CALIBRATION OF MATERIAL MODELS FOR THE HUMAN CERVICAL SPINE LIGAMENT BEHAVIOUR USING A GENETIC ALGORITHM

Marina Franulović, Kristina Marković, Ana Trajkovski

DOI Number
https://doi.org/10.22190/FUME201029023F
First page
751
Last page
765

Abstract


Research of biomaterials in loading conditions has become a significant field in the material science nowadays. In order to provide better understanding of the loading effects on material structures, complex material models are usually chosen, depending on their applicability to the material under consideration. In order to provide as accurate as possible the material behavior modeling of the human cervical spine ligaments, the procedure for calibration of two material models has been evaluated. The calibration of material models was based on the genetic algorithm procedure in order to make possible optimization of material parameters identification for the chosen models. The influence of genetic algorithm operators upon the results in evaluated procedure has been tested and discussed here and the simulated behavior of the material has been compared to the experimentally recorded stress stretch relationship of the material under consideration. Since various influential factors contribute to the genetic algorithm performance in calibration of complex material models and identification of material parameters, additional possible improvements have been suggested for further research.

Keywords

Material Model, Behavior Modeling, Biomaterial, Genetic Algorithm, Inverse Analysis

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References


Mooney, M., 1940, A theory of large elastic deformation, Journal of Applied Physics, 11(9), pp. 582-592.

Rivlin, R.S., 1948, Large elastic deformations of isotropic materials. IV. further developments of the general theory, Philosophical Transactions of the Royal Society and Engineering Sciences, 241(835), pp. 379-397.

Chagnon, G., Rebouah, M., Favier, D., 2015, Hyperelastic energy densities for soft biological tissues: a review, Journal of Elasticity, 120, pp. 129-160.

Marckmann, G., Verron, E. 2006, Comparison of hyperelastic models for rubber-like materials, Rubber Chemistry and Technology, American Chemical Society, 79(5), pp. 835-858.

Ali, A., Hosseini,M., Sahari, B.B., 2010, A review of constitutive models for rubber-like materials, American Journal of Engineering and Applied Sciences, 3(1), pp. 232–239.

Seibert, D.J., Schöche, N., 2000, Direct comparison of some recent rubber elasticity models, Rubber Chemistry and Technology, 73(2), pp. 366–384.

Kohandel, M., Sivaloganathan,S., Tenti, G., 2008, Estimation of the quasi-linear viscoelastic parameters using a genetic algorithm, Mathematical and Computer Modelling, 47(3-4), pp. 266-270.

Sinha, N.K., Gupta, M.M., Zadeh, L.A., 1999, Soft computing and intelligent systems : theory and applications, Academic Press, 639 p.

Holland, J.H., 1992, Adaptation in Natural and Artificial Systems, The MIT press, Cambridge MA, 232 p.

Wright, A.H., 1991, Genetic algorithms for real parameter optimization, Foundations of Genetic Algorithms, 1, pp. 205-218.

Forrest, S., 1993, Genetic Algorithms: Principles of Natural Selection Applied to Computation, Science, 261(5123), pp. 872-878.

Schraudolph, N.N., Belew, R.K., 1992, Dynamic Parameter Encoding for Genetic Algorithms, Machine Learning, 9(1), pp. 9–21.

Louchet, J., 1994, An evolutionary algorithm for physical motion analysis, Proc. Conference on British Machine Vision, BMVC 94, York, 2, pp. 701-710.

Louchet, J., Provot, X., Crochemore, D., 1995, Evolutionary identification of cloth animation models, In Computer Animation and Simulation ’95: Proc. Eurographics Workshop in Maastricht, Springer, Vienna, pp. 44-54.

Chatterjee, S., Laudato, M., Lynch, L.A., 1996, Genetic algorithms and their statistical applications: an introduction, Computational Statistics and Data Analysis, 22(6), pp. 633-654.

Joukhadar, A., Garat, F., Laugier, C., 1997, Parameter identification for dynamic simulation, Proc. IEEE International Conference on Robotics and Automation, CIRA 1997, Monterey, 3, pp: 1928-1933.

Sid, B., Domaszewski, M., Peyraut, F., 2005, Topology optimization using an adaptive genetic algorithm and a new geometric representation, WIT Transactions on The Built Environment, 80, pp. 127-135.

Karr, C.L., Yakushin, I., Nicolosi, K., 2000, Solving inverse initial-value, boundary-value problems via genetic algorithm, Engineering Applications of Artificial Intelligence, 13(6), pp. 625-633.

Vigdergauz, S., 2001, The effective properties of a perforated elastic plate: Numerical optimization by genetic algorithm, International Journal of Solids and Structures, 38, pp. 8593-8616.

Albu, A., Precup, R.E., Teban, T.A., 2019, Results and challenges of artificial neural networks used for decision-making and control in medical applications, Facta Universitatis-Series Mechanical Engineering, 17(3), pp. 285-308.

Lago, M.A., Ruperez, M.J., Martinez-Martinez, F., Monserrat, C., 2014, Genetic algorithms for estimating the biomechanical behavior of breast tissues, Proc. International Conference on Biomedical and Health Informatics, BHI 2014, Valencia, pp. 760-763.

Han, L., Hipwell, J.H., Tanner, C., Taylor, Z., Mertzanidou, T., Cardoso, J., Ourselin, S., Hawkes, D.J., 2012, Development of patient-specific biomechanical models for predicting large breast deformation, Physics in Medicine and Biology, 57(2), pp. 455-472.

Madjidi, Y., Shirinzadeh, B., Banirazi, R., Tian, Y., Smith, J., Zhong, Y., 2009, An improved approach to estimate soft tissue parameters using genetic algorithm for minimally invasive measurement, 2nd Proc. International Conference on Biomedical Engineering and Informatics, BMEI 2009, Tianjin, pp. 1-6.

Bianchi, G., Solenthaler, B., Székely, G., Harders, M., 2004, Simultaneous topology and stiffness identification for mass-spring models based on fem reference deformations, Medical Image Computing and Computer-Assisted Intervention, 3217, pp. 293-301.

Nair, A.U., Taggart, D.G., Vetter, F.J., 2007, Optimizing cardiac material parameters with a genetic algorithm, Journal of Biomechanics, 40(7), pp. 1646-1650.

Martínez-Martínez, F., Rupérez, M.J., Martín-Guerrero, J.D., Monserrat, C., Lago, M.A., Pareja, E., Brugger, S., López-Andújar, R., 2013, Estimation of the elastic parameters of human liver biomechanical models by means of medical images and evolutionary computation, Computer Methods and Programs in Biomedicine, 111(3), pp. 537-549.

Pandit, A., Lu, X., Wang, C., Kassab, G.S., 2005, Biaxial elastic material properties of porcine coronary media and adventitia, American Journal of Physiology, Heart and Circulatory Physiology, 288, pp. H2581–H2587.

Sverdlik, A., Lanir, Y., 2002, Time-dependent mechanical behavior of sheep digital tendons, including the effects of preconditioning, Journal of Biomechanical Engineering, 124(1), pp. 78-84.

Hu, J., Klinich, K.D., Miller, C.S., Nazmi, G., Pearlman, M.D., Schneider, L.W., Rupp, J.D., 2009, Quantifying dynamic mechanical properties of human placenta tissue using optimization techniques with specimen-specific finite-element models, Journal of Biomechanics, 42(15), pp. 2528-2534.

Schendel, M., Wood, K., Buttermann, G., Lewis, J., Ogilvie, J., 1993, Experimental measurement of ligament force, facet force, and segment motion in the human lumbar spine, Journal of Biomechanics, 26(4-5), pp. 427-438.

Haines, D.W., Wilson, W.D., 1979, Strain-energy density function for rubberlike materials, Journal of the Mechanics and Physics of Solids, 27(4), pp. 345-360.

De Pascalis, R., 2010, The Semi-Inverse Method in solid mechanics: Theoretical underpinnings and novel applications, PhD Thesis, Universite Pierre et Marie Curie and Universita del Salento, 140 p.

Misra, S., Ramesh, K.T., Okamura, A.M., 2010, Modeling of Nonlinear Elastic Tissues for Surgical Simulation, Computer Methods in Biomechanics and Biomedical Engineering, 13(6), pp. 811-818.

Hackett, R.M., 2016, Hyperelasticity primer, Springer, Switzerland, 170 p.

Guo, Z., Caner, F., Peng, X., Moran, B., 2008, On constitutive modelling of porous neo-Hookean composites, Journal of the Mechanics and Physics of Solids, 56(6), pp. 2338–2357.

Quapp, K.M., Weiss, J.A., 1998, Material characterization of human medial collateral ligament, Journal of Biomechanical Engineering, 120(6), pp. 757–763.

Franulović, M., Basan, R., Prebil, I., 2009, Genetic algorithm in material model parameters’ identification for low-cycle fatigue, Computational Materials Science, 45(2), pp. 505-510.

Harb, N., Labed, N., Domaszewski, M., Peyraut, F., 2011, A new parameter identification method of soft biological tissue combining genetic algorithm with analytical optimization, Computer Methods in Applied Mechanics and Engineering, 200(4), pp. 208-215.

Reeves, C.R., Rowe, J.E., 2002, Genetic Algorithms—Principles and Perspectives, Springer US, New York, 332 p.

Blickle, T., Thiele, L., 1996, A comparison of selection schemes used in evolutionary algorithms, Evolutionary Computation, 4(4), pp. 361–394.

Xie, H., Zhang, M., 2013, Tuning Selection Pressure in Tournament Selection, IEEE Transactions on Evolutionary Computation, 17(1), pp. 1-19.

Reynes, C., Sabatier, R., 2012, A New Stopping Criterion for Genetic Algorithms, Proceedings of the 4th International Joint Conference on Computational Intelligence, IJCCI 2012, Barcelona, Spain, pp. 202-207.

Bhandari, D., Murthy, C.A., Pal, S.K., 2012, Variance as a stopping criterion for genetic algorithms with elitist model, Fundamenta Informaticae, 120, pp. 145-164.

Eiben, A.E., Raué, P.E., Ruttkay, Z., 1994, Genetic algorithms with multi-parent recombination, Proc. Parallel Problem Solving from Nature, PPSN 1994, Jerusalem, pp. 78-87.

Akbari, R., Ziarati, K., 2011, A multilevel evolutionary algorithm for optimizing numerical functions, International Journal of Industrial Engineering Computations, 2(2), pp. 419-430.




DOI: https://doi.org/10.22190/FUME201029023F

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