E. De-rocquigny, Modelling Under Risk and Uncertainty: An Introduction to Statistical, Phenomenological and Computational Methods, 2012.
DOI : 10.1002/9781119969495

A. Pasanisi, Uncertainty analysis and decision-aid: methodological, technical and managerial contributions to engineering and R&D studies, Mémoire d'HabilitationàHabilitationà Diriger des Recherches de l, 2014.

M. Baudin, A. Dutfoy, B. Iooss, and A. Popelin, Open TURNS: An industrial software for uncertainty quantification in simulation, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01107849

K. Fang, R. Li, and A. Sudjianto, Design and modeling for computer experiments, 2006.
DOI : 10.1201/9781420034899

R. Smith, Uncertainty quantification, SIAM, 2014.

C. Rasmussen and C. Williams, Gaussian Processes in Machine Learning, 2006.
DOI : 10.1162/089976602317250933

URL : http://hdl.handle.net/11858/00-001M-0000-0013-F365-A

A. Marrel, B. Iooss, F. Van-dorpe, and E. Volkova, An efficient methodology for modeling complex computer codes with Gaussian processes, Computational Statistics & Data Analysis, vol.52, issue.10, pp.4731-4744, 2008.
DOI : 10.1016/j.csda.2008.03.026

URL : https://hal.archives-ouvertes.fr/hal-00239492

L. , L. Gratiet, S. Marelli, and B. Sudret, Metamodel-based sensitivity analysis: Polynomial chaos expansions and Gaussian processes, 2017.
DOI : 10.1007/978-3-319-11259-6_38-1

URL : https://hal.archives-ouvertes.fr/hal-01428947

B. Iooss and P. Lema??trelema??tre, A review on global sensitivity analysis methods Uncertainty management in Simulation- Optimization of Complex Systems: Algorithms and Applications, pp.101-122, 2015.

J. Oakley and A. O. Hagan, Probabilistic sensitivity analysis of complex models: a Bayesian approach, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.34, issue.3, pp.66-751, 2004.
DOI : 10.1214/ss/1009213004

L. , L. Gratiet, C. Cannamela, and B. Iooss, A Bayesian approach for global sensitivity analysis of (multifidelity) computer codes, Journal of Uncertainty Quantification, vol.2, pp.336-363, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00842432

P. Lema??trelema??tre, E. Sergienko, A. Arnaud, N. Bousquet, F. Gamboa et al., Density modification-based reliability sensitivity analysis, Journal of Statistical Computation and Simulation, vol.22, issue.3, pp.1200-1223, 2015.
DOI : 10.1139/L10-056

L. Maurice, V. Costan, E. Guillot, and P. Thomas, Eddy current NDE performance demonstrations using simulation tools, AIP Conference Proceedings, vol.1511, pp.464-471, 2013.
DOI : 10.1063/1.4789084

T. Browne, L. L. Gratiet, G. Blatman, S. Cordeiro, B. Goursaud et al., Building Probability of Detection curves via metamodels, 12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP12, p.573, 2015.

L. , L. Gratiet, B. Iooss, T. Browne, G. Blatman et al., Model assisted probability of detection curves: New statistical tools and progressive methodology, Journal of Nondestructive Evaluation, vol.36, issue.8, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01260335

R. Thompson, A UNIFIED APPROACH TO THE MODEL-ASSISTED DETERMINATION OF PROBABILITY OF DETECTION, AIP Conference Proceedings, pp.1685-1692, 2007.
DOI : 10.1063/1.2902639

P. Calmon, Trends and Stakes of NDT Simulation, Journal of Nondestructive Evaluation, vol.38, issue.4, pp.339-341, 2012.
DOI : 10.1007/s10921-012-0152-x

I. Zentner, Numerical computation of fragility curves for NPP equipment, Nuclear Engineering and Design, vol.240, issue.6, pp.1614-1621, 2010.
DOI : 10.1016/j.nucengdes.2010.02.030

I. Zentner and E. Borgonovo, Construction of variance-based metamodels for probabilistic seismic analysis and fragility assessment, Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, vol.5, issue.2, pp.202-216, 2014.
DOI : 10.1016/j.envsoft.2008.12.002

G. Damblin, M. Keller, A. Pasanisi, P. Barbillon, and E. Parent, Approche décisionnelle bayésienne pour estimer une courbe de fragilité, Journal de la Société Française de Statistique, vol.155, pp.78-103, 2014.

M. Shinozuka, M. Feng, J. Lee, and T. Naganuma, Statistical Analysis of Fragility Curves, Journal of Engineering Mechanics, vol.126, issue.12, pp.1224-1231, 2000.
DOI : 10.1061/(ASCE)0733-9399(2000)126:12(1224)

A. Berens, NDE reliability data analysis, in: Metals Handbook, pp.689-701, 1988.

G. Box and D. Cox, An analysis of transformations, Journal of the Royal Statistical Society, vol.26, pp.211-252, 1964.

C. Mai, K. Konakli, and B. Sudret, Seismic fragility curves for structures using non-parametric representations, Frontiers of Structural and Civil Engineering In press

S. Demeyer, F. Jenson, and N. Dominguez, Modélisation d'un code numérique par un processus gaussien -Application au calcul d'une courbe de probabilité de dépasser un seuil, Proceedings of 44èmes Journées de Statistique, 2012.

J. Sacks, W. Welch, T. Mitchell, and H. Wynn, Design and Analysis of Computer Experiments, Statistical Science, vol.4, issue.4, pp.409-435, 1989.
DOI : 10.1214/ss/1177012413

E. Borgonovo and E. Plischke, Sensitivity analysis: A review of recent advances, European Journal of Operational Research, vol.248, issue.3, pp.869-887, 2016.
DOI : 10.1016/j.ejor.2015.06.032

F. Rupin, G. Blatman, S. Lacaze, T. Fouquet, and B. Chassignole, Probabilistic approaches to compute uncertainty intervals and sensitivity factors of ultrasonic simulations of a weld inspection, Ultrasonics, vol.54, issue.4, pp.1037-1046, 2014.
DOI : 10.1016/j.ultras.2013.12.006

E. Borgonovo, I. Zentner, A. Pellegri, S. Tarantola, and E. De-rocquigny, On the importance of uncertain factors in seismic fragility assessment, Reliability Engineering & System Safety, vol.109, pp.66-76, 2013.
DOI : 10.1016/j.ress.2012.08.007

I. Sobol, Sensitivity estimates for non linear mathematical models, Mathematical Modelling and Computational Experiments, vol.1, pp.407-414, 1993.

C. Prieur and S. Tarantola, Variance-Based Sensitivity Analysis: Theory and Estimation Algorithms, 2017.
DOI : 10.1007/978-3-319-11259-6_35-1

F. Gamboa, A. Janon, T. Klein, and A. Lagnoux, Sensitivity analysis for multidimensional and functional outputs, Electronic Journal of Statistics, vol.8, issue.1, pp.575-603, 2014.
DOI : 10.1214/14-EJS895

URL : https://hal.archives-ouvertes.fr/hal-00881112

A. Marrel and N. Saint-geours, Sensitivity Analysis of Spatial and/or Temporal Phenomena, 2017.
DOI : 10.1007/978-3-319-11259-6_39-1

E. Borgonovo and B. , Iooss, Moment-independent and reliability-based importance measures, 2017.
DOI : 10.1007/978-3-319-11259-6_37-1

T. Hesterberg, Estimates and confidence intervals for importance sampling sensitivity analysis, Mathematical and Computer Modelling, vol.23, issue.8-9, pp.79-85, 1996.
DOI : 10.1016/0895-7177(96)00041-6

URL : http://doi.org/10.1016/0895-7177(96)00041-6

J. Helton, J. Johnson, W. Obekampf, and C. Salaberry, Representation of analysis results involving aleatory and epistemic uncertainty, International Journal of General Systems, vol.12, issue.6, pp.605-646, 2010.
DOI : 10.1016/0165-0114(78)90029-5

A. Pasanisi, M. Keller, and E. Parent, Estimation of a quantity of interest in uncertainty analysis: Some help from Bayesian decision theory, Reliability Engineering & System Safety, vol.100, pp.93-101, 2012.
DOI : 10.1016/j.ress.2012.01.001

J. Bect, D. Ginsbourger, L. Li, V. Picheny, and E. Vazquez, Sequential design of computer experiments for the estimation of a probability of failure, Statistics and Computing, vol.34, issue.4, pp.773-793, 2012.
DOI : 10.1007/s11222-011-9241-4

URL : https://hal.archives-ouvertes.fr/hal-00689580

T. Browne and J. Fort, Redéfinition de la POD comme fonction de répartition aléatoire, Proceedings of 47èmes Journées de Statistique, 2015.

J. Kleijnen, Design and analysis of simulation experiments, 2015.
DOI : 10.1007/978-3-319-18087-8