Blunt aortic injury in vehicle crash accident may occur at any moment of the cardiac cycle. However, there is currently a lack of in-depth understanding within the industry regarding the effects of hemodynamic characteristics at different physiological stages on the mechanical response of the aorta under seatbelt restraint loading. Based on a heart-aorta finite element system, this study developed a physiologically characteristics fluid-structure interaction (FSI) model and validated its hemodynamic simulation reliability. On this basis, a stepwise modeling strategy was employed to establish a thorax-enclosed visceral organs FSI model under seatbelt restraint loading. Numerical simulation analyses of different physiological stages of the heart during systole (early, mid, and late) were conducted by varying the boundary conditions of left ventricular motion displacement and aortic outlet pressure. The results indicate that, during the rapid ejection phase (mid-systole), there is a higher risk of aortic injury under seatbelt restraint loading. During the rapid ejection phase, the aortic arch region exhibits a peak strain of 0.148 at 0.12 s, representing 9.4% and 6.1% increases compared to early-systole and late-systole phases, respectively. The peak blood pressure at the outlet of the descending aorta reaches 18.31 kPa at 0.08 s, which is 14.7% and 9.6% higher than that in the early and late systole, respectively. The established thorax-viscera integrated FSI model under seatbelt restraint loading demonstrates the capability to simulate hemodynamic responses of the aorta under complex loading conditions, providing a technical reference for analyzing physiological state-dependent aortic injury mechanisms.

A model for predicting the risks of pedestrian injuries after accidents based on clustering method and back-propagation neural network was proposed to study the factors that affect pedestrian injuries in car-pedestrian collision accidents. Firstly, the data of 372 car-pedestrian collision accidents between 2018 and 2019 in the National Vehicle Accident In-depth Investigation System (NAIS) database was collected as the research object. And it was statistically analyzed to obtain 9 accident characteristic parameters in three dimensions of vehicle, pedestrian and collision status. Then, combined with the characteristics of each accident, the K-means clustering method was selected for continuous eigenvalues, and the hierarchical clustering method was selected for discrete eigenvalues to obtain the correlation between pedestrian injury and death risks and various characteristic parameters. Finally, a BP neural network prediction model based on accident characteristics was established to predict pedestrian injuries and deaths. The test results show that the success rate of the pedestrian casualty risk prediction model is 86%.