ICDS12 LNEC Lisbon Portugal 31 May - 1 June 2012...

Meta-model for the assessment of soilstochastic properties from monitering:

application to harbor structuresKhanh Toan Le, Franck Schoefs, Francesca Lanata

UMR CNRS 6183

1. Objective and methodology

2. Presentation of the studied structure

3. Random variables and probabilistic modeling

4. Calibration for response surface

5. Identification results

6. Conclusion and perspectives

31st May - 1st June 2012, Lisbon Portugal

Durable StructuresFrom construction to rehabilitation

1. Identify parameters of soil characteristics from StructuralHealth Monitoring data (SHM)

2. Developp a Response Surface based inverse analysis

3. Provide pdf of soil parameters for structural reliability

Objective and methodology

Structural model

Random loading(here tide and water

level)

Random behavior(materials)

Responses:+ Displacement

+ Stress

Measured loading

Inverse analysis RS model



ICDS12 International Conference DURABLE STRUCTURES: from construction to rehabilitation LNEC • Lisbon • Portugal • 31 May - 1 June 2012

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Numericalsituations for

external loading

Set of a priori parameters

Numericalresponse

Meta-model calibration

Inverse analysis

Random parametersidentified

Verification, validation

FEM solving(Numerical data base)

Polynomial Response Surface

Objective function

Measureddata

Objective and methodology 31st May - 1st June 2012, Lisbon Portugal


Presentation of the studiedstructure

Cheviré 4

• 12 monitored tie-rods (30%)–Technology : electricalstrain gauges

• 3 piezometers ; measure of underground water level

• Data collection every 30 minutes

• Duration of collection : 01/2004-6/2005Strain gauge

Cross section of the wharf Cheviré-4 View of the instrumentation plan




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Random variables and probabilistic modeling

Elements of wharf modelled under PLAXIS (FEM code)

Calculated parameters of soil:

1. γsat : Own weight satured

2. γunsat : Own weight unsatured

3. E: Young modulus

4. φ : Frictional internal angle

5. ν : Poisson modulus

6. c : Cohesion.

7. ψ : Dilatancy angle

Selection of set of a priori parameters

Water level of the tide measureduring the two years

Sand: γunsat = 19kN/m3; E=30Mpa; φ =30 ; ν=0; ψ=0

I. Selection of set of a priori parameters



Selected phases of water level during a tide, CMAR=69

II. Sensibility analysis of parameters of soil to theloading in the tie-rod

Sensitivity analysis: influence of parameters of soil in the response

CMAR: in France, theoscillation amplitudeof semi-diurnal tide isassociated to acoefficient named tidecoefficient .

III. Construction of a numerical data base

450 (to 600) computations byPLAXIS are performed

A numerical data base of 450 (to 600) resultscalculated of the loading in the tie-rod

Random variables and probabilistic modeling




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Calibration for response surface I. Selection of input random variables forthe response suface calibration

6 input variables 28 terms in the expression of SRReduction of

variable

2 variables



II. Quadratic response surfaces

Full quadratic

Linear

Pure quadratic

Linear withinteraction

Calibration of response surface 31st May - 1st June 2012, Lisbon Portugal



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III. Response Surface Selection

Residual values criteria for each type of RS

Representation of loading calculed in the tie-rod by RS and Plaxis

Calibration for response surface 31st May - 1st June 2012, Lisbon Portugal


Identification resultsI. Identification Methodology

Objective fonction and least squares method

II. Results

Identification result X1 et X2




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Identification: results for X1 et X2

II. Results

Parameters of distribution of X1 et X2

Identification results 31st May - 1st June 2012, Lisbon Portugal


III. Verification and validation

Evolution of loading in the tie-rod T4 (2004/2005)

Identification results 31st May - 1st June 2012, Lisbon Portugal



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Conclusion and perspective

I. Conclusions

1. A probabilistic model suggested: Quadratic Response Surface foridentifying mechanical parameters of embankment from Structural HealthMonitoring data .

2. Research shows the good accuracy of RS meta-model in comparison to EFmodel. Accuracy of the modeling.

3. Identification result of frictional internal angle shows that support of thedistribution contains acceptable values in comparison to littérature.Physical sense of the modeling.

4. Suggested model allows to extrapolate results for tide coefficients verydifferent from a numerical data base. Robustness of the modeling

5. Reduction of numerical calculation time. Tractability of the modeling

6. Reduction of number of input random variables.

7. Capacity of application for different behavior models

II. Perspectives

1. Identification of another parameters: γ, E.

2. Analysis of structural reliability for in service structures.

3. Consideration for structures submitted to various loading: moorings,berthings, wind...

THANK YOU FOR YOUR ATTENTION!


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ICDS12 LNEC Lisbon Portugal 31 May - 1 June 2012...

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