Methodology for Occupant Head-Neck Injury Testing in Under-Body Blast Impact Based on Virtual-Real Fusion

The high-biofidelity dummy used to evaluate occupant protection under blast conditions is often costly and vulnerable. To address the limitations of low-cost, simplified dummy head–neck structures, which exhibit significant differences in mechanical properties compared to high-biofidelity dummies, a virtual–real fusion-based test method for assessing occupant head–neck injury in under-body blast impacts is proposed. A simplified dummy head–neck physical model, designed based on human biomechanical characteristics, is constructed for physical testing. A mapping test model based on 1D convolutional neural network (1D-CNN) is developed as the virtual counterpart to process physical model test data, specifically head and chest centroid accelerations, into head centroid acceleration and upper neck axial compression force matching the Hybrid III 50th numerical model. Pendulum collision tests are conducted to simulate under-body blast impacts, generating multiple sets of head and chest acceleration data. Under identical loading conditions, the head centroid acceleration and upper neck axial compression force of the Hybrid III 50th numerical model are computed. The parameters of the mapping test model are then optimized using these simulated experimental datasets based on non-dominated sorting genetic algorithm II (NSGA-II). Validation through blast experiments demonstrates that the proposed method achieves high accuracy, with errors of 10.9% for the Head Injury Criterion (HIC15) and 2.4% for upper neck maximum axial compression force compared to the Hybrid III 50th numerical model calculations. This approach effectively bridges the gap between biofidelity and cost-efficiency in dummy testing for blast impact scenarios.