Predicting Biaxial Failure Strengths of Aortic Tissues Using a Dispersed Fiber Failure Model

35 Pages Posted: 26 Oct 2024

See all articles by Hutomo Tanoto

Hutomo Tanoto

Texas A&M University

Zhongxi Zhou

Texas Tech University

Kaijia Chen

affiliation not provided to SSRN

Riuxin Qiu

affiliation not provided to SSRN

Hanwen Fan

affiliation not provided to SSRN

Jacob Zachary Chen

University of Texas at Austin

Ethan Milton

affiliation not provided to SSRN

Yuxiao Zhou

Texas A&M University

Minliang Liu

Texas Tech University

Abstract

Despite advances in methods to incorporate patient-specific aortic geometries and tissue elastic properties into computational rupture risk analyses of aortic aneurysms, isotropic failure metrics remain widely used for aortic tissue, which oversimplifies its anisotropic failure characteristics. While classical failure criteria for engineered unidirectional fiber-reinforced composites demonstrate improved performance over isotropic metrics in predicting aortic failure properties, an accurate failure metric tailored to the aorta that accounts for dispersed collagen fiber architecture remains largely undeveloped and requires experimental validation. In this study, we employed a novel dispersed fiber failure metric that considers fiber dispersion and assessed its ability to predict the biaxial failure strengths of the aortic wall. We conducted off-axis uniaxial and planar biaxial failure tests, from which anisotropic failure strengths of aortic tissues were obtained through digital image correlation (DIC) analysis. The off-axis uniaxial data were used to calibrate the failure model parameters, while the biaxial failure data provided direct experimental validations. Using this approach, we evaluated the performance of two variants of the dispersed fiber failure metric: the dispersed Tsai-Hill and dispersed Hashin-Rotem models, comparing them to their unidirectional counterparts. Results showed that the dispersed Tsai-Hill and dispersed Hashin-Rotem models outperformed their unidirectional counterparts, reducing errors by 33.8% and 34.3%, respectively. These findings highlight the significance of incorporating fiber dispersion in models that predict aortic tissue failure.

Note:
Funding declaration: This study is supported in part by the American Heart Association Career Development Award (24CDA1266455).

Conflict of Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Keywords: aortic tissue, failure metric, biaxial testing, digital image correlation, aortic aneurysm

Suggested Citation

Tanoto, Hutomo and Zhou, Zhongxi and Chen, Kaijia and Qiu, Riuxin and Fan, Hanwen and Chen, Jacob Zachary and Milton, Ethan and Zhou, Yuxiao and Liu, Minliang, Predicting Biaxial Failure Strengths of Aortic Tissues Using a Dispersed Fiber Failure Model. Available at SSRN: https://ssrn.com/abstract=4988347 or http://dx.doi.org/10.2139/ssrn.4988347

Hutomo Tanoto

Texas A&M University ( email )

Zhongxi Zhou

Texas Tech University ( email )

2500 Broadway
Lubbock, TX 79409
United States

Kaijia Chen

affiliation not provided to SSRN ( email )

No Address Available

Riuxin Qiu

affiliation not provided to SSRN ( email )

No Address Available

Hanwen Fan

affiliation not provided to SSRN ( email )

No Address Available

Jacob Zachary Chen

University of Texas at Austin ( email )

Ethan Milton

affiliation not provided to SSRN ( email )

No Address Available

Yuxiao Zhou

Texas A&M University ( email )

Minliang Liu (Contact Author)

Texas Tech University ( email )

2500 Broadway
Lubbock, TX 79409
United States

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