000079507 001__ 79507
000079507 005__ 20191122145053.0
000079507 0247_ $$2doi$$a10.1002/cnm.3121
000079507 0248_ $$2sideral$$a108473
000079507 037__ $$aART-2018-108473
000079507 041__ $$aeng
000079507 100__ $$0(orcid)0000-0002-8503-9291$$aCilla, M.
000079507 245__ $$aOn the use of machine learning techniques for the mechanical characterization of soft biological tissues
000079507 260__ $$c2018
000079507 5060_ $$aAccess copy available to the general public$$fUnrestricted
000079507 5203_ $$aMotivated by the search for new strategies for fitting a material model, a new approach is explored in the present work. The use of numerical and complex algorithms based on machine learning techniques such as support vector machines for regression, bagged decision trees, and artificial neural networks is proposed for solving the parameter identification of constitutive laws for soft biological tissues. First, the mathematical tools were trained with analytical uniaxial data (circumferential and longitudinal directions) as inputs, and their corresponding material parameters of the Gasser, Ogden, and Holzapfel strain energy function as outputs. The train and test errors show great efficiency during the training process in finding correlations between inputs and outputs; besides, the correlation coefficients were very close to 1. Second, the tool was validated with unseen observations of analytical circumferential and longitudinal uniaxial data. The results show an excellent agreement between the prediction of the material parameters of the strain energy function and the analytical curves. Finally, data from real circumferential and longitudinal uniaxial tests on different cardiovascular tissues were fitted; thus, the material model of these tissues was predicted. We found that the method was able to consistently identify model parameters, and we believe that the use of these numerical tools could lead to an improvement in the characterization of soft biological tissues.
000079507 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/DPI2016-76630-C2-1-R
000079507 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000079507 590__ $$a2.082$$b2018
000079507 591__ $$aMATHEMATICS, INTERDISCIPLINARY APPLICATIONS$$b32 / 105 = 0.305$$c2018$$dQ2$$eT1
000079507 591__ $$aMATHEMATICAL & COMPUTATIONAL BIOLOGY$$b19 / 59 = 0.322$$c2018$$dQ2$$eT1
000079507 591__ $$aENGINEERING, BIOMEDICAL$$b46 / 80 = 0.575$$c2018$$dQ3$$eT2
000079507 592__ $$a0.653$$b2018
000079507 593__ $$aApplied Mathematics$$c2018$$dQ1
000079507 593__ $$aBiomedical Engineering$$c2018$$dQ1
000079507 593__ $$aSoftware$$c2018$$dQ1
000079507 593__ $$aModeling and Simulation$$c2018$$dQ1
000079507 593__ $$aMolecular Biology$$c2018$$dQ1
000079507 593__ $$aComputational Theory and Mathematics$$c2018$$dQ1
000079507 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000079507 700__ $$aPerez-Rey, I.
000079507 700__ $$0(orcid)0000-0002-8375-0354$$aMartinez, M.$$uUniversidad de Zaragoza
000079507 700__ $$0(orcid)0000-0002-0664-5024$$aPeña, E.$$uUniversidad de Zaragoza
000079507 700__ $$aMartinez, J.
000079507 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000079507 773__ $$g34, 10 (2018), e3121 [12 pp]$$tINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING$$x2040-7939
000079507 8564_ $$s484591$$uhttps://zaguan.unizar.es/record/79507/files/texto_completo.pdf$$yPostprint
000079507 8564_ $$s72456$$uhttps://zaguan.unizar.es/record/79507/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000079507 909CO $$ooai:zaguan.unizar.es:79507$$particulos$$pdriver
000079507 951__ $$a2019-11-22-14:44:13
000079507 980__ $$aARTICLE