L. Arany, S. Bhattacharya, J. Macdonald, and S. J. Hogan, Design of monopiles for offshore wind turbines in 10 steps, Soil Dynamics and Earthquake Engineering, vol.92, pp.126-152, 2017.

M. Arshad and B. C. O'kelly, Offshore wind-turbine structures: a review, Proceedings of the Institution of Civil Engineers -Energy, vol.166, pp.139-152, 2013.

T. Burton, N. Jenkins, D. Sharpe, and E. Bossanyi, Wind Energy Handbook, 2011.

S. Butterfield, W. Musial, J. Jonkman, and P. Sclavounos, Engineering challenges for floating offshore wind turbines, Proceedings of the 2005 Copenhagen Offshore Wind Conference, 2005.

M. Cheng and Y. Zhu, The state of the art of wind energy conversion systems and technologies: A review, Energy Conversion and Management, vol.88, pp.332-347, 2014.

C. C. Ciang, J. R. Lee, and H. J. Bang, Structural health monitoring for a wind turbine system: a review of damage detection methods, Measurement Science and Technology, vol.19, 2008.

T. Dirlik, Application of computers in fatigue analysis, 1985.

. Dnv-gl and . Dnv-os-j101, Design Of Offshore Wind Turbine Structures, 2014.

M. Dolores-esteban, J. S. López-gutiérrez, V. Negro, C. Matutano, F. M. García-flores et al., Offshore wind foundation design: Some key issues, Journal of Energy Resources Technology, vol.137, 2015.

I. Erlich, F. Shewarega, C. Feltes, F. W. Koch, and J. Fortmann, Offshore wind power generation technologies, Proceedings of the IEEE, vol.101, issue.4, pp.891-905, 2012.

. Ewea, The european offshore wind industry -key trends and statistics. ewea tech. rep. 23, 2015.

P. Fuglsang, C. Bak, J. G. Schepers, B. Bulder, T. T. Cockerill et al., Site-specific design optimization of wind turbines, Wind Energy, vol.5, pp.261-279, 2002.

K. Hasselmann, T. P. Barnett, E. Bouws, H. Carlson, D. Cartwright et al., Measurements of wind-wave growth and swell decay during the joint north sea wave project (jonswap), 1973.

R. Hoffmann, A comparison of control concepts for wind turbines in terms of energy capture, 2002.

J. Häfele, C. Hübler, C. G. Gebhardt, and R. Rolfes, A comprehensive fatigue load set reduction study for offshore wind turbines with jacket substructures, Renewable Energy, vol.118, pp.99-112, 2017.

, IEC. IEC 61400-1: Wind turbines -Part 1: Design Requirements, 2005.

. Iec and . Iec, 61400-3: Wind turbines -Part 3: Design requirements for offshore wind turbines, 2009.

G. Ingram, Wind turbine blade analysis using the blade element momentum method -version 1.1, 2011.

M. R. Islam, Y. Guo, and J. Zhu, A review of offshore wind turbine nacelle: Technical challenges, and research and developmental trends, Renewable and Sustainable Energy Reviews, vol.33, pp.161-176, 2014.

B. J. Jonkman and L. Kilcher, Turbsim user's guide: Version 1.06.00, 2012.

J. Jonkman, S. Butterfield, W. Musial, and G. Scott, Definition of a 5-mw reference wind turbine for offshore system development, 2009.

J. K. Kaldellis and D. Zafirakis, The wind energy (r)evolution: A short review of a long history, Renewable Energy, vol.36, pp.1887-1901, 2011.

K. I. Oh, W. Nam, M. S. Ryu, J. Y. Kim, and B. I. Epureanu, A review of foundations of offshore wind energy convertors: Current status and future perspectives, Renewable and Sustainable Energy Reviews, vol.88, pp.16-36, 2018.

P. Passon, Memorandum -derivation and description of the soil-pile-interaction models, iea-annex xxiii subtask 2, 2006.

P. Passon and K. Branner, Condensation of long-term wave climates for the fatigue design of hydrodynamically sensitive offshore wind turbine support structures. Ships and Offshore Structures, vol.11, pp.142-166, 2016.

W. J. Pierson and L. A. Moskowitz, Proposed spectral form for fully developed wind seas based on the similarity theory of s. a. kitaigorodskii, Journal of Geophysical Research, vol.69, pp.5181-5190, 1964.

I. Rychlik, On the 'narrow-band' approximation for expected fatigue damage, Prababilistic Engineering Mechanics, vol.8, pp.1-4, 1993.

S. Schafhirt, N. Verkaik, Y. Salman, and M. Muskulus, Ultra-fast analysis of offshore wind turbine support structures using impulse based substructuring and massively parallel processors, Proceedings of the Twenty-fifth International Ocean and Polar Engineering Conference, pp.301-308, 2015.

S. Schløer, L. G. Castillo, F. Fejerskov, H. Stroescu, and . Bredmose, A model for quick load analysis for monopile-type offshore wind turbine substructures, Wind Energy Science, vol.3, pp.57-73, 2018.

J. Schneider and M. Senders, Foundation design: A comparison of oil and gas platforms with offshore wind turbines, Marine Technology Society Journal, 2010.

P. J. Schubel and R. J. Crossley, Wind turbine blade design. Energies, vol.5, pp.3425-3449, 2012.

F. Sherratt, N. W. Bishop, and T. Dirlik, Predicting fatigue life from frequency domain data. Engineering integrity, vol.18, pp.12-16, 2005.

G. M. Stewart, A. Robertson, and M. A. Lackner, The creation of a comprehensive metocean data set for offshore wind turbine simulations, Wind Energy, vol.19, pp.1151-1159, 2015.

L. E. Stieng and M. Muskulus, Reducing the number of load cases for fatigue damage assessment of offshore wind turbine support structures by a simple severity-based sampling method (under review), Wind Energy Science Discussions, pp.1-16, 2018.

L. E. Stieng, R. Hetland, S. Schafhirt, and M. Muskulus, Relative assessment of fatigue loads for offshore wind turbine support structures, Energy Procedia, vol.80, pp.229-236, 2015.

C. Tibaldi, L. C. Henriksen, M. H. Hansen, and C. Bak, Wind turbine fatigue damage evaluation based on a linear model and a spectral method, Wind Energy, vol.19, pp.1289-1306, 2016.

. References-p-abrahamsen, A review of gaussian random fields and correlation functions, 1997.

I. A. Basheer and M. Hajmeer, Artificial neural networks: fundamentals, computing, design, and application, Journal of Microbiological Methods, vol.43, issue.1, pp.3-31, 2000.

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.22, pp.773-793, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00689580

B. J. Bichon, J. M. Mcfarland, and S. Mahadevan, Efficient surrogate models for reliability analysis of systems with multiple failure modes, Reliability Engineering and System Safety, vol.96, pp.1386-1395, 2011.

M. Bilgili, B. Sahin, and Y. A. , Application of artificial neural networks for the wind speed prediction of target station using reference stations data, Renewable Energy, vol.32, issue.14, pp.2350-2360, 2007.

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.

J. Bourinet, Reliability analysis and optimal design under uncertainty -Focus on adaptive surrogate-based approaches. Habilitation à diriger des recherches, 2018.
URL : https://hal.archives-ouvertes.fr/tel-01737299

G. E. Box and N. R. Draper, Empirical model-building and response surfaces. Wiley series in probability and mathematical statistics, 1987.

V. C. Chen, K. L. Tsui, R. R. Barton, and M. Meckesheimer, A review on design, modeling and application of computer experiments, IIE Transactions, vol.38, pp.273-291, 2006.

V. Dubourg, Adaptive surrogate models for reliability analysis and reliability-based design optimization, 2011.
URL : https://hal.archives-ouvertes.fr/tel-00697026

B. Echard, N. Gayton, and M. Lemaire, AK-MCS: An active learning reliability method combining kriging and monte carlo simulation, Structural Safety, vol.33, pp.145-154, 2011.

J. H. Friedman, Multivariate adaptive regression splines, Annals of Statistics, vol.19, pp.1-67, 1991.

R. L. Hardy, Multiquadric equations of topography and other irregular surfaces, Journal of Geolophysical Research, vol.76, issue.8, pp.1905-1915, 1971.

B. Iooss, L. Boussouf, V. Feuillard, and A. Marrel, Numerical studies of the metamodel fitting and validation processes, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00444666

N. Hansen, The CMA evolution strategy: A tutorial, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01297037

Q. Huchet, C. Mattrand, P. Beaurepaire, N. Gayton, and N. Relun, The help of metamodels or wind turbine certification, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01649675

B. J. Jonkman and L. Kilcher, Turbsim user's guide: Version 1.06.00, 2012.

J. Jonkman, S. Butterfield, and G. Scott, Definition of a 5-mw reference wind turbine for offshore system development, 2009.

J. M. Jonkman and M. L. Buhl, Fast user's guide, 2005.

N. Lelièvre, P. Beaurepaire, C. Mattrand, and N. Gayton, AK-MCSi: A kriging-based method to deal with small failure probabilities and time-consuming models, Structural Safety, vol.73, pp.1-11, 2018.

R. Schöbi, B. Sudret, and S. Marelly, Rare event estimation using polynomial-chaos-kriging, 2016.

G. M. Stewart, A. Robertson, and M. A. Lackner, The creation of a comprehensive metocean data set for offshore wind turbine simulations, Wind Energy, vol.19, pp.1151-1159, 2015.

S. Aasen, A. M. Page, K. S. Skau, and T. A. Nygaard, Effect of foundation modelling on the fatigue lifetime of a monopile-based offshore wind turbine, Wind Energy Science, vol.2, pp.361-376, 2017.

L. V. Andersen, T. L. Andersen, and L. Manuel, Model uncertainties for soil-structure interaction in offshore wind turbine monopile foundations, Journal of Marine Science and Engineering, vol.6, 2018.

,. S. Laszlo-arany, J. Bhattacharya, S. J. Macdonald, and . Hogan, Design of monopiles for offshore wind turbines in 10 steps, Soil Dynamics and Earthquake Engineering, vol.92, pp.126-152, 2017.

V. Dubourg, B. Sudret, and J. M. Bourinet, Reliability-based design optimization using kriging surrogates and subset simulation. Structural and Multidisciplinary Optimization, vol.44, pp.673-690, 2011.
DOI : 10.1007/s00158-011-0653-8
URL : https://hal.archives-ouvertes.fr/hal-00587311

B. Echard, N. Gayton, and M. Lemaire, Ak-mcs: An active learning reliability method combining kriging and monte carlo simulation, Structural Safety, vol.33, issue.2, pp.145-154, 2011.
DOI : 10.1016/j.strusafe.2011.01.002

C. M. Fontana, W. Carswell, S. R. Arwade, D. J. Degroot, and A. T. Myers, Sensitivity of the dynamic response of monopile-supported offshore wind turbines to structural and foundation damping, WIND ENGINEERING, vol.39, pp.609-628, 2015.

Z. Hu and S. Mahadevan, A single-loop kriging (silk) surrogate modeling for time-dependent reliability analysis, Journal of Mechanical Design, vol.138, 2016.
DOI : 10.1115/1.4033428

Z. Jiang, W. Hu, W. Dong, Z. Gao, and Z. Ren, Structural reliability analysis of wind turbines: A review, p.10, 2017.

N. Lelièvre, P. Beaurepaire, C. Mattrand, and N. Gayton, AK-MCSi: A kriging-based method to deal with small failure probabilities and time-consuming models, Structural Safety, vol.73, pp.1-11, 2018.

N. Lelièvre, Développement des méthodes AK pour l'analyse de fiabilité. Focus sur les événe-ments rares et la grande dimension, 2018.

M. Lemaire, Structural reliability, 2013.

J. Luengo, V. Negro, J. García-barba, J. López-gutiérrez, and M. D. Esteban, New detected uncertainties in the design of foundations for offshore wind turbines, Renewable Energy, vol.131, pp.667-677, 2019.

V. Negro, J. López-gutiérrez, M. D. Esteban, and C. Matutano, Uncertainties in the design of support structures and foundations for offshore wind turbines, Renewable Energy, vol.63, pp.125-132, 2014.

J. D. Sørensen and H. S. Toft, Probabilistic design of wind turbines, Energies, vol.3, pp.241-257, 2010.

B. Sudret, Uncertainty propagation and sensitivity analysis in mechanical models Contributions to structural reliability and stochastic spectral methods. Habilitation à diriger des recherches (HDR), 2007.

A. A. Taflanidis and J. L. Beck, Stochastic subset optimization for optimal reliability problems, 5th International Conference on Computational Stochastic Mechanics, vol.23, pp.324-338, 2008.
DOI : 10.1016/j.probengmech.2007.12.011

J. Zhang, A. A. Taflanidis-taflanidis, and J. C. Medina, Sequential approximate optimization for design under uncertainty problems utilizing kriging metamodeling in augmented input space, Computer Methods in Applied Mechanics and Engineering, vol.315, pp.369-395, 2017.
DOI : 10.1016/j.cma.2016.10.042

C. 4. Vers and L. Conceptions, Bien que présentant une erreur d'approximation de l'ordre de 12%, cette méthode permet un approximation rapide des quantités de probabilité de défaillance

, L'ensemble de la formalisation du problème de fiabilité, les explications relatives ainsi que les illustrations complètes des méthodes présentée ici sont présentées dans le Chapitre 4

D. Bibliographie-synthétique, Design Of Offshore Wind Turbine Structures, 2014.

B. Echard, N. Gayton, and M. Lemaire, AK-MCS: An active learning reliability method combining kriging and monte carlo simulation, Structural Safety, vol.33, issue.2, pp.145-154, 2011.

, IEC. IEC 61400-1: Wind turbines -Part 1: Design Requirements, 2005.

. Iec and . Iec, 61400-3: Wind turbines -Part 3: Design requirements for offshore wind turbines, 2009.

J. Jonkman, S. Butterfield, W. Musial, and G. Scott, Definition of a 5-mw reference wind turbine for offshore system development, 2009.

N. Lelièvre, P. Beaurepaire, C. Mattrand, and N. Gayton, AK-MCSi: A kriging-based method to deal with small failure probabilities and time-consuming models, Structural Safety, vol.73, pp.1-11, 2018.

M. Lemaire, Structural reliability, 2013.