Machine learning model to predict Chest Injury during Thorax Impact on Human Body Models

In recent years, virtual models have been handy in predicting potential injury risk to occupants in vehicle crashes. Virtual models offer detailing of occupant anthropometry and closest possible bio-fidelity over existing test devices. This study focuses on the assessment of chest deflections in frontal thorax impacts using virtual human body models of a few anthropometries and transforming the assessment of injuries of broader range of anthropometries (population). The study utilizes machine learning models to enable injury assessment across a broader range of body types. The study has taken a standard test scenario (Kroell load case) having frontal blunt thoracic impact. The load case is replicated in LS-DYNA and finite element human body models (HBMC) of 05th Female and 50th Male are used. The simulation setup replicates the boundary conditions as per literature, the responses necessary to measure chest deflection were validated & recorded. The outputs from the simulations and data from literature (PMHS) are used to train multiple algorithms to generate supervised machine learning models. The combination of virtual simulation and machine learning reduces the reliance on physical prototypes and expands the reach of chest injury prediction for various populations. It provides a scalable, time-efficient approach for estimating injury risk and to protect a more diverse range of occupants. By integrating advanced simulation with data-driven modelling, this study offers a practical framework for evaluating chest injury risk in a wider population. The study highlights and demonstrates how predictive algorithms can enhance the versatility of simulation outcomes. Future work will explore expanding this approach to other impact types and body configurations, including vulnerable and underrepresented population groups.