Open Access
Mechanics & Industry
Volume 15, Number 1, 2014
Page(s) 1 - 17
Published online 07 April 2014
  1. T.G. Trucano, L.P. Swiler, T. Igusa, W.L. Oberkampf, M. Pilch, Calibration, validation, and sensitivity analysis: What’s what, Reliab. Eng. Syst. Saf. 91 (2006) 1331–1357 [CrossRef] [Google Scholar]
  2. W.L. Oberkampf, T.G. Trucano, Verification and validation benchmarks, Nucl. Eng. Design 238 (2008) 716–743 [CrossRef] [Google Scholar]
  3. C. Zang, C.W. Schwingshackl, D.J. Ewins, Model validation for structural dynamic analysis: An approach to the Sandia Structural Dynamic Challenge, Comput. Methods Appl. Mech. Engrg. 197 (2008) 2645–2659 [CrossRef] [Google Scholar]
  4. R.G. Hills, M. Pilch, K.J. Dowding, J. Red-Horse, T.L. Paez, I. Babuska, R. Tempone, Validation Challenge Workshop, Comput. Methods Appl. Mech. Engrg. 197 (2008) 2375–2380 [CrossRef] [MathSciNet] [Google Scholar]
  5. T.L Paez, J. Red-Horse, Structural dynamics challenge problem: Summary, Comput. Methods Appl. Mech. Engrg. 197 (2008) 2660–2665 [CrossRef] [Google Scholar]
  6. J. Red-Horse, T.L Pae, Sandia National Laboratories Validation workshop: Structural dynamics application, Comput. Methods Appl. Mech. Engrg. 197 (2008) 2578–2584 [CrossRef] [Google Scholar]
  7. M. Link, M. Friswell, Working group 1: generation of validated structural dynamic models – Results of a benchmark study utilising the GARTEUR SM-AG19 test-bed, Mech. Syst. Signal Process. 17 (2003) 9–20 [CrossRef] [Google Scholar]
  8. S. Atamturkur, F.M. Hemez, J.A. Laman, Uncertainty quantification in model verification and validation as applied to large scale historic masonry monuments, Eng. Struct. 43 (2012) 221–234 [CrossRef] [Google Scholar]
  9. M.H. de A. Carqueja, J.D. Riera, Model uncertainty in the determination of dynamic response of generator foundation, in: Michel Livolant; The International Association for Structural Mechanics in Reactor Technology (IASMIRT) (eds.), Transactions of the 14th International Conference on Structural Mechanics on Reactor technology SMiRT14 (1997), Lyon, France, 1997, pp. 95–102 [Google Scholar]
  10. S. Audebert, SICODYN International benchmark on dynamic analysis of structure assemblies: variability and numerical-experimental correlation on an industrial pump, Mécanique et Industries 11 (2010) 439–451 [CrossRef] [EDP Sciences] [Google Scholar]
  11. Jeong Kim, Joo-Cheol Yoon, Beom-Soo Kang, Finite element analysis and modeling of structure with bolted joints, Appl. Math. Modell. 31 (2007) 895–911 [CrossRef] [Google Scholar]
  12. J. Mackerle, Finite element analysis of fastening and joining: a bibliography (1990–2002), Int. J. Press. Vessels Piping 80 (2003) 253–271 [CrossRef] [Google Scholar]
  13. D. Dane Quinn, Modal analysis of joined structures, J. Sound Vib. 331 (2012) 81–93 [CrossRef] [Google Scholar]
  14. A. Gallina, W. Lisowski, L. Pichler, A. Strachowski, T. Uhl, Analysis of natural frequency variability of a brake component, Mech. Syst. Signal Process. 32 (2012) 188–199 [CrossRef] [Google Scholar]
  15. CodeAster, general public licensed structural mechanics finite element software, [Google Scholar]
  16. S. Audebert, I. Zentner, A. Mikchevitch, Variability and propagation of uncertainties on modal simulations of a built-up structure (SICODYN benchmark), in: G. de Roeck, G. Degrande, G. Lombaert, G. Müller (eds.), Proceedings of the 8th International Conference on Structural Dynamics, ISBN 978–90–760–1931–4, EURODYN 2011, Leuwen, Belgium, 2011, pp. 3000–3007 [Google Scholar]
  17. Sacks et al., Design and analysis of computer experiments, Stat. Sci. 4 (1989) 409–423 [CrossRef] [MathSciNet] [Google Scholar]
  18. I. M. Sobol’, V.I. Turchaninov, Y. L. Levitan, B.V. Shukhman, Quasirandom sequence generators, Ipm zak. no. 30, Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, Moscow, 1992 [Google Scholar]
  19. DACE. A Matlab Kriging Toolbox. S. Lophaven et al., Technical report IMM-TR-2002–12, 2002 [Google Scholar]
  20. E. de Rocquigny, N. Devictor, S. Tarantola, Uncertainty in industrial practice. A guide to quantitative uncertainty management, Wiley & Sons eds. Chichester, England, 2008 [Google Scholar]
  21. I. Zentner, S. Tarantola, E. de Rocquigny, Sensitivity analysis for reliable design verification of nuclear turbosets, Reliab. Eng. Syst. Saf. 96 (2010) 391–397 [CrossRef] [Google Scholar]
  22. A. Saltelli, M. Ratto, T. Andres, E. Campolongo, J. Cariboni, D. Gatelli, M. Saisana, S. Tarantola, Global sensitivity analysis, The primer, Wiley, 2008 [Google Scholar]
  23. F. Gant, P. Rouch, F. Louf, L. Champaney, Definition and updating of simplified models of joint stiffness, Int. J. Solids Struct. 48 (2011) 775–784 [CrossRef] [Google Scholar]
  24. F. Gant, L. Champaney, P. Rouch, Modeling of the bolted joint behavior variability with the Lack of Knowledge theory, in: ICCES Organizing Committee, Tech. Science Press (ed.), ISSN:1933–2815, ICCES 2010 International Conference on Computational and Experimental Engineering and Sciences, Las Vegas, USA, 2010, vol. 14, pp. 97–98 [Google Scholar]
  25. C.J. Roy, W.L. Oberkampf, A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing, Comput. Methods Appl. Mech. Engrg. 200 (2011) 2131–2144 [CrossRef] [MathSciNet] [Google Scholar]
  26. C. Unal, B. Williams, F. Hemez, S.H. Atamturktur, P. Mc Clure, Improved best estimate plus uncertainty methodology, including advanced validation concepts, to licence evolving nuclear reactors, Nucl. Eng. Design 241 (2011) 1813–1833 [CrossRef] [Google Scholar]
  27. S. Atamturktur, F. Hemez, B. Williams, C. Tome, C. Unal, A forecasting metric for predictive modelling, Comput. Struct. 89 (2011) 2377–2387 [CrossRef] [Google Scholar]
  28. J.R. Langenbrunner, F.M. Hemez, J.M. Booker, T.J. Ross, Model choice considerations and information integration using analytical hierarchy process, Sixth International Conference on Sensitivity Analysis of Model Output, Procedia Social and Behavioral Sciences 2 (2010) 7700–7701 [CrossRef] [Google Scholar]
  29. M. Pilch, T.G. Trucano, J.C. Helton, Ideas underlying the quantification of margins and uncertainties, Reliab. Eng. Syst. Saf. 96 (2011) 965–975 [CrossRef] [Google Scholar]
  30. A. Batou, C. Soize, M. Corus, Experimental identification of an uncertain computational dynamical model representing a family of structures, Comput. Struct. 89 (2011) 1440–1448 [CrossRef] [Google Scholar]
  31. W.L. Oberkampf, M.F. Barone, Measures of agreement between computation and experiment: validation metrics, J. Comput. Phys. 217 (2006) 5–36 [CrossRef] [Google Scholar]
  32. B. Van den Nieuwenhof, J.P. Coyette, Modal approaches for the stochastic finite element analysis of structures with material and geometry uncertainties, Comput. Methods Appl. Mech. Engrg. 192 (2003) 3705–3729 [CrossRef] [Google Scholar]
  33. D.C. Kammer, S. Nimityongskul, Propagation of uncertainty in test-analysis correlation of substructured spacecraft, J. Sound Vib. 330 (2011) 1211–1224 [CrossRef] [Google Scholar]
  34. M. Guedri, S. Cogan, N. Bouhaddi, Robustness of structural reliability analyses to epistemic uncertainties, Mech. Syst. Signal Process. 28 (2012) 458–469 [CrossRef] [Google Scholar]
  35. A.K. Der Kiureghian, O. Ditlevsen, Aleatory or epistemic? Does it matter? Struct. Safe. 31 (2009) 105–111 [Google Scholar]
  36. L. Hinke, Modelling approaches for the low-frequency analysis of built-up structures with non-deterministic properties, Master Thesis, University of Southampton, Faculty of Engineering, Science and Mathematics, Institute of Sound and Vibration Research, 2008 [Google Scholar]
  37. B.R. Mace, P.J. Shorter, A local modal perturbational method for estimating frequency response statistics of built-up structures with uncertain properties, J. Sound Vib. 242 (2001) 793–811 [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.