Development of a Computational Framework for Blast-Induced Brain Injury Risk Assessment 7 views

Blast-induced traumatic brain injury (bTBI) has emerged as one of the most pressing challenges in modern military medicine, representing a major cause of morbidity among Soldiers exposed to explosive environments. Despite extensive research, accurate prediction and mitigation of bTBI remain limited by the scarcity of human blast exposure data, the inadequacies of cross-species injury scaling, and the computational expense of high-fidelity simulations. This dissertation addresses these limitations by developing a computationally efficient, multi-scale framework for the prediction and assessment of blast-induced traumatic brain injury in humans. The research is organized into three core aims. Aim 1 focuses on the development and validation of a high-fidelity finite element (FE) model of the human head coupled with a two-stage simulation pipeline that integrates a short-duration Arbitrary Lagrangian-Eulerian (ALE) simulation to capture the complex interaction of blast waves and the head model, followed by pure Lagrangian continuation using pressure-mapped boundary conditions to simulate long-duration brain tissue deformation and head kinematics. This hybrid approach significantly reduces computational cost while maintaining predictive accuracy, enabling the exploration of both short- and long-duration blast responses. Aim 2 bridges the translational gap between animal and human injury data by employing an anatomically accurate ferret head FE model to construct probabilistic injury risk functions, enabling mappings from blast inputs to brain tissue response and injury likelihood. Aim 3 integrates these models and functions into a comprehensive, scalable bTBI risk assessment platform. This platform supports injury risk prediction across realistic battlefield blast scenarios and incorporates a reduced-order multibody surrogate model for rapid risk assessment. The framework is implemented as a MATLAB-based toolbox designed to facilitate efficient risk estimation for both research and operational use. Collectively, this work advances the state of research by establishing a computationally efficient and biomechanical response-based framework for bTBI prediction. The developed models and methodologies enhance the ability to translate experimental findings into human-relevant injury criteria, provide new tools for large-scale risk evaluation, and contribute directly to the design and optimization of protective systems. Ultimately, this research lays the foundation for more accurate, accessible, and standardized approaches to blast injury assessment, supporting improved diagnosis, prevention, and mitigation strategies.

PHD (Doctor of Philosophy)

Blast-induced traumatic brain injury; Injury risk assessment; Computational blast modelling

English

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2025-11-24