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

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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!

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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

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Flood risk world-wide

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Recent major floods

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New Orleans, 2005 (Katrina)

France, 2010 (Xyntia)

Thailand, 2011 Germany, 2013

Bosnia, 2014

Breaches in flood defenses

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Breitenhagen, Germany (June 2013)

LHW Sachsen-Anhalt, Flussbereich Schönebeck

Flood-prone Netherlands

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Piping is a danger for our levees!

Piping (backward internal erosion)

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Piping uncertainties

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geo-hydrology

stratification “anomalies”

+ ground properties (e.g. permeability, erodibility etc.)!

Piping reliability (probability of failure)

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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)

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Head monitoring (pore pressures)

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We measure the hydraulic head in the aquifer during a (long duration) river flood.

Site investigation (parameters)

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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

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Decision analysis (in everyday life)

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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)

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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)

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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.

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Hoe verder met de afgekeurde dijken: monitoren of versterken?

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On Reducing Piping Uncertainties A Bayesian Decision Approach

Timo Schweckendiek

Public Defense