For the accurate prediction of the vascular disease progression, there is a crucial need for developing a systematic tool aimed toward patient-specific modeling. Considering the interpatient variations, a prior distribution of model parameters has a strong influence on computational results for arterial mechanics. One crucial step toward patient-specific computational modeling is to identify parameters of prior distributions that reflect existing knowledge. In this paper, we present a new systematic method to estimate the prior distribution for the parameters of a constrained mixture model using previous biaxial tests of healthy abdominal aortas (AAs). We investigate the correlation between the estimated parameters for each constituent and the patient's age and gender; however, the results indicate that the parameters are correlated with age only. The parameters are classified into two groups: Group-I in which the parameters are correlated with age, and Group-II in which the parameters are not correlated with age. For the parameters in Group-I, we used regression associated with age via linear or inverse relations, in which their prior distributions provide conditional distributions with confidence intervals. For Group-II, the parameter estimated values were subjected to multiple transformations and chosen if the transformed data had a better fit to the normal distribution than the original. This information improves the prior distribution of a subject-specific model by specifying parameters that are correlated with age and their transformed distributions. Therefore, this study is a necessary first step in our group's approach toward a Bayesian calibration of an aortic model. The results from this study will be used as the prior information necessary for the initialization of Bayesian calibration of a computational model for future applications.
Skip Nav Destination
Article navigation
October 2015
Research-Article
Prior Distributions of Material Parameters for Bayesian Calibration of Growth and Remodeling Computational Model of Abdominal Aortic Wall
Sajjad Seyedsalehi,
Sajjad Seyedsalehi
Department of Mechanical Engineering,
Michigan State University,
East Lansing, MI 48824-1226
Michigan State University,
East Lansing, MI 48824-1226
Search for other works by this author on:
Liangliang Zhang,
Liangliang Zhang
Department of Statistics and Probability,
Michigan State University,
East Lansing, MI 48824-1027
Michigan State University,
East Lansing, MI 48824-1027
Search for other works by this author on:
Jongeun Choi,
Jongeun Choi
Department of Mechanical Engineering,
Department of Electrical and
Computer Engineering,
Michigan State University,
East Lansing, MI 48824-1226
Department of Electrical and
Computer Engineering,
Michigan State University,
East Lansing, MI 48824-1226
Search for other works by this author on:
Seungik Baek
Seungik Baek
Department of Mechanical Engineering,
Michigan State University,
2457 Engineering Building,
East Lansing, MI 48824-1226
e-mail: sbaek@egr.msu.edu
Michigan State University,
2457 Engineering Building,
East Lansing, MI 48824-1226
e-mail: sbaek@egr.msu.edu
Search for other works by this author on:
Sajjad Seyedsalehi
Department of Mechanical Engineering,
Michigan State University,
East Lansing, MI 48824-1226
Michigan State University,
East Lansing, MI 48824-1226
Liangliang Zhang
Department of Statistics and Probability,
Michigan State University,
East Lansing, MI 48824-1027
Michigan State University,
East Lansing, MI 48824-1027
Jongeun Choi
Department of Mechanical Engineering,
Department of Electrical and
Computer Engineering,
Michigan State University,
East Lansing, MI 48824-1226
Department of Electrical and
Computer Engineering,
Michigan State University,
East Lansing, MI 48824-1226
Seungik Baek
Department of Mechanical Engineering,
Michigan State University,
2457 Engineering Building,
East Lansing, MI 48824-1226
e-mail: sbaek@egr.msu.edu
Michigan State University,
2457 Engineering Building,
East Lansing, MI 48824-1226
e-mail: sbaek@egr.msu.edu
1Corresponding author.
Manuscript received January 7, 2015; final manuscript received July 6, 2015; published online August 6, 2015. Assoc. Editor: Jonathan Vande Geest.
J Biomech Eng. Oct 2015, 137(10): 101001 (13 pages)
Published Online: August 6, 2015
Article history
Received:
January 7, 2015
Revision Received:
July 6, 2015
Citation
Seyedsalehi, S., Zhang, L., Choi, J., and Baek, S. (August 6, 2015). "Prior Distributions of Material Parameters for Bayesian Calibration of Growth and Remodeling Computational Model of Abdominal Aortic Wall." ASME. J Biomech Eng. October 2015; 137(10): 101001. https://doi.org/10.1115/1.4031116
Download citation file:
Get Email Alerts
Related Articles
Differential Passive and Active Biaxial Mechanical Behaviors of Muscular and Elastic Arteries: Basilar Versus Common Carotid
J Biomech Eng (May,2011)
Constitutive Modeling of Mouse Arteries Suggests Changes in Directional Coupling and Extracellular Matrix Remodeling That Depend on Artery Type, Age, Sex, and Elastin Amounts
J Biomech Eng (June,2024)
3D Mechanical Properties of the Layered Esophagus: Experiment and Constitutive Model
J Biomech Eng (December,2006)
Age Dependency of the Biaxial Biomechanical Behavior of Human Abdominal Aorta
J Biomech Eng (December,2004)
Related Proceedings Papers
Related Chapters
Introduction to Stress and Deformation
Introduction to Plastics Engineering
In Situ Observations of the Failure Mechanisms of Hydrided Zircaloy-4
Zirconium in the Nuclear Industry: 20th International Symposium
A 3D Cohesive Modelling Approach for Hydrogen Embrittlement in Welded Joints of X70 Pipeline Steel
International Hydrogen Conference (IHC 2012): Hydrogen-Materials Interactions