H. E. Ross, D. Sicking, and R. A. Zimmer, Recommended Procedures for the Safety Performance Evaluation of Highway Features, NCHRP Report Transportation Researsh Board, vol.350, 1993.

. Thomson, D06 European best practice for roadside design: guidelines for roadside infrastructure on new and existing roads Project Roadside infrastructure for safer European roads, European Community [9] " Requirement for Road Restraint Systems, Design Manual for Roads and Bridges, p.6, 2006.

D. F. Neuenhaus, U. J. Geßler, and M. Feldmann, Using multibody-system modeling to make accurate predictions of vehicle impacts on road restraint systems, International Journal of Non-Linear Mechanics, vol.53, pp.24-31, 2013.
DOI : 10.1016/j.ijnonlinmec.2012.10.011

A. Tabiei and J. Wu, Roadmap for crashworthiness finite element simulation of roadside safety structures, Finite Elements in Analysis and Design, vol.34, issue.2, pp.145-57, 2000.
DOI : 10.1016/S0168-874X(99)00035-9

M. Mongiardini, WP5 -Computational Mechanics: Modelling of bolt connections for the ESP-N2 barrier, Department of Aerospace engineering Politecnico di Milano, Robust project report Doc. No.: ROBUST-05-016c -Rev

D. Neuenhaus, Simple Multi-body System Models of Bolted Connections to Consider all Relevant Nonlinearities of Failure mechanisms, TRB AFB (20)2 Meeting on Road Side Safety Design, 2014.

J. D. Reid and N. R. Hiser, Detailed modeling of bolted joints with slippage, Finite Elements in Analysis and Design, vol.41, issue.6, pp.547-62, 2005.
DOI : 10.1016/j.finel.2004.10.001

F. Nhtsa, Development of a Finite Element Model for W-Beam Guardrails

D. Marzougui, U. Mahadevaiah, and K. S. Opiela, Development of a Modified MGS Design for Test Level 2 Impact Conditions Using Crash Simulation, pp.2010-2015

M. R. Ferdous, Performance limit analysis for common roadside and median barriers using LS-DYNA, International Journal of Crashworthiness, vol.970, issue.6, pp.16-691, 2011.
DOI : 10.3141/1690-10

M. Borovinsek, Simulation of crash tests for high containment levels of road safety barriers, Engineering Failure Analysis, vol.14, issue.8, pp.1711-1729, 2007.
DOI : 10.1016/j.engfailanal.2006.11.068

Z. Ren and M. Vesenjak, Computational and experimental crash analysis of the road safety barrier, Engineering Failure Analysis, vol.12, issue.6, pp.963-73, 2005.
DOI : 10.1016/j.engfailanal.2004.12.033

S. O. Funtowicz and J. R. Ravetz, Uncertainty and Quality in Science for Policy, 1990.
DOI : 10.1007/978-94-009-0621-1

A. Saltelli and H. Rabitz, What is Sensitivity Analysis Sensitivity analysis Local Methods Sensitivity analysis, pp.3-14, 2008.

A. Saltelli, Global Sensitivity Analysis: The Primer, 2008.
DOI : 10.1002/9780470725184
URL : http://media.wiley.com/product_data/excerpt/74/04700599/0470059974.pdf

J. C. Heltona, Survey of sampling-based methods for uncertainty and sensitivity analysis, Reliability Engineering & System Safety, vol.91, issue.10-11, pp.1175-209, 2006.
DOI : 10.1016/j.ress.2005.11.017

F. Campolongo, J. Kleijnen, and T. Andres, Screening Methods Sensitivity analysis, pp.65-80, 2008.

A. Parkinson, C. Sorensen, and N. Pourhassan, A General Approach for Robust Optimal Design, Journal of Mechanical Design, vol.115, issue.1, pp.74-80, 1993.
DOI : 10.1115/1.2919328

G. Saporta, Probabilités, analyse de données et statistique Evaluation of Sensitivity and Uncertainty Analysis Methods in a Quality Assessment Framework with Application to Environmental and Business Statistics, 1997.

F. Campolongo and A. Saltelli, Design of Experiments Sensitivity Analysis, pp.51-64, 2008.

A. S. Hedayat, N. J. Sloane, and J. Stufken, Orthogonal Arrays: Theory and Applications, 1999.
DOI : 10.1007/978-1-4612-1478-6

M. D. Morris, Factorial Sampling Plans for Preliminary Computational Experiments, Technometrics, vol.1, issue.2, pp.161-74, 1991.
DOI : 10.2307/1266468
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.584.521

F. Campolongo, J. Cariboni, and A. Saltelli, An effective screening design for sensitivity analysis of large models, Environmental Modelling & Software, vol.22, issue.10, pp.1509-1527, 2007.
DOI : 10.1016/j.envsoft.2006.10.004

K. Chan, Variane-Based Mehtods Sensitivity Analysis, pp.167-98, 2008.

A. Saltelli, Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index, Computer Physics Communications, vol.181, issue.2, pp.259-70, 2010.
DOI : 10.1016/j.cpc.2009.09.018

Q. Zhai, J. Yang, and Y. Zhao, Space-partition method for the variance-based sensitivity analysis: Optimal partition scheme and comparative study, Reliability Engineering & System Safety, vol.131, pp.66-82, 2014.
DOI : 10.1016/j.ress.2014.06.013

E. Plischke, E. Borgonovo, and C. L. Smith, Global sensitivity measures from given data, European Journal of Operational Research, vol.226, issue.3, pp.536-50, 2012.
DOI : 10.1016/j.ejor.2012.11.047

X. Zhang and M. Pandey, An effective approximation for variance-based global sensitivity analysis, Reliability Engineering & System Safety, vol.121, pp.164-74, 2014.
DOI : 10.1016/j.ress.2013.07.010

M. C. Roger and M. N. Jan, Graphical Methods Sensitivity analysis, pp.245-66, 2000.

F. Pianosi and T. Wagener, A simple and efficient method for global sensitivity analysis based on?cumulative distribution functions, Environmental Modelling & Software, vol.67, pp.1-11, 2015.
DOI : 10.1016/j.envsoft.2015.01.004

C. Xu and G. Gertner, Understanding and comparisons of different sampling approaches for the Fourier Amplitudes Sensitivity Test (FAST), Computational Statistics & Data Analysis, vol.55, issue.1, pp.184-98, 2011.
DOI : 10.1016/j.csda.2010.06.028

B. Iooss, Revue sur l'analyse de sensibilité globale de modèles numériques, pp.152-153, 2011.

T. Simpson, Metamodels for Computer-based Engineering Design: Survey and recommendations, Engineering with Computers, vol.17, issue.2, pp.129-50, 2001.
DOI : 10.1007/PL00007198

E. D. Rocquigny, N. Devictor, and S. Tarantola, Uncertainty in industrial practice, 2008.
DOI : 10.1002/9780470770733

C. Goubel, M. Massenzio, and S. , Wood???steel structure for roadside safety barriers, International Journal of Crashworthiness, vol.17, issue.1, pp.63-73, 2012.
DOI : 10.1533/ijcr.2005.0321
URL : https://hal.archives-ouvertes.fr/hal-00951845

Y. D. Murray, Manual for LS-DYNA Wood Material Model 143, 2007.

J. Melcher, Design characteristics of structural steels based on statistical analysis of metallurgical products, Journal of Constructional Steel Research, vol.60, issue.3-5, pp.795-808, 2004.
DOI : 10.1016/S0143-974X(03)00144-5

S. Shan and G. G. Wang, Survey of modeling and optimization strategies to solve highdimensional design problems with computationally-expensive black-box functions, Struct Multidisc Optim, pp.41-219, 2010.

B. Lamoureux, N. Mechbal, and . Massé, A combined sensitivity analysis and kriging surrogate modeling for early validation of health indicators, Reliability Engineering & System Safety, vol.130, pp.130-142, 2014.
DOI : 10.1016/j.ress.2014.03.007
URL : https://hal.archives-ouvertes.fr/hal-01059286

Q. Ge, B. Ciuffo, and M. Menendez, Combining screening and metamodel-based methods: An efficient sequential approach for the sensitivity analysis of model outputs, Reliability Engineering & System Safety, vol.134, pp.134-334, 2015.
DOI : 10.1016/j.ress.2014.08.009

W. L. Oberkampf, Error and uncertainty in modeling and simulation, Reliability Engineering & System Safety, vol.75, issue.3, pp.75-333, 2002.
DOI : 10.1016/S0951-8320(01)00120-X

V. Kodjo, Evaluation et modélisation des dispositifs de retenue pour motards, 2016.

S. Yan, Sensitivity Analysis of Guardrail Impact Parameters Based on Deflection Index, Modern Transportation, vol.3, pp.1-7, 2014.

J. Ryu, Kriging interpolation methods in geostatistics and DACE model, KSME International Journal, vol.1, issue.No. 6, pp.619-651, 2002.
DOI : 10.1016/0309-1708(78)90016-7

A. Marrel, Calculations of Sobol indices for the Gaussian process metamodel, Reliability Engineering & System Safety, vol.94, issue.3, pp.94-742, 2009.
DOI : 10.1016/j.ress.2008.07.008
URL : https://hal.archives-ouvertes.fr/hal-00239494

J. O. Hallquist, LS-Dyna Theoretical Manual, Livermore Software Technology Corporation, 2006.

S. Bala and J. Day, General Guidelines for Crash Analysis in LS-DYNA

B. Silva and J. Meireles, Study of the Connections Behaviour in Road Safety Barriers, Conference ICEM15, 2012.

D. M. Bruce, Dynamic Tensile Testing of Sheet Steels & Influence of Strain Rate on Strengthening Mechanisms in Sheet Steels, Dissertation, Colorado School of Mines, 2003.

. Federal-highway-administration, Manual for LS-DYNA Soil Material Model 147, 2004.

T. A. Johansen, Gain-scheduled wheel slip control in automotive brake systems, IEEE Transactions on Control Systems Technology, vol.11, issue.6, pp.11-799, 2003.
DOI : 10.1109/TCST.2003.815607
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.9.3162

J. Harned, L. Johnston, and G. Scharpf, Measurement of tire brake force characteristics as related to wheel slip (anti-block) control system design, SAE Transactions, vol.690214, pp.78-909, 1969.

W. Web, <https://en.wikipedia.org/wiki/Multi-objective_optimization> [72] Web source Wikipedia: <https

A. Parkinson, C. Sorensen, and N. Pourhassan, A General Approach for Robust Optimal Design, Journal of Mechanical Design, vol.115, issue.1, pp.74-80, 1993.
DOI : 10.1115/1.2919328

L. Mian, Robut Optimization and Sensitivity Analysis with Multi-objective Genetic Algorithms: Singe and Multidisciplinary applications, 2007.

F. Viana, Things you wanted to know about the Latin hypercube design and were afraid to ask, " 10th world congree on structural and multidisciplinary optimization, 2013.

J. C. Helton and F. J. Davis, Latin hypercube sampling and the propagation of uncertainty in analysis of complex systems, Reliability Engineering and System Safety, pp.81-104, 2003.

G. Zhang, Global optimization of reliability design for large ball mill gear transmission based on the Kriging model and genetic algorithm, Mechanism and Machine Theory, pp.69-321, 2013.
DOI : 10.1016/j.mechmachtheory.2013.06.003

K. Elsayed and C. Lacor, Robust parameter design optimization using Kriging, RBF and RBFNN with gradient-based and evolutionary optimization techniques, Applied Mathematics and Computation, vol.236, pp.236-325, 2014.
DOI : 10.1016/j.amc.2014.03.082

J. Nocedal and S. J. Wright, Numerical Optimization, 2006.
DOI : 10.1007/b98874

J. Kennedy and R. C. Eberhart, Particle swarm optimization, Proceedings of ICNN'95, International Conference on Neural Networks, pp.1942-1950, 1995.
DOI : 10.1109/ICNN.1995.488968

R. C. Eberhart and J. Kennedy, A new optimizer using particle swarm theory, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp.39-43, 1995.
DOI : 10.1109/MHS.1995.494215

J. Holland, Adaptation in Natural and Artificial Systems, 1975.

K. Deb, Multiobjective Optimization Using Evolutionary Algorithms, 2001.

L. Méthodes-graphiques-donnent-une-vue-intuitive-de-la-complexité-du-modèle, Les AS avec corrélation et régression sont des méthodes qui peuvent être utilisées dans l'étude des relations entre les entrées et les sorties des modèles linéaires. Avec des biais acceptable, la méthode des transformations de rang linéarise des modèles 'non-linéaires mais monotones