Volume 27 · Issue 04 · 2024

The toughest challenge in pedestrian traffic accident identification lies in ascertaining injury manners. This study aimed to systematically simulate and parameterize 3 types of craniocerebral injury including impact injury, fall injury, and run-over injury, to compare the injury response outcomes of different injury manners.

Based on the total human model for safety (THUMS) and its enhanced human model THUMS-hollow structures, a total of 84 simulations with 3 injury manners, different loading directions, and loading velocities were conducted. Von Mises stress, intracranial pressure, maximum principal strain, cumulative strain damage measure, shear stress, and cranial strain were employed to analyze the injury response of all areas of the brain. To examine the association between injury conditions and injury consequences, correlation analysis, principal component analysis, linear regression, and stepwise linear regression were utilized.

There is a significant correlation observed between each criterion of skull and brain injury (p < 0.01 in all Pearson correlation analysis results). A 2-phase increase of cranio-cerebral stress and strain as impact speed increases. In high-speed impact (> 40 km/h), the Von Mises stress on the skull was with a high possibility exceed the threshold for skull fracture (100 MPa). When falling and making temporal and occipital contact with the ground, the opposite side of the impacted area experiences higher frequency stress concentration than contact at other conditions. Run-over injuries tend to have a more comprehensive craniocerebral injury, with greater overall deformation due to more adequate kinetic energy conduction. The mean value of maximum principal strain of brain and Von Mises stress of cranium at run-over condition are 1.39 and 403.8 MPa, while they were 1.31, 94.11 MPa and 0.64, 120.5 MPa for the impact and fall conditions, respectively. The impact velocity also plays a significant role in craniocerebral injury in impact and fall loading conditions (the p of all F-test < 0.05). A regression equation of the craniocerebral injury manners in pedestrian accidents was established.

The study distinguished the craniocerebral injuries caused in different manners, elucidated the biomechanical mechanisms of craniocerebral injury, and provided a biomechanical foundation for the identification of craniocerebral injury in legal contexts.

Head injury criterion (HIC) companied by a rotation-based metric was widely believed to be helpful for head injury prediction in road traffic accidents. Recently, the Euro-New Car Assessment Program utilized a newly developed metric called diffuse axonal multi-axis general evaluation (DAMAGE) to explain test device for human occupant restraint (THOR) head injury, which demonstrated excellent ability in capturing concussions and diffuse axonal injuries. However, there is still a lack of comprehensive understanding regarding the effectiveness of using DAMAGE for Hybrid III 50th percentile male dummy (H50th) head injury assessment. The objective of this study is to determine whether the DAMAGE could capture the risk of H50th brain injury during small overlap barrier tests.

To achieve this objective, a total of 24 vehicle crash loading curves were collected as input data for the multi-body simulation. Two commercially available mathematical dynamic models, namely H50th and THOR, were utilized to investigate the differences in head injury response. Subsequently, a decision method known as simple additive weighting was employed to establish a comprehensive brain injury metric by incorporating the weighted HIC and either DAMAGE or brain injury criterion. Furthermore, 35 sets of vehicle crash test data were used to analyze these brain injury metrics.

The rotational displacement of the THOR head is significantly greater than that of the H50th head. The maximum linear and rotational head accelerations experienced by H50th and THOR models were (544.6 ± 341.7) m/s², (2468.2 ± 1309.4) rad/s² and (715.2 ± 332.8) m/s², (3778.7 ± 1660.6) rad/s², respectively. Under the same loading condition during small overlap barrier (SOB) tests, THOR exhibits a higher risk of head injury compared to the H50th model. It was observed that the overall head injury response during the small overlap left test condition is greater than that during the small overlap right test. Additionally, an equation was formulated to establish the necessary relationship between the DAMAGE values of THOR and H50th.

If H50th rather than THOR is employed as an evaluation tool in SOB crash tests, newly designed vehicles are more likely to achieve superior performance scores. According to the current injury curve for DAMAGE and brain injury criterion, it is highly recommended that HIC along with DAMAGE was prioritized for brain injury assessment in SOB tests.

With the increasing level of automation in automobiles, the advent of autonomous vehicles has reduced the tendency of drivers and passengers to focus on the task of driving. The increasing automation in automobiles reduced the drivers' and passengers' focus on driving, which allowed occupants to choose a more relaxed and comfortable sitting position. Meanwhile, the occupant's sitting position went from a frontal, upright position to a more relaxed and reclined one, which resulted in the existing restraint systems cannot to keep occupants safe and secure. This study aimed to determine the effects of different reclining states on occupants' lumbar and neck injuries.

This is an original research on the field of automotive safety engineering. Occupants in different initial sitting positions (25°, 35°, 45°, and 55°) were adapted to changes in seat back angle and restraint systems and placed in the same frontal impact environment. Neck injury indexes, lumbar axial compression force and acceleration, as well as occupant dynamic response during the impact, were compared in different sitting positions. The injury response and kinematic characteristics of occupants in different reclining positions were analyzed by the control variable method.

As the sitting angle increased, the occupant's head acceleration decreased, and the forward-lean angle decreased. Occupants in the standard sitting position had the greatest neck injury, with an N_ij of 0.95, and were susceptible to abbreviated injury scale 2+ cervical medullary injuries. As the seatback angle increased, the geometric position of the lumbar spine tended to be horizontal, and the impact load transmitted greater forces to the lumbar spine. The occupant's lumbar injury was greatest in the lying position, with a peak axial compression force on the lumbar region of 5.5 KN, which was 2.3 KN greater than in the standard sitting position.

The study of occupant lumbar and neck injuries based on different recline states can provide a theoretical basis for optimizing lumbar evaluation indexes, which is conducive to the understanding of the lumbar injury mechanism and the comprehensive consideration of occupant safety protection.

The present study aimed to analyze the influence of muscle activation on lumbar injury under a specific +Gz load.

A hybrid finite element human body model with detailed lumbar anatomy and lumbar muscle activation capabilities was developed. Using the specific +Gz loading acceleration as input, the kinematic and biomechanical responses of the occupant's lower back were studied for both activated and deactivated states of the lumbar muscles.

The results indicated that activating the major lumbar muscles enhanced the stability of the occupant's torso, which delayed the contact between the occupant's head and the headrest. Lumbar muscle activation led to higher strain and stress output in the lumbar spine under +Gz load, such as the maximum Von Mises stress of the vertebrae and intervertebral discs increased by 177.9% and 161.8%, respectively, and the damage response index increased by 84.5%.

In both simulations, the occupant's risk of lumbar injury does not exceed 10% probability. Therefore, the activation of muscles could provide good protection for maintaining the lumbar spine and reduce the effect of acceleration in vehicle travel direction.

The current study aimed to assess the protective performance of helmets equipped with multi-directional impact protection system (MIPS) under various oblique impact loads.

Initially, a finite element model of a bicycle helmet with MIPS was developed based on the scanned geometric parameters of an actual bicycle helmet. Subsequently, the validity of model was confirmed using the KASK WG11 oblique impact test method. Three different impact angles (30°, 45°, and 60°) and 2 varying impact speeds (5 m/s and 8 m/s) were employed in oblique tests to evaluate protective performance of MIPS in helmets, focusing on injury assessment parameters such as peak linear acceleration (PLA) and peak angular acceleration (PAA) of the head.

The results demonstrated that in all impact simulations, both assessment parameters were lower during impact for helmets equipped with MIPS compared to those without. The PAA was consistently lower in the MIPS helmet group, whereas the difference in PLA was not significant in the no-MIPS helmet group. For instance, at an impact velocity of 8 m/s and a 30° inclined anvil, the MIPS helmet group exhibited a PAA of 3225 rad/s² and a PLA of 281 g. In contrast, the no-MIPS helmet group displayed a PAA of 8243 rad/s² and a PLA of 292 g. Generally, both PAA and PLA parameters decreased with the increase of anvil angles. At a 60° anvil angles, PAA and PLA values were 664 rad/s² and 20.7 g, respectively, reaching their minimum.

The findings indicated that helmets incorporating MIPS offer enhanced protection against various oblique impact loads. When assessing helmets for oblique impacts, the utilization of larger angle anvils and rear impacts might not adequately evaluate protective performance during an impact event. These findings will guide advancements in helmet design and the refinement of oblique impact test protocols.

Under-foot impact loadings can cause serious lower limb injuries in many activities, such as automobile collisions and underbody explosions to military vehicles. The present study aims to compare the biomechanical responses of the mainstream vehicle occupant dummies with the human body lower limb model and analyze their robustness and applicability for assessing lower limb injury risk in underfoot impact loading environments.

The Hybrid III model, the test device for human occupant restraint (THOR) model, and a hybrid human body model with the human active lower limb model were adopted for under-foot impact analysis regarding different impact velocities and initial lower limb postures.

The results show that the 2 dummy models have larger peak tibial axial force and higher sensitivity to the impact velocities and initial postures than the human lower limb model. In particular, the Hybrid III dummy model presented extremely larger peak tibial axial forces than the human lower limb model. In the case of minimal difference in tibial axial force, Hybrid III's tibial axial force (7.5 KN) is still 312.5% that of human active lower limb's (2.4 KN). Even with closer peak tibial axial force values, the biomechanical response curve shapes of the THOR model show significant differences from the human lower limb model.

Based on the present results, the Hybrid III dummy cannot be used to evaluate the lower limb injury risk in under-foot loading environments. In contrast, potential improvement in ankle biofidelity and related soft tissues of the THOR dummy can be implemented in the future for better applicability.

Road traffic accidents pose a global challenge with substantial human and economic costs. Iran experiences a high incidence of road traffic injuries, leading to a significant burden on society. This study aims to predict the future burden of road traffic injuries in Iran until 2030, providing valuable insights for policy-making and interventions to improve road safety and reduce the associated human and economic costs.

This analytical study utilized time series models, specifically autoregressive integrated moving average (ARIMA) and artificial neural networks (ANNs), to predict the burden of road traffic accidents by analyzing past data to identify patterns and trends in Iran until 2030. The required data related to prevalence, death, and disability-adjusted life years (DALYs) rates were collected from the Institute for Health Metrics and Evaluation database and analyzed using R software and relevant modeling and statistical analysis packages.

Both prediction models, ARIMA and ANNs indicate that the prevalence rates (per 100,000) of all road traffic injuries, except for motorcyclist road injuries which have an almost flat trend, remaining at around 430, increase by 2030. Based on estimations of both models, the rates of death and DALYs due to motor vehicle and pedestrian road traffic injuries decrease. For motor vehicle road injuries, estimated trends decrease to approximately 520 DALYs and 10 deaths. Also, for pedestrian road injuries these rates reached approximately 300 DALYs and 6 deaths, according to the models. For cyclists and other road traffic injuries, the predicted DALY rates by the ANN model increase to almost 50 and 8, while predictions conducted by the ARIMA model show a static trend, remaining at 40 and approximately 6.5. Moreover, these rates for the prediction of death rate by the ANN model increased to 0.6 and 0.1, while predictions conducted by the ARIMA model show a static trend, remaining at 0.43 and 0.07. According to the ANN model, the predicted rates of DALY and death for motorcyclists decrease to 100 and approximately 2.7, respectively. On the other hand, predictions made by the ARIMA model show a static trend, with rates remaining at 200 and approximately 3.2, respectively.

The prevalence of road traffic injuries is predicted to increase, while the death and DALY rates of road traffic injuries show different patterns. Effective intervention programs and safety measures are necessary to prevent and reduce road traffic accidents. Different interventions should be designed and implemented specifically for different groups of pedestrians, cyclists, motorcyclists, and motor vehicle drivers.