Beex, L. A. A.; Institute of Computational Engineering, Faculty of Science, Technology and Communication, University of Luxembourg, Maison du Nombre, 6, Avenue de la FonteEsch-sur-Alzette 4364, Luxembourg
Language :
English
Title :
Estimating fibres’ material parameter distributions from limited data with the help of Bayesian inference
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
Bibliography
Alvin, K.F., Finite element model update via bayesian estimation and minimization of dynamic residuals. AIAA J. 35:5 (1997), 879–886.
Argento, G., Simonet, M., Oomens, C.W.J., Baaijens, F.P.T., Multi-scale mechanical characterization of scaffolds for heart valve tissue engineering. J. Biomech. 45:16 (2012), 2893–2898.
Arnoux, P.J., Bonnoit, J., Chabrand, P., Jean, M., Pithioux, M., Numerical damage models using a structural approach: application in bones and ligaments. Eur. Phys. J. Appl. Phys. 17:1 (2002), 65–73.
Badiche, X., Forest, S., Guibert, T., Bienvenu, Y., Bartout, J.D., Ienny, P., Croset, M., Bernet, H., Mechanical properties and non-homogeneous deformation of open-cell nickel foams: application of the mechanics of cellular solids and of porous materials. Mater. Sci. Eng., A 289:1 (2000), 276–288.
Ban, E., Barocas, V.H., Shephard, M.S., Picu, C.R., Effect of fiber crimp on the elasticity of random fiber networks with and without embedding matrices. J. Appl. Mech., 83(4), 2016 041008–041008–7.
Beck, J.L., Bayesian system identification based on probability logic. Struct. Contr. Health Monit. 17:7 (2010), 825–847.
Beck, J.L., Katafygiotis, L.S., Updating models and their uncertainties. I: bayesian statistical framework. J. Eng. Mech. 124:4 (1998), 455–461.
Beex, L.A.A., Peerlings, R.H.J., Geers, M.G.D., A multiscale quasicontinuum method for lattice models with bond failure and fiber sliding. Comput. Methods Appl. Mech. Eng. 269 (2014), 108–122.
Beex, L.A.A., Peerlings, R.H.J., van Os, K., Geers, M.G.D., The mechanical reliability of an electronic textile investigated using the virtual-power-based quasicontinuum method. Mech. Mater. 80 (2015), 52–66.
Bishop, C.M., Pattern Recognition and Machine Learning, Information Science and Statistics. 2006, Springer, New York.
Bosco, E., Peerlings, R.H.J., Geers, M.G.D., Predicting hygro-elastic properties of paper sheets based on an idealized model of the underlying fibrous network. Int. J. Solid Struct. 56–57 (2015), 43–52.
Bronkhorst, C.A., Modelling paper as a two-dimensional elasticplastic stochastic network. Int. J. Solid Struct. 40:20 (2003), 5441–5454.
Brooks, S., Gelman, A., Jones, G., Meng, X.L., Handbook of Markov Chain Monte Carlo. 2011, CRC press.
Daghia, F., de Miranda, S., Ubertini, F., Viola, E., Estimation of elastic constants of thick laminated plates within a Bayesian framework. Compos. Struct. 80:3 (2007), 461–473.
Delincé M., Delannay, F., Elastic anisotropy of a transversely isotropic random network of interconnected fibres: non-triangulated network model. Acta Mater. 52:4 (2004), 1013–1022.
D.D. Fitzenz, A. Jalobeanu, S.H. Hickman, Integrating laboratory creep compaction data with numerical fault models: a Bayesian framework, J. Geophys. Res.: Solid Earth 112 (B8), B08410.
Gao, S., Liang, B., Vidal-Salle, E., Development of a new 3D beam element with section changes: the first step for large scale textile modelling. Finite Elem. Anal. Des. 104 (2015), 80–88.
Genest, C., Ghoudi, K., Rivest, L.P., A semiparametric estimation procedure of dependence parameters in multivariate families of distributions. Biometrika 82:3 (1995), 543–552.
Gogu, C., Haftka, R., Riche, R.L., Molimard, J., Vautrin, A., Introduction to the Bayesian approach applied to elastic constants identification. AIAA J. 48:5 (2010), 893–903.
Gogu, C., Yin, W., Haftka, R., Ifju, P., Molimard, J., Le Riche, R., Vautrin, A., Bayesian identification of elastic constants in multi-directional laminate from moiré interferometry displacement fields. Exp. Mech. 53:4 (2013), 635–648.
Haario, H., Saksman, E., Tamminen, J., Adaptive proposal distribution for random walk Metropolis algorithm. Comput. Stat. 14:3 (1999), 375–396.
Hatami-Marbini, H., Picu, R.C., Scaling of nonaffine deformation in random semiflexible fiber networks. Phys. Rev., 77, 2008, 062103.
Hernandez, W.P., Borges, F.C.L., Castello, D.A., Roitman, N., Magluta, C., Bayesian inference applied on model calibration of fractional derivative viscoelastic model. Steffen, V. Jr., Rade, D.A., Bessa, W.M., (eds.) DINAME 2015-Proceedings of the XVII International Symposium on Dynamic Problems of Mechanics, Natal, 2015.
Hürlimann, W., Fitting bivariate cumulative returns with copulas. Comput. Stat. Data Anal. 45:2 (2004), 355–372.
Isenberg, J., Progressing from least squares to Bayesian estimation. Proceedings of the 1979 ASME Design Engineering Technical Conference, New York, 1979, 1–11.
Jung, A., Diebels, S., Koblischka-Veneva, A., Schmauch, J., Barnoush, A., Koblischka, M.R., Microstructural analysis of electrochemical coated open-cell metal foams by EBSD and nanoindentation. Adv. Eng. Mater. 16:1 (2013), 15–20.
Jung, A., Beex, L.A.A., Diebels, S., Bordas, S.P.A., Open-cell aluminium foams with graded coatings as passively controllable energy absorbers. Mater. Des. 87 (2015), 36–41.
Kaipio, J., Somersalo, E., Statistical and Computational Inverse Problems, vol. 160, 2006, Springer, New York.
Koutsourelakis, P.S., A novel Bayesian strategy for the identification of spatially varying material properties and model validation: an application to static elastography. Int. J. Numer. Methods Eng. 91:3 (2012), 249–268.
Kulachenko, A., Uesaka, T., Direct simulations of fiber network deformation and failure. Mech. Mater. 51 (2012), 1–14.
Lai, T.C., Ip, K.H., Parameter estimation of orthotropic plates by Bayesian sensitivity analysis. Compos. Struct. 34:1 (1996), 29–42.
Latil, P., Orgéas, L., Geindreau, C., Dumont, P.J.J., du Roscoat, S.R., Towards the 3D in situ characterisation of deformation micro-mechanisms within a compressed bundle of fibres. Compos. Sci. Technol. 71:4 (2011), 480–488.
Le, T.H., Dumont, P.J.J., Orgéas, L., Favier, D., Salvo, L., Boller, E., X-ray phase contrast microtomography for the analysis of the fibrous microstructure of SMC composites. Compos. Appl. Sci. Manuf. 39:1 (2008), 91–103.
Liu, P., Au, S.K., Bayesian parameter identification of hysteretic behavior of composite walls. Probabilist. Eng. Mech. 34 (2013), 101–109.
Marwala, T., Sibusiso, S., Finite element model updating using Bayesian framework and modal properties. J. Aircraft 42:1 (2005), 275–278.
Most, T., Identification of the parameters of complex constitutive models: least squares minimization vs. Bayesian updating. Straub, D., (eds.) Reliability and Optimization of Structural Systems, 2010, CRC press, 119–130.
Muto, M., Beck, J.L., Bayesian updating and model class selection for hysteretic structural models using stochastic simulation. J. Vib. Contr. 14:1–2 (2008), 7–34.
Nelsen, R.B., An Introduction to Copulas. 2007, Springer Science & Business Media.
Nichols, J.M., Link, W.A., Murphy, K.D., Olson, C.C., A Bayesian approach to identifying structural nonlinearity using free-decay response: application to damage detection in composites. J. Sound Vib. 329:15 (2010), 2995–3007.
Noh, Y., Choi, K.K., Lee, I., Identification of marginal and joint CDFs using Bayesian method for RBDO. Struct. Multidiscip. Optim., 40(1), 2009, 35.
Persson, J., Isaksson, P., A particlebased method for mechanical analyses of planar fiber-based materials. Int. J. Numer. Methods Eng. 93:11 (2013), 1216–1234.
Picu, R.C., Mechanics of random fiber networks-a review. Soft Matter 7 (2011), 6768–6785.
H. Rappel, L.A.A. Beex, J.S. Hale, L. Noels, S.P.A. Bordas, A tutorial on Bayesian inference to identify material parameters in solid mechanics, Arch. Comput. Methods Eng. doi:10.1007/s11831-018-09311-x.
Rappel, H., Beex, L.A.A., Bordas, S.P.A., Bayesian inference to identify parameters in viscoelasticity. Mechanics of Time-Dependent Materials 22:2 (May 2018), 221–258, 10.1007/s11043-017-9361-0.
H. Rappel, L.A.A. Beex, L. Noels, S.P.A. Bordas, Identifying elastoplastic parameters with Bayes theorem considering output error, input error and model uncertainty, Probabilist. Eng. Mech https://doi.org/10.1016/j.probengmech.2018.08.004.
Ridruejo, A., González, C., LLorca, J., Damage micromechanisms and notch sensitivity of glass-fiber non-woven felts: an experimental and numerical study. J. Mech. Phys. Solid. 58:10 (2010), 1628–1645.
Roch, O., Alegre, A., Testing the bivariate distribution of daily equity returns using copulas. An application to the Spanish stock market. Comput. Stat. Data Anal. 51:2 (2006), 1312–1329.
Sastry, A.M., Cheng, X., Wang, C.W., Mechanics of stochastic fibrous networks. J. Thermoplast. Compos. Mater. 11:3 (1998), 288–296.
Sencu, R.M., Yang, Z., Wang, Y.C., Withers, P.J., Rau, C., Parson, A., Soutis, C., Generation of micro-scale finite element models from synchrotron X-ray CT images for multidirectional carbon fibre reinforced composites. Compos. Appl. Sci. Manuf. 91 (2016), 85–95.
Seth, R.S., Page, D.H., The stress-strain curve of paper. Brander, J., (eds.) The Role of Fundamental Research in Paper Making: Transactions of the Symposium Held at Cambridge, 1983, Mechanical Engineering Publications Ltd., London, 421–452.
Shahsavari, A.S., Picu, R.C., Size effect on mechanical behavior of random fiber networks. Int. J. Solid Struct. 50:20 (2013), 3332–3338.
Silva, R.d.S., Lopes, H.F., Copula, marginal distributions and model selection: a Bayesian note. Stat. Comput. 18:3 (2008), 313–320.
Simo, J.C., Hughes, T.J.R., Computational Inelasticity. 2000, Springer Science & Business Media, New York.
Sklar, M., Fonctions de répartition à n dimensions et leurs marges. Université Paris, 8, 1959.
Song, P.X., Multivariate dispersion models generated from Gaussian copula. Scand. J. Stat. 27:2 (2000), 305–320.
Varrette, S., Bouvry, P., Cartiaux, H., Georgatos, F., Management of an academic HPC cluster: the UL experience. Proc. Of the 2014 Intl. Conf. on High Performance Computing & Simulation (HPCS 2014), 2014, IEEE, Bologna, Italy, 959–967.
Wang, C.W., Berhan, L., Sastry, A.M., Structure, mechanics and failure of stochastic fibrous networks: Part I-microscale considerations. J. Eng. Mater. Technol. 122:4 (2000), 450–459.
Wilbrink, D.V., Beex, L.A.A., Peerlings, R.H.J., A discrete network model for bond failure and frictional sliding in fibrous materials. Int. J. Solid Struct. 50:9 (2013), 1354–1363.
Similar publications
Sorry the service is unavailable at the moment. Please try again later.
This website uses cookies to improve user experience. Read more
Save & Close
Accept all
Decline all
Show detailsHide details
Cookie declaration
About cookies
Strictly necessary
Performance
Strictly necessary cookies allow core website functionality such as user login and account management. The website cannot be used properly without strictly necessary cookies.
This cookie is used by Cookie-Script.com service to remember visitor cookie consent preferences. It is necessary for Cookie-Script.com cookie banner to work properly.
Performance cookies are used to see how visitors use the website, eg. analytics cookies. Those cookies cannot be used to directly identify a certain visitor.
Used to store the attribution information, the referrer initially used to visit the website
Cookies are small text files that are placed on your computer by websites that you visit. Websites use cookies to help users navigate efficiently and perform certain functions. Cookies that are required for the website to operate properly are allowed to be set without your permission. All other cookies need to be approved before they can be set in the browser.
You can change your consent to cookie usage at any time on our Privacy Policy page.