G. E. Archer, A. Saltelli, and I. M. Sobol-', Sensitivity measures, ANOVA like 670 techniques and the use of bootstrap Journal of Statistical Computation and Simulation mechanical behaviour taking into account the soil-water-air coupling), 1997.

D. Aubry, J. Hujeux, F. Lassoudière, and Y. Meimon, A double memory model with 678 multiple mechanisms for cyclic soil behaviour, Proceedings of the International 679 symposium on numerical models, pp.3-13, 1982.

J. Biarez and P. Hicher, Elementary mechanics of. soil behaviour, p.681, 1994.

C. Bonnard, F. Noverraz, O. Lateltin, and H. Raetzo, Large Landslides and Possibilities 683 of Sudden Reactivation, Proceedings 44th Geomechanics Colloquy, p.684, 1995.

F. Boulahya, . I. Dubus, F. Dupros, and P. Lombard, Footprint@work, a computing 686 framework for large scale parametric simulations: application to pesticide risk 687 assessment and management, p.688, 2007.

N. A. Cressie, Statistics for Spatial Data, 1993.
DOI : 10.1002/9781119115151

G. B. Crosta and J. J. Clague, Dating, triggering, modelling, and hazard assessment of large landslides, Geomorphology, vol.103, issue.1, pp.1-4, 2009.
DOI : 10.1016/j.geomorph.2008.04.007

F. Dupros, F. Boulahya, J. Vairon, P. Lombard, N. Capit et al., IGGI, a 693 computing framework for large scale parametric simulations: application to 694 uncertainty analysis with toughreact, p.pp, 2006.

S. G. Evans, G. Scarascia-mugnozza, A. Strom, R. L. Hermanns, G. Rohn et al., Landslides from van den Ham Finite Element simulation of a slow 699 moving natural slope in the Upper-Austrian Alps using a visco-hypoplastic 700, 2002.

J. Hujeux, Une loi de comportement pour le chargement cyclique des sols, p.702, 1985.

V. Davidovici, Génie Parasismique, pp.278-302

R. B. Gramacy, tgp: An R Package for Bayesian Nonstationary, p.708, 2007.

R. B. Gramacy and K. H. Herbert, Adaptive Design and Analysis of Supercomputer 711, 2009.

R. B. Gramacy and M. Taddy, Categorical Inputs, Sensitivity Analysis, p.713, 2010.

H. Gzyl, The Method of Maximum Entropy, Series 716 on Advances in Mathematics for Applied Sciences, pp.717-746, 1995.

N. A. Hamm, J. W. Hall, M. G. Anderson, T. Hastie, R. Tibshirani et al., Variance-based sensitivity analysis of the The Elements of Statistical Learning, p.724, 2002.

M. Kennedy and A. O-'hagan, Bayesian calibration of computer models (with discussion), 2001.

L. Laloui, L. Tacher, M. Moreni, and C. Bonnard, Hydromechanical modeling of crises 730 of large landslides: application to the La Frasse Landslide, Proceedings of the 9 th 731, 2004.

F. Lopez-caballero, A. Modaressi-farahmand-razavi, and H. Modaressi, Nonlinear numerical method for earthquake site response analysis I ??? elastoplastic cyclic model and parameter identification strategy, Bulletin of Earthquake Engineering, vol.117, issue.1, pp.303-741, 2007.
DOI : 10.1007/s10518-007-9032-7
URL : https://hal.archives-ouvertes.fr/hal-00179375

F. Lopez-caballero and A. Modaressi-farahmand-razavi, Numerical simulation of 743 liquefaction effects on seismic SSI, Soil Dynamics and Earthquake Engineering, vol.28, issue.748, 2008.

A. Marrel, B. Iooss, F. Van-dorpe, and E. Volkova, An efficient methodology for 750 modeling complex computer codes with Gaussian processes, Computational Statistics 751 and Data Analysis, pp.4731-4744, 2008.

A. Marrel, B. Iooss, B. Laurent, and O. Roustant, Calculations of Sobol indices for the 753, 2009.

J. D. Martin and T. W. Simpson, Use of Kriging Models to Approximate Deterministic Computer Models, AIAA Journal, vol.43, issue.4, pp.853-863, 2005.
DOI : 10.2514/1.8650

M. D. Mckay, R. J. Beckman, and W. J. Conover, A comparison of three methods for 758 selecting values of input variables in the analysis of output from a computer code, p.759, 1979.

F. Noverraz and C. Bonnard, Technical note on the visit of La Frasse Landslide, p.761, 1990.

J. E. Oakley and A. O-'hagan, Probabilistic sensitivity analysis of complex models, p.763, 2004.

O. Hagan and A. , Bayesian analysis of computer code outputs: A tutorial, Reliability 765 Engineering and System Safety 91, pp.1290-1300, 2006.

C. E. Rasmussen and C. K. Williams, Gaussian Processes in Machine Learning, p.773, 2006.
DOI : 10.1162/089976602317250933

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

A. Saltelli and . Sobol-'im, About the use of rank transformation in sensitivity analysis of model output, Reliability Engineering & System Safety, vol.50, issue.3, pp.225-239, 1995.
DOI : 10.1016/0951-8320(95)00099-2

A. Saltelli, K. Chan, and E. M. Scott, Sensitivity Analysis, p.779, 2000.
URL : https://hal.archives-ouvertes.fr/inria-00386559

A. Saltelli, Making best use of model evaluations to compute sensitivity indices, Computer Physics Communications, vol.145, issue.2, p.781, 2002.
DOI : 10.1016/S0010-4655(02)00280-1

A. Saltelli, Sensitivity Analysis for Importance Assessment, Risk Analysis, vol.23, issue.1, pp.579-783, 2002.
DOI : 10.1111/0272-4332.00040
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.194.7359

S. Tarantola, Global sensitivity analysis: The Primer, pp.786-304, 2008.

A. Saltelli, P. Annoni, I. Azzini, F. Campolongo, M. Ratto et al., Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index, Computer Physics Communications, vol.181, issue.2, pp.259-270, 2010.
DOI : 10.1016/j.cpc.2009.09.018

T. Santner, B. Williams, and W. Notz, The Design and Analysis of Computer 791, 2003.

I. M. Sobol-', Sensitivity estimates for non linear mathematical models Interpolation of Spatial Data, Mathematical Stein, M.L, 1993.

C. B. Storlie, L. P. Swiler, J. C. Helton, and C. J. Sallaberry, Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models, Reliability Engineering & System Safety, vol.94, issue.11, pp.1735-1763, 2009.
DOI : 10.1016/j.ress.2009.05.007

V. Mises and R. , Mathematical Theory of Probability and Statistics, Mathematical, vol.803, 1964.

. Fig, Mean of the main effect (blue dots) for each input factor of the slip surface constitutive 841 law at different instants of the crisis period (30 days (top), 150 days (middle) and 210 days 842 (bottom)) at the observation point 1 in the upper part of the landslide (left) and at the 843 observation point 2 in the lower part of the landslide (right) The bounds of the confidence 844 intervals (5% and 95 % quantile) are outlined by black cross-type markers, p.845