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A Continuous Method to Compute Model Parameters for Soft Biological Materials

机译:计算软生物材料模型参数的连续方法

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摘要

Developing appropriate mathematical models for biological soft tissues such as ligaments, tendons, and menisci is challenging. Stress-strain behavior of these tissues is known to be continuous and characterized by an exponential toe region followed by a linear elastic region. The conventional curve-fitting technique applies a linear curve to the elastic region followed by a separate exponential curve to the toe region. However, this technique does not enforce continuity at the transition between the two regions leading to inaccuracies in the material model. In this work, a Continuous Method is developed to fit both the exponential and linear regions simultaneously, which ensures continuity between regions. Using both methods, three cases were evaluated: idealized data generated mathematically, noisy idealized data produced by adding random noise to the idealized data, and measured data obtained experimentally. In all three cases, the Continuous Method performed superiorly to the conventional technique, producing smaller errors between the model and data and also eliminating discontinuities at the transition between regions. Improved material models may lead to better predictions of nonlinear biological tissues' behavior resulting in improved the accuracy for a large array of models and computational analyses used to predict clinical outcomes.
机译:为诸如韧带,肌腱和半月板之类的生物软组织开发适当的数学模型具有挑战性。已知这些组织的应力-应变行为是连续的,其特征是指数趾区和线性弹性区。传统的曲线拟合技术将线性曲线应用于弹性区域,然后将单独的指数曲线应用于脚趾区域。但是,此技术不会在两个区域之间的过渡处强制执行连续性,从而导致材料模型不准确。在这项工作中,开发了一种连续方法以同时拟合指数区域和线性区域,从而确保区域之间的连续性。使用这两种方法,对三种情况进行了评估:数学生成的理想化数据,通过向理想化数据中添加随机噪声而产生的有噪声理想化数据以及实验获得的测量数据。在所有三种情况下,连续方法的性能均优于传统技术,从而在模型和数据之间产生较小的误差,并且还消除了区域之间过渡处的不连续性。改进的材料模型可以更好地预测非线性生物组织的行为,从而提高用于预测临床结果的大量模型和计算分析的准确性。

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