79507 20191122145053.0 doi 10.1002/cnm.3121 sideral 108473 ART-2018-108473 eng (orcid)0000-0002-8503-9291 Cilla, M. On the use of machine learning techniques for the mechanical characterization of soft biological tissues 2018 Access copy available to the general public Unrestricted Motivated 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. info:eu-repo/grantAgreement/ES/MINECO/DPI2016-76630-C2-1-R info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 2.082 2018 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS 32 / 105 = 0.305 2018 Q2 T1 MATHEMATICAL & COMPUTATIONAL BIOLOGY 19 / 59 = 0.322 2018 Q2 T1 ENGINEERING, BIOMEDICAL 46 / 80 = 0.575 2018 Q3 T2 0.653 2018 Applied Mathematics 2018 Q1 Biomedical Engineering 2018 Q1 Software 2018 Q1 Modeling and Simulation 2018 Q1 Molecular Biology 2018 Q1 Computational Theory and Mathematics 2018 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Perez-Rey, I. (orcid)0000-0002-8375-0354 Martinez, M. Universidad de Zaragoza (orcid)0000-0002-0664-5024 Peña, E. Universidad de Zaragoza Martinez, J. 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 34, 10 (2018), e3121 [12 pp] INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2040-7939 484591 http://zaguan.unizar.es/record/79507/files/texto_completo.pdf Postprint 72456 http://zaguan.unizar.es/record/79507/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:79507 articulos driver 2019-11-22-14:44:13 ARTICLE