Construction of a risk model through the fusion of experimental data and finite element modeling: Application to car crash-induced TBI. Issue 6 (26th April 2019)

• Record Type: Journal Article
• Authors: Ahmadisoleymani, Seyed Saeed; Missoum, Samy
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 22:Issue 6(2019)
• Page Start: 605
• Page End: 619
• Publication Date: 2019-04-26
• Subjects: Risk model -- Data fusion -- Crash simulations -- Finite element analysis -- Traumatic Brain Injury; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; Biomechanics -- Periodicals; Biomedical Engineering -- methods -- Periodicals; Computing Methodologies -- Periodicals; 612.7
• DOI: 10.1080/10255842.2019.1574343
• Languages: English
• ISSNs: 1025-5842

Abstract

Abstract: This article introduces a new approach for the construction of a risk model for the prediction of Traumatic Brain Injury (TBI) as a result of a car crash. The probability of TBI is assessed through the fusion of an experiment-based logistic regression risk model and a finite element (FE) simulation-based risk model. The proposed approach uses a multilevel framework which includes FE simulations of vehicle crashes with dummy and FE simulations of the human brain. The loading conditions derived from the crash simulations are transferred to the brain model thus allowing the calculation of injury metrics such as the Cumulative Strain Damage Measure (CSDM). The framework is used to propagate uncertainties and obtain probabilities of TBI based on the CSDM injury metric. The risk model from FE simulations is constructed from a support vector machine classifier, adaptive sampling, and Monte-Carlo simulations. An approach to compute the total probability of TBI, which combines the FE-based risk assessment as well as the risk prediction from the experiment-based logistic regression model is proposed. In contrast to previous published work, the proposed methodology includes the uncertainty of explicit parameters such as impact conditions (e.g., velocity, impact angle), and material properties of the brain model. This risk model can provide, for instance, the probability of TBI for a given assumed crash impact velocity.