The probability of traumatic brain injuries based on tissue-level reliability analysis

Area of Science:

• Biomechanics
• Injury Biomechanics
• Automotive Safety Engineering

Background:

• Motor vehicle crashes are a primary cause of traumatic brain injuries (TBIs).
• Current car restraint system evaluations rely on human tolerance data, but its reliability is questioned due to neglected effects and uncertainties.
• Existing risk curves may not accurately reflect TBI probability in real-world crash scenarios.

Purpose of the Study:

• To re-evaluate the current TBI risk curve used in automotive safety.
• To incorporate previously neglected effects, such as rotational forces and tissue-level uncertainties, into the risk assessment.
• To develop a more accurate prediction of TBI probability in frontal crashes.

Main Methods:

• Reliability analysis was employed to determine TBI probability, accounting for various uncertainties.
• Finite element analysis simulated human brain tissue response during frontal crashes.
• Monte Carlo simulations and sensitivity analysis identified key contributing factors to injury risk.

Main Results:

• The study found a significantly higher TBI risk than predicted by current safety standards.
• A new lognormal distribution risk curve was constructed (μLN = 6.5445, SLN = 1.1993).
• Sensitivity analysis highlighted rotational effects and tissue-level uncertainties as primary drivers of increased injury risk.

Conclusions:

• Tissue-level reliability analysis supports rotational effects as a major cause of brain injuries.
• Translational acceleration-based injury metrics alone contain uncertainties, potentially leading to underestimated injury risks.
• Updated risk curves considering rotational dynamics are crucial for accurate TBI prediction and enhanced vehicle safety.