together May 4-8 2015, Tongji
University Shanghai, China
Slide 2
Overview -Site Information & Motivation for the Study
-Geotechnical Data -Variation of Input Properties -Site Response
Analysis Allowing For Uncertainty -Analysis Results May 4 th, 2015
2/27
Slide 3
May 4 th, 2015 Site Response Analysis The propagation of
seismic waves as they travel through the local soil stratigraphy to
the surface The key components: Input Motions and Soil Properties
3/27
Slide 4
May 4 th, 2015 Site Information Three schools sites in
Richmond, BC British Columbia 4/27
Slide 5
May 4 th, 2015 Site Information Three schools sites in
Richmond, BC 5/27
Slide 6
May 4 th, 2015 Site Information The geological formation of the
Fraser Delta As a result, there can be a considerable depth to
bedrock in areas of the delta Source: Clague et al. (1998)
6/27
Slide 7
May 4 th, 2015 Site Information Geological cross-section from
Burrard Inlet, BC to Bellingham, WN 7/27
Slide 8
8/27 May 4 th, 2015 Site Information Geological cross-section
from Burrard Inlet, BC to Bellingham, WN
Slide 9
May 4 th, 2015 Site Information Geological cross-section from
Burrard Inlet, BC to Bellingham, WN Source: Clague et al. (1998)
9/27
Slide 10
May 4 th, 2015 Geotechnical Data Example of data available
>300m Borehole from Richmond Source: Clague et al. (1998)
10/27
Slide 11
May 4 th, 2015 Geotechnical Data V s data, 300m Borehole from
Richmond 11/27
Slide 12
May 4 th, 2015 Geotechnical Data Richmond V s data V s (to
depths of 3.5km) derived from seismic reflection data Source:
Clague et al. (1998) 12/27
Slide 13
May 4 th, 2015 Geotechnical Data Richmond V s data Empirical
relationship Source: Clague et al. (1998) 13/27
Slide 14
May 4 th, 2015 Geotechnical Data Borings were performed at the
three school sites, giving V s profiles (top 30m only) 14/27
Slide 15
May 4 th, 2015 Geotechnical Data Combining the Measured V s
profiles and Empirical V s -depth relationship 15/27
Slide 16
May 4 th, 2015 Variation of Input Properties Monte Carlo
Distributions 16/27
Slide 17
May 4 th, 2015 17/27 Site Response Analysis Allowing For
Uncertainty 1,000s of randomly simulated sets of soil properties
are analysed subject to the same input motions
Slide 18
May 4 th, 2015 Analysis Results Example 1: to demonstrate the
influence of the uncertainty in a critical parameter, V s Input
Variables to Monte Carlo Material propertyMean valueStandard
Deviation V s (above 30m)Varies (from SCPT data)25m/s or 75m/s V s
(below 30m)Varies (Empirical equation)25m/s or 75m/s N kt 142 382
19kN/m 3 0.5kN/m 3 18/27
Slide 19
May 4 th, 2015 Analysis Results Example 1: to demonstrate the
influence of the uncertainty in a critical parameter, V s The
standard deviation used in the input in 25m/s here 19/27
Slide 20
May 4 th, 2015 Analysis Results Example 1: to demonstrate the
influence of the uncertainty in a critical parameter, V s Spread of
Input parameters, V s (standard deviation = 25m/s) 20/27
Slide 21
May 4 th, 2015 Analysis Results Example 1: to demonstrate the
influence of the uncertainty in a critical parameter, V s The
standard deviation is increased to 75m/s here 21/27
Slide 22
May 4 th, 2015 Analysis Results Example 1: to demonstrate the
influence of the uncertainty in a critical parameter, V s Comparing
the 25m/s and 75m/s standard deviation results 22/27
Slide 23
May 4 th, 2015 Analysis Results Full Stochastic Analysis: 30
input motions, 150 variations of soil properties for each motion,
4,500 simulations in total 23/27
Slide 24
May 4 th, 2015 Analysis Results Full Stochastic Analysis: 30
input motions, 150 variations of soil properties for each motion,
4,500 simulations in total 24/27
Slide 25
May 4 th, 2015 Analysis Results Full Stochastic Analysis: 30
input motions, 150 variations of soil properties for each motion,
4,500 simulations in total 25/27
Slide 26
May 4 th, 2015 Conclusions -Stochastic Monte Carlo simulation
was used in the site response analyses of three deep (300m) school
sites located in Richmond, BC to estimate amplification factors for
seismic retrofit designs -It was found to be an effective method of
coping with the effects of uncertainty in the soil properties used
in nonlinear 1D site response analyses -The simulations resulted in
stable mean values of spectral accelerations -The mean spectral
response in the period range of interest for retrofit design of
1-2seconds, increased by 15% as the standard deviation in V s went
from 25m/s to 75m/s 26/27
Slide 27
We would like to express our gratitude to some organisations
and industry partners who contributed to our travel expenses
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