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

Transcription

1 UMR CNRS 6183 Meta-model for the assessment of soil stochastic properties from monitering: application to harbor structures Khanh Toan Le, Franck Schoefs, Francesca Lanata 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 Objective and methodology 1. Identify parameters of soil characteristics from Structural Health Monitoring data (SHM) 2. Developp a Response Surface based inverse analysis 3. Provide pdf of soil parameters for structural reliability Random loading (here tide and water level) Random behavior (materials) Structural model Inverse analysis Responses: + Displacement + Stress Measured loading RS model

2 Objective and methodology Numerical situations for external loading FEM solving (Numerical data base) Polynomial Response Surface Objective function Set of a priori parameters Measured data Presentation of the studied structure Cross section of the wharf Cheviré-4 Strain gauge Numerical response Meta-model calibration Inverse analysis Random parameters identified Verification, validation Cheviré 4 View of the instrumentation plan 12 monitored tie-rods (30%) Technology : electrical strain gauges 3 piezometers ; measure of underground water level Data collection every 30 minutes Duration of collection : 01/2004-6/2005

3 Random variables and probabilistic modeling I. Selection of set of a priori parameters Elements of wharf modelled under PLAXIS (FEM code) Selection of set of a priori parameters Sand: γ unsat = 19kN/m 3 ; E=30Mpa; φ =30 ; ν=0; ψ=0 Random variables and probabilistic modeling Calculated parameters of soil: 1. γ sat : Own weight satured 2. γ unsat : Own weight unsatured 3. E: Young modulus 4. φ : Frictional internal angle 5. ν : Poisson modulus 6. c : Cohesion. 7. ψ : Dilatancy angle II. Sensibility analysis of parameters of soil to the loading in the tie-rod Selected phases of water level during a tide, CMAR=69 Water level of the tide measure during the two years CMAR: in France, the oscillation amplitude of semi-diurnal tide is associated to a coefficient named tide coefficient. Sensitivity analysis: influence of parameters of soil in the response III. Construction of a numerical data base 450 (to 600) computations by A numerical data base of 450 (to 600) results PLAXIS are performed calculated of the loading in the tie-rod

4 Calibration for response surface I. Selection of input random variables for the response suface calibration 6 input variables 28 terms in the expression of SR II. Quadratic response surfaces Reduction of variable 2 variables Calibration of response surface 31st May - 1 st June 2012, Lisbon Portugal Full quadratic Linear Pure quadratic Linear with interaction

5 Calibration for response surface 31st May - 1 st June 2012, Lisbon Portugal III. Response Surface Selection Representation of loading calculed in the tie-rod by RS and Plaxis Residual values criteria for each type of RS Identification results I. Identification Methodology Objective fonction and least squares method II. Results Identification result X 1 et X 2

6 II. Results Identification results Identification: results for X 1 et X 2 III. Verification and validation Parameters of distribution of X 1 et X 2 Identification results Evolution of loading in the tie-rod T4 (2004/2005)

7 I. Conclusions Conclusion and perspective 1. A probabilistic model suggested: Quadratic Response Surface for identifying mechanical parameters of embankment from Structural Health Monitoring data. 2. Research shows the good accuracy of RS meta-model in comparison to EF model. Accuracy of the modeling. 3. Identification result of frictional internal angle shows that support of the distribution contains acceptable values in comparison to littérature. Physical sense of the modeling. 4. Suggested model allows to extrapolate results for tide coefficients very different 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!