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On Reducing Piping Uncertainties A Bayesian Decision Approach
Timo Schweckendiek
Public Defense
Program
9:30-9:50
10:00-11:00
11:15-11:30
----------------
13:30-16:00
16:00-17:30
4 July 2014 On Reducing Piping Uncertainties (PhD Defense Timo Schweckendiek) 2
Presentation (“lekenpraatje”)
Defense
Graduation ceremony
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Mini-symposium
Reception and drinks (“borrel”)
Foyer, Aula TU Delft
Switch off mobile phones!
4 July 2014 On Reducing Piping Uncertainties (PhD Defense Timo Schweckendiek) 3
Outline
1. Floods and flood defenses
2. Piping reliability – process and models
– uncertainties
3. Reliability updating a) Field observations
b) Head monitoring
c) Site investigation
4. Decision analysis for monitoring and site investigation
5. Main findings and outlook
4 July 2014 On Reducing Piping Uncertainties (PhD Defense Timo Schweckendiek) 4
Flood risk world-wide
4 July 2014 On Reducing Piping Uncertainties (PhD Defense Timo Schweckendiek) 5
Recent major floods
4 July 2014 On Reducing Piping Uncertainties (PhD Defense Timo Schweckendiek) 6
New Orleans, 2005 (Katrina)
France, 2010 (Xyntia)
Thailand, 2011 Germany, 2013
Bosnia, 2014
Breaches in flood defenses
4 July 2014 On Reducing Piping Uncertainties (PhD Defense Timo Schweckendiek) 7
Breitenhagen, Germany (June 2013)
LHW Sachsen-Anhalt, Flussbereich Schönebeck
Flood-prone Netherlands
4 July 2014 On Reducing Piping Uncertainties (PhD Defense Timo Schweckendiek) 8
Piping is a danger for our levees!
Piping (backward internal erosion)
4 July 2014 On Reducing Piping Uncertainties (PhD Defense Timo Schweckendiek) 9
Piping uncertainties
4 July 2014 On Reducing Piping Uncertainties (PhD Defense Timo Schweckendiek) 10
geo-hydrology
stratification “anomalies”
+ ground properties (e.g. permeability, erodibility etc.)!
Piping reliability (probability of failure)
4 July 2014 On Reducing Piping Uncertainties (PhD Defense Timo Schweckendiek) 11
k
l
hc
h,hc
MODEL
(failure mechanism)h
water level
(load)
probability of failure:
P(hc < h)
critical water level
(resistance)
updated
resistance
hch
Bayesian
updating
(new data)
hc”
updated
probability of failure
h,hc
RESEARCH QUESTION
How can piping-related uncertainty be reduced cost-effectively?
Field observations (types)
On Reducing Piping Uncertainties (PhD Defense Timo Schweckendiek) 12
Observation Uplift Heave Piping
“Nothing” NO no
information no
information
Excessive Seepage
YES no
information no
information
Erosion / Sand Boil(s)
YES YES no
information
…at the observed water level (loading)!
4 July 2014
Field observations (effects observed sand boil)
4 July 2014 On Reducing Piping Uncertainties (PhD Defense Timo Schweckendiek) 13
Head monitoring (pore pressures)
4 July 2014 On Reducing Piping Uncertainties (PhD Defense Timo Schweckendiek) 14
We measure the hydraulic head in the aquifer during a (long duration) river flood.
Site investigation (parameters)
4 July 2014 On Reducing Piping Uncertainties (PhD Defense Timo Schweckendiek) 15
THROUGH INTERPRETATION OF SOIL TYPES
• stratification (soil types)
• blanket thickness (and weight)
THROUGH CORRELATIONS
• grain size
• permeability
+ ANOMALIES
Site investigation (anomalies)
UPDATING PROBABILITY OF ANOMALIES AND FAILURE
4 July 2014 On Reducing Piping Uncertainties (PhD Defense Timo Schweckendiek) 16
Decision analysis (in everyday life)
4 July 2014 On Reducing Piping Uncertainties (PhD Defense Timo Schweckendiek) 17
Should I get an insurance or a better lock for my new bike?
value bike: 1000€
insurance: 150€
better lock: 50€
P(bike stolen) = 0.1
get insurance
do nothing
buy better lock
P(bike not stolen) = 0.9
P(bike stolen) = 0.01
P(bike not stolen) = 0.99
P(bike stolen) = 0.1
P(bike not stolen) = 0.9
ACTIONS OUTCOMES CONSEQUENCES
150€
150€
1050€
50€
1000€
0€
150€
0.1*1000 = 100€
0.01*1050+0.99*50 = 60€
EXPECTED COST
Decision analysis (monitoring and site investigation)
4 July 2014 On Reducing Piping Uncertainties (PhD Defense Timo Schweckendiek) 18
starting point: flood defense unsafe
we buy information our retrofitting design and cost change
find optimal strategy (lowest expected cost)
Decision analysis (example anomaly detection)
4 July 2014 On Reducing Piping Uncertainties (PhD Defense Timo Schweckendiek) 19
Benefit cost ratio
𝐵𝐶𝑅 ≈106−105
104 =
9⋅105
104 = 90
Main findings and recommendations
Bayesian analysis allows us to incorporate information from different sources to update the probability of failure.
Where the prior uncertainties are large, all extra information has a considerable impact.
Investments in monitoring and site investigation can be highly cost-effective.
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Research: Some gaps still need to be filled (length-effect, staged strategies, waiting time to significant flood …)
Practice: Quantify the expected return of investment of your site investigation to convince clients.
Policy: Reliability-based standards open up opportunities. The Eurocode and the envisaged Dutch safety standards are heading in the right direction.
4 July 2014 On Reducing Piping Uncertainties (PhD Defense Timo Schweckendiek) 20
Hoe verder met de afgekeurde dijken: monitoren of versterken?
4 July 2014 On Reducing Piping Uncertainties (PhD Defense Timo Schweckendiek) 21
On Reducing Piping Uncertainties A Bayesian Decision Approach
Timo Schweckendiek
Public Defense