### 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.

Original language | English (US) |
---|---|

Pages (from-to) | 605-619 |

Number of pages | 15 |

Journal | Computer Methods in Biomechanics and Biomedical Engineering |

Volume | 22 |

Issue number | 6 |

DOIs | |

State | Published - Apr 26 2019 |

### Fingerprint

### Keywords

- Crash simulations
- Data fusion
- Finite element analysis
- Risk model
- Traumatic Brain Injury

### ASJC Scopus subject areas

- Bioengineering
- Biomedical Engineering
- Human-Computer Interaction
- Computer Science Applications

### Cite this

*Computer Methods in Biomechanics and Biomedical Engineering*,

*22*(6), 605-619. https://doi.org/10.1080/10255842.2019.1574343

**Construction of a risk model through the fusion of experimental data and finite element modeling : Application to car crash-induced TBI.** / Ahmadisoleymani, Seyed Saeed; Missoum, Samy.

Research output: Contribution to journal › Article

*Computer Methods in Biomechanics and Biomedical Engineering*, vol. 22, no. 6, pp. 605-619. https://doi.org/10.1080/10255842.2019.1574343

}

TY - JOUR

T1 - Construction of a risk model through the fusion of experimental data and finite element modeling

T2 - Application to car crash-induced TBI

AU - Ahmadisoleymani, Seyed Saeed

AU - Missoum, Samy

PY - 2019/4/26

Y1 - 2019/4/26

N2 - 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.

AB - 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.

KW - Crash simulations

KW - Data fusion

KW - Finite element analysis

KW - Risk model

KW - Traumatic Brain Injury

UR - http://www.scopus.com/inward/record.url?scp=85065785316&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85065785316&partnerID=8YFLogxK

U2 - 10.1080/10255842.2019.1574343

DO - 10.1080/10255842.2019.1574343

M3 - Article

C2 - 30773915

AN - SCOPUS:85065785316

VL - 22

SP - 605

EP - 619

JO - Computer Methods in Biomechanics and Biomedical Engineering

JF - Computer Methods in Biomechanics and Biomedical Engineering

SN - 1025-5842

IS - 6

ER -