, ABAQUS/Standard User's Manual, Version 6.14, 2017.

S. R. Arwade, M. Moradi, and A. Louhghalam, Variance decomposition and global sensitivity for structural systems, Engineering Structures, vol.32, issue.1, pp.1-10, 2010.

R. Askey and J. Wilson, Some basic hypergeometric polynomials that generalize Jacobi polynomials, Memoirs of the American Mathematical Society, vol.54, issue.319, pp.1-57, 1985.

, Time-dependent total Sobol' indices S T i for the surrogate model of Product D (E4), Figure, vol.16

J. L. Beck and L. S. Katafygiotis, Updating models and their uncertainties. I: Bayesian statistical framework, Journal of Engineering Mechanics, vol.124, issue.4, pp.455-461, 1998.

M. Berveiller, B. Sudret, and M. Lemaire, Stochastic finite elements: a non intrusive approach by regression, European Journal of Computational Mechanics, vol.15, issue.1-3, pp.81-92, 2006.

G. Blatman, Adaptive sparse polynomial chaos expansions for uncertainty propagation and sensitivity analysis, 2009.
URL : https://hal.archives-ouvertes.fr/tel-00440197

G. Blatman and B. Sudret, An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis, Probabilistic Engineering Mechanics, vol.25, pp.183-197, 2010.

G. Blatman and B. Sudret, Adaptive sparse polynomial chaos expansion based on Least Angle Regression, Journal of Computational Physics, vol.230, pp.2345-2367, 2011.

G. Blatman and B. Sudret, Principal component analysis and Least Angle Regression in spectral stochastic finite element analysis, Proceedings 11th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP11), 2011.

G. Blatman and B. Sudret, Sparse polynomial chaos expansions of vector-valued response quantities, Proceedings 11th International Conference on Structural Safety and Reliability (ICOSSAR'2013), 2013.

R. D. Breu, Improved component additive method for the separating function -development of a testing and calculation procedure, 2016.

S. Choi, R. Grandhi, R. Canfield, and C. Pettit, Polynomial chaos expansion with Latin Hypercube sampling for estimating response variability, AIAA Journal, vol.45, pp.1191-1198, 2004.

, Feuerwiderstandsprüfungen -Teil 1: Allgemeine Anforderungen, Deutsches Institut für Normung, 2012.

, Eurocode 1: Actions on structures -Part 1-2: General actions -Actions on structures exposed to fire, European Committee for Standardization, 1991.

A. Frangi, V. Schleifer, and M. Fontana, Design model for the verification of the separating function of light timber frame assemblies, Engineering Structures, vol.32, issue.4, pp.1184-1195, 2010.

A. Gelman, J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari et al., , 2014.

, Texts in Statistical Science, Bayesian Data Analysis

A. Gelman, H. X.-l.-meng, and . Stern, Posterior predictive assessment of model fitness via realized discrepancies, Statistica Sinica, vol.6, issue.4, pp.733-760, 1996.

J. Goodman and J. Weare, Ensemble samplers with affine invariance, Communications in Applied Mathematics and Computational Science, vol.5, issue.1, pp.65-80, 2010.

X. Guo, D. Dias, C. Carvajal, L. Peyras, and P. Breul, Reliability analysis of embankment dam sliding stability using the sparse polynomial chaos expansion, Engineering Structures, vol.174, issue.1, pp.295-307, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02019576

H. Haario, E. Saksman, and J. Tamminen, An adaptive Metropolis algorithm, Bernoulli, vol.7, issue.2, pp.223-242, 2001.

W. K. Hastings, , 1970.

, Monte Carlo sampling methods using Markov chains and their applications, Biometrika, vol.57, issue.1, pp.97-109

, Fire-resistance tests -Elements of building construction, International Organization for Standardization, 1999.

I. T. Jolliffe, Springer Series in Statistics, 2002.

A. Just, Model scale fire tests of four gypsum plasterboards of Gyproc and stone wool, 2016.

A. Just and J. Schmid, Guidance for implementation of materials and products in fire design methods of timber frame assemblies, COST Action FP1404, 2018.

D. J. Mackay, Information Theory, Inference and Learning Algorithms, 2003.

K. N. Mäger, A. Just, J. Schmid, N. Werther, M. Klippel et al., , 2017.

, Procedure for implementing new materials to the component additive method, Fire Safety Journal, vol.107, pp.149-160

S. Marelli and B. Sudret, UQLab: A framework for uncertainty quantification in Matlab, Vulnerability, Uncertainty, and Risk (Proceedings 2nd International Conference on Vulnerability, Risk Analysis and Management (ICVRAM2014), pp.2554-2563, 2014.

S. Marelli and B. Sudret, Compressive polynomial chaos expansion for multidimensional model maps, Proceedings 12th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP12), 2015.

N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, Equation of state calculations by fast computing machines, The Journal of Chemical Physics, vol.21, issue.6, pp.1087-1092, 1953.

J. E. Mottershead, M. Link, and M. I. Friswell, The sensitivity method in finite element model updating: A tutorial, Mechanical Systems and Signal Processing, vol.25, issue.7, pp.2275-2296, 2011.

J. Nagel, J. Rieckermann, and B. Sudret, Uncertainty quantification in urban drainage simulation: Fast surrogates for sensitivity analysis and model calibration
URL : https://hal.archives-ouvertes.fr/hal-01902014

W. Oberkampf and C. Roy, Verification and Validation in Scientific Computing, 2010.

W. Oberkampf, T. Trucano, and C. Hirsch, Verification, validation, and predictive capability in computational engineering and physics, Applied Mechanics Reviews, vol.57, issue.5, pp.345-384, 2004.

E. Patelli, Y. Govers, M. Broggi, H. Gomes, M. Link et al., Sensitivity or Bayesian model updating: a comparison of techniques using the DLR AIRMOD test data, Archive of Applied Mechanics, vol.87, pp.905-925, 2017.

G. O. Roberts and J. S. Rosenthal, Examples of adaptive MCMC, Journal of Computational and Graphical Statistics, vol.18, issue.2, pp.349-367, 2009.

P. J. Rossky, J. D. Doll, and H. L. Friedmann, Brownian dynamics as smart Monte Carlo simulation, The Journal of Chemical Physics, vol.69, issue.10, pp.4628-4633, 1978.

, Sensitivity analysis, 2000.

V. Schleifer, Zum Verhalten von raumabschliessenden mehrschichtigen Holzbauteilen im Brandfall, 2009.

&. Sobol and I. , Sensitivity estimates for nonlinear mathematical models, Mathematical Modeling & Computational Experiment, vol.1, pp.407-414, 1993.

C. Soize and R. Ghanem, Physical systems with random uncertainties: chaos representations with arbitrary probability measure, SIAM Journal on Scientific Computing, vol.26, issue.2, pp.395-410, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00686211

B. Sudret, Global sensitivity analysis using polynomial chaos expansions, Proceedings 5th International Conference on Computational Stochastic Mechanics (CSM5), 2006.
URL : https://hal.archives-ouvertes.fr/hal-01432217

B. Sudret, Uncertainty propagation and sensitivity analysis in mechanical modelscontributions to structural reliability and stochastic spectral methods, 2007.

, Habilitationà diriger des recherches

B. Sudret, Global sensitivity analysis using polynomial chaos expansions, Reliability Engineering & System Safety, vol.93, pp.964-979, 2008.
URL : https://hal.archives-ouvertes.fr/hal-01432217

L. Van-der-maaten, E. Postma, H. Van-den, and . Herik, Dimensionality reduction: a comparative review, Journal of Machine Learning Research, vol.10, pp.66-71, 2008.

P. Wagner, J. Nagel, S. Marelli, and B. Sudret, UQLab user manual -Bayesian inference for model calibration and inverse problems, 2019.

D. Xiu and G. Karniadakis, The Wiener-Askey polynomial chaos for stochastic differential equations, SIAM Journal on Scientific Computing, vol.24, issue.2, pp.619-644, 2002.

B. Yu, R. Tang, and B. Li, Probabilistic calibration for development length models of deformed reinforcing bar, Engineering Structures, vol.182, issue.1, pp.279-289, 2019.