On the use of machine learning techniques for the mechanical characterization of soft biological tissues
Open accessAbstract
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.
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Cited by
Sorted by citations
Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913 · see citing works
- Authors
- Cilla, M., Pérez‐Rey, I., Martínez, M. A., Peña, E., & Martínez, J.
- Affiliations
- Universidad de Zaragoza (ES)
- Department of Natural Resources and Environmental Engineering University of Vigo Vigo Spain
- Aragon Institute of Engineering Research (I3A) University of Zaragoza Zaragoza Spain
- Centro Universitario de la Defensa en San Javier (ES)
- Corresponding author
- Martínez, Javier
- Venue
- International Journal for Numerical Methods in Biomedical Engineering
- Publisher
- Wiley
- Published
- 2018-07-12
- Volume
- 34
- Issue
- 10
- Pages
- e3121-e3121
- ISSN
- 2040-7939 (ISSN-L)
- 2040-7947
- OpenAlex
- W2810809154
- Cited by
- 38
- References
- 43
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 8 · 2023: 2 · 2022: 3 · 2021: 6 · 2020: 6 · 2019: 2 · 2018: 1
- FWCI
- 1.50
- Type
- journal article
- Language
- en
- Open access
- green
- zaguan.unizar.es
- Locations
- International Journal for Numerical Methods in Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- Zaguan (University of Zaragoza Repository) (submittedVersion)
- Funders
- Spanish Ministry of Economy, Industry and Competitiveness (TIN2016-76770-R, Project identificator: DPI2016-76630-C2-1-R)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Elasticity and Material Modeling
- Physical Sciences › Engineering › Biomedical Engineering › Soft Robotics and Applications
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- soft biological tissues, mechanical characterization, parameter estimation, constitutive equations, strain energy function, Holzapfel-Gasser-Ogden model, support vector regression, bagged decision trees, artificial neural networks, uniaxial tensile testing, cardiovascular tissue, hyperelasticity
- MeSH terms
- Machine Learning, Blood Vessels, Humans, Models, Biological, Stress, Mechanical, Neural Networks, Computer
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