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A multiscale modelling of bone ultrastructure elastic properties using finite elements simulation and neural network method

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A multiscale modelling of bone ultrastructure elastic properties using finite elements simulation and neural network method

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dc.contributor.author Barkaoui, Abdelwahed es_ES
dc.contributor.author Tlili, Brahim es_ES
dc.contributor.author Vercher Martínez, Ana es_ES
dc.contributor.author Hambli, Ridha es_ES
dc.date.accessioned 2017-06-05T08:53:20Z
dc.date.available 2017-06-05T08:53:20Z
dc.date.issued 2016-10
dc.identifier.issn 0169-2607
dc.identifier.uri http://hdl.handle.net/10251/82324
dc.description.abstract Bone is a living material with a complex hierarchical structure which entails exceptional mechanical properties, including high fracture toughness, specific stiffness and strength. Bone tissue is essentially composed by two phases distributed in approximately 30 70%: an organic phase (mainly type I collagen and cells) and an inorganic phase (hydroxyapatite-HA-and water). The nanostructure of bone can be represented throughout three scale levels where different repetitive structural units or building blocks are found: at the first level, col-lagen molecules are arranged in a pentameric structure where mineral crystals grow in specific sites. This primary bone structure constitutes the mineralized collagen microfibril. A struc-tural organization of inter-digitating microfibrils forms the mineralized collagen fibril which represents the second scale level. The third scale level corresponds to the mineralized col-lagen fibre which is composed by the binding of fibrils. The hierarchical nature of the bone tissue is largely responsible of their significant mechanical properties; consequently, this is a current outstanding research topic. Scarce works in literature correlates the elastic prop-erties in the three scale levels at the bone nanoscale. The main goal of this work is to estimate the elastic properties of the bone tissue in a multiscale approach including a sensitivity analy-sis of the elastic behaviour at each length scale. This proposal is achieved by means of a novel hybrid multiscale modelling that involves neural network (NN) computations and finite elements method (FEM) analysis. The elastic properties are estimated using a neural network simulation that previously has been trained with the database results of the finite element models. In the results of this work, parametric analysis and averaged elastic constants for each length scale are provided. Likewise, the influence of the elastic constants of the tissue constituents is also depicted. Results highlight that intelligent numerical methods are pow-erful and accurate procedures to deal with the complex multiscale problem in the bone tissue with results in agreement with values found in literature for specific scale levels. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Computer Methods and Programs in Biomedicine es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Bone ultrastructure es_ES
dc.subject Multiscale modelling es_ES
dc.subject Finite element method es_ES
dc.subject Neural network computation es_ES
dc.subject Elastic properties es_ES
dc.subject.classification INGENIERIA MECANICA es_ES
dc.title A multiscale modelling of bone ultrastructure elastic properties using finite elements simulation and neural network method es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.cmpb.2016.07.005
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials es_ES
dc.description.bibliographicCitation Barkaoui, A.; Tlili, B.; Vercher Martínez, A.; Hambli, R. (2016). A multiscale modelling of bone ultrastructure elastic properties using finite elements simulation and neural network method. Computer Methods and Programs in Biomedicine. 134:69-78. doi:10.1016/j.cmpb.2016.07.005 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://doi.org/10.1016/j.cmpb.2016.07.005 es_ES
dc.description.upvformatpinicio 69 es_ES
dc.description.upvformatpfin 78 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 134 es_ES
dc.relation.senia 317120 es_ES
dc.identifier.eissn 1872-7565


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