Pseudo-Component Viscoelastic Modeling of Soft Tissues

Abstract

Understanding the response of human tissues to externally imposed stress and strain is critical to improving quality of life. This work explored regression approaches and modeling that aid discovery and describe mechanisms of soft tissue structures. The scope of the study is to develop a non-linear, multi-component, viscoelastic model using pseudo-component approach and to optimize the model parameters to best match the time-dependent experimental stress-strain data. This study also does qualitative testing to test the appropriateness and utility of the model. The models are developed using nonlinear optimization technique in MS Excel/ VBA for convenient access for others. Six composite models were developed using various combinations of viscoelastic components namely hyper-elastic spring, spring-and-dashpot, reform and retain component. Various optimization techniques such as RLM2, Cyclic and Leapfrogging were tested using the experimental stress time data and it was found that Leapfrogging was the most efficient and robust optimizer. 8 parameter models showed sufficiency in the number of parameters by best modeling PLGA, PCL and SIS data. The appropriateness of the models was studied by testing the models on literature data and also testing the models by generating cyclic stress-strain data. The models proved to be appropriate by returning expected results for the tests. The six composite models are made available for others along with the nonlinear optimization techniques as MS Excel/ VBA code.