Diffuse axon injury is a common trauma that affects the axons in the brain's white matter. Computational models of axons, both in isolation and within the matrix, have been developed to study this injury at cellular and tissue levels. However, axonal behaviour depends strongly on the mechanical properties of the surrounding matrix. Accurate material properties of axons and the matrix are essential for realistic modelling of their behaviour. This study characterises the hyper-viscoelastic properties of axons and their matrix for human brain tissue in two different white matter regions. First, previous experimental data on isolated axons under tension were used to determine their mechanical properties. Then, employing finite element analysis, neural networks, and optimisation methods, matrix properties were inferred using experimental data on human brain tissue behaviour under three shear modes at large deformations and varying strain rates. The results indicate that axons are approximately 10–13 times stiffer than the surrounding matrix, depending on the region. The material properties defined in this study provide an accurate representation of axonal and matrix behaviour under injurious conditions, as they are based on large-strain and high-strain-rate data. The constitutive model can be used for a more precise assessment of the injury threshold and the mechanisms of diffuse axon injury at the cellular level.

Fall accidents among older adults represent a major public health challenge worldwide. Energy-absorbing flooring has gained increasing attention due to its high usability and robustness against fall-related hip impacts. This study proposes a novel method that integrates finite element (FE) analysis, deep learning(DL) models, and multi-objective optimization (MOO) algorithms to enhance the biomechanical protective performance of a bio-inspired energy-absorbing structure. To achieve this, 100 structural configurations were generated based on a design of experiments (DOE) framework, automatically modeled in Hypermesh, and integrated into a hip regional model in LS-DYNA. The deep neural network (DNN) models were developed to predict femoral neck force (𝐹𝑛𝑒𝑐𝑘) and energy absorption efficiency (𝑆𝐸𝐴𝑣), and were subsequently utilized in the MOO framework to construct the Pareto front, optimizing the dual objectives of minimizing 𝐹𝑛𝑒𝑐𝑘 and maximizing 𝑆𝐸𝐴𝑣 using 50,000 optimization samples. Five optimal solutions on the Pareto front were validated and demonstrated substantial performance improvements. Compared to the baseline structure, the optimized designs demonstrated up to a 23% reduction in peak femoral neck force and a 65% increase in energy absorption efficiency. This study presents a framework that ensures high accuracy, robustness, and continuity in representing biomechanical responses towards improved hip protection. The findings have practical implications for enhancing safety in high fall-risk environments and provide valuable guidance for manufacturers in designing protective devices with enhanced performance and clinical effectiveness.

Brain deformation caused by a head impact leads to traumatic brain injury (TBI). The maximum principal strain (MPS) was used to measure the extent of brain deformation and predict injury, and the recent evidence has indicated that incorporating the maximum principal strain rate (MPSR) and the product of MPS and MPSR, denoted as MPS × SR, enhances the accuracy of TBI prediction. However, ambiguities have arisen about the calculation of MPSR. Two schemes have been utilized: one is to use the time derivative of MPS (MPSR1), and another is to use the first eigenvalue of the strain rate tensor (MPSR2). Both MPSR1 and MPSR2 have been applied in previous studies to predict TBI. To quantify the discrepancies between these two methodologies, we compared them across eight in-vivo and one in-silico head impact datasets and found that 95MPSR1 was slightly larger than 95MPSR2 and 95MPS × SR1 was 4.85 % larger than 95MPS × SR2 in average. Across every element in all head impacts, the average MPSR1 was 12.73 % smaller than MPSR2, and MPS × SR1 was 11.95 % smaller than MPS × SR2. Furthermore, logistic regression models were trained to predict TBI using MPSR (or MPS × SR), and no significant difference was observed in the predictability. The consequence of misuse of MPSR and MPS × SR thresholds (i.e. compare threshold of 95MPSR1 with value from 95MPSR2 to determine if the impact is injurious) was investigated, and the resulting false rates were found to be around 1 %. The evidence suggested that these two methodologies were not significantly different in detecting TBI.

The development of autonomous vehicle platoons will lead to new accident patterns. Insufficient research exists on occupant injury and protection associated with this new type of accident. To provide a reference for the research and technological development of occupant protection in autonomous vehicle platoon collisions, a continuous crash accident scenario involving a typical three-car autonomous vehicle platoon under high-speed conditions was utilized to determine boundary conditions such as impact time and speed. A full-scale finite element simulation was conducted to obtain the driver kinematics and injury response in each collision condition, and driver injury risk in the autonomous vehicle platoon collision scenarios was analyzed. The results show that although the risk of skull fracture is less than 1%, the risk of severe craniocerebral injury is significant, with the highest predicted risk of AIS 3+ using the BrIC criterion reaching 70.2%. Owing to excessive forward bending and backward extension of the cervical spine, three types of ligaments are at risk of serious injury. Furthermore, the risk of chest rib fracture is relatively low, whereas the risk of viscera damage is contingent on the collision sequence. When the middle car first experiences a frontal collision and is then rear-ended, the maximum principal strain on the driver's heart and liver far exceeds the damage threshold of 0.3, resulting in significant damage risk. Conversely, when the middle car is rear-ended and then collides with the front car, the maximum principal strain on the driver's internal organs is less than 0.3, resulting in a low overall damage risk.

Vulnerable road users (VRUs) face high risks of injury and death from traffic accidents. The current VRU head protection program relies on a single head impact velocity and injury assessment criteria that fail to account for brain tissue strain. This limitation affects the effectiveness of simulating real-world impacts and the accuracy of head injury risk assessments. In this study, the VRU head impact boundary conditions were extracted based on the reconstruction of 40 real-world pedestrian VRU head impacts. Using the Total Human Model for Safety (THUMS) head finite element model and headform impactor, this study explored the effects of real head impact boundary conditions on head kinematics and injury under procedural test scenarios and compared these conditions with test procedure scenarios. The results indicate that the peak linear acceleration in the current test procedure scenarios is higher; however, the peak rotational velocity is significantly lower than that observed in real-world accidents. Different impact locations have significant effects on the head kinematics and injury response parameters, particularly in stiffer areas, such as the windshield edges and lower right corner, where the injury risk under regulatory conditions is higher than that in real accident cases. In contrast, the opposite is true in other windshield areas. This study suggests that future programs or virtual assessments should diversify the head impact boundaries and injury assessment criteria to consider the differences in impact locations and the effects of head rotation on brain tissue injuries. For most windshields (non-edge areas), increasing the linear velocity enhances head rotation, and rotational injury assessment criteria should therefore be introduced. For future virtual assessments, injury criteria based on brain tissue strain should be used to assess VRU head injury risk in real accidents more comprehensively and accurately.

Head injuries constitute an increasing public health concern. Although motor vehicle injuries have steadily declined in Sweden, the number of injuries and fatalities among unprotected vulnerable road users (VRUs) continues to rise. Inspired by playground surfaces, rubberized asphalts have recently been developed as impact-absorbing pavement (IAP), with the dual objectives of enhancing collective safety and minimizing injuries, while promoting higher-value applications within the waste hierarchy. This study aims to biomechanically evaluate IAP's head injury protection performance by conducting laboratory oblique impact tests to obtain impact kinematics and finite element (FE) simulations to estimate brain strain responses. A total of 30 impact tests were performed on five kinds of asphalt samples under three impact locations. Eleven kinematics-based and five strain-based head injury metrics were analyzed and compared. For example, the peak linear acceleration (PLA), peak angular velocity (PAV), and max principal strain (MPS) were lower than 150 g, 36 rad/s, and 0.4 during oblique impact against the IAP prototype. The results demonstrated that the IAP achieved a comparable head protection performance to helmets, indicated by both the linear-based and rotational-based head injury metrics at 6 m/s. These findings show that IAP has significant potential to reduce head injuries among unprotected VRUs and contribute to a safer traffic environment.

With the rapid growth in the number of electric vehicles equipped with advanced consumer electronics, the rate of impact accidents has also been rising year by year. Side pole impact tests are an important method for evaluating the collision safety of these modern electric vehicles. The purpose of this study is to gain industry insights into the kinematics and injury risk for far-side occupants in electric vehicle side pole impacts. This study uses a full-scale finite element model of an electric vehicle and a human body finite element model to conduct an in-depth analysis of the occupant's kinematic response and the risk of injury to the head, neck, chest, and internal organs under various conditions by changing the relative position and impact angle between the rigid pole and the vehicle. The results show that the seatbelt fails to effectively restrict the upper body movement of the occupants, leading to the occupants slipping out of the seatbelt; the position of the impact significantly affects the injury risk to the occupants, with the highest probability of injury occurring during an A pillar impact and a lowest probability during a C pillar impact. In 28%-40% of the cases, the risk of far-side occupants sustaining serious head and brain abbreviated injury scale AIS 3+ injuries exceeds 40%, and in 22% of the cases, the probability of occupants sustaining diffuse axonal injuries based on which metric is higher than 40%; there is no correlation between the head injury criterion HIC15 and the impact angle, but a weak correlation exists between HIC15 and maximum principal strain (MPS); a strong positive correlation is found between the impact angle and brain injury criterion BrIC/MPS. The predicted MPS of nearly 40% and 80% of the far-side occupants' anterior longitudinal ligament and posterior longitudinal ligament exceeds the injury threshold, respectively, while in all cases, the predicted MPS of the occupants' capsular ligament and interspinous ligament exceeds the injury threshold, which indicates an extremely high risk of ligament injury. The peak strains of the internal organs of all far-side occupants exceed the threshold, indicating that the occurrence of these internal organ injuries mainly stems from a viscous mechanism, and the peak strains have a strong positive correlation with the impact angle.

Traumatic brain injury (TBI) is a brain dysfunction caused by an external mechanical force and is a leading cause of disability worldwide. In traumatic brain injury, the brain strain is driven by inertial force associated with the head acceleration. We identified three distinct mechanisms by which inertial forces induce brain strain: the global rotation effect, the global translation effect, and the local force effect. The global rotation and translation effects arise from whole-brain movement relative to the skull, produce brain strain through shearing, pushing and pulling, respectively. In contrast, the local force effect refers to the strain produced inside the brain by the local force without the whole brain movement. The effects are produced by different inertial force components: Euler force (angular acceleration) produces brain strain by the global rotation effect, the linear force (linear acceleration) produces brain strain by the global translation effect, and the centrifugal force (angular velocity) produces brain strain by the local force effect. Although inertial force components are well recognized, their individual contributions to brain strain during head impacts remain unclear. In this study, we applied impact loading by each inertial force component independently in the simulation, rather than by head accelerations where all components act together, with the aim of quantifying their distinct contributions and clarifying the conditions under which Holbourn's hypothesis applies. We found that 97 % of the total MPS was produced by the Euler force in American football head impacts. However, when the range of head kinematics was deliberately extended beyond typical sports impacts to simulate extreme scenarios, such as those potentially occurring in aviation or high-impact accidents, both linear and centrifugal forces were also found capable of producing significant brain strain, highlighting clear biomechanical conditions under which Holbourn's hypothesis is insufficient. Furthermore, we estimated the independent kinematic thresholds for producing brain strain at injury-relevant levels and found that most injurious head impacts are consistently associated with angular accelerations exceeding these thresholds, while corresponding linear accelerations and angular velocities remain below them.

Finite element (FE) models of the human head are important injury assessment tools but developing a high-quality, hexahedral-meshed FE head model without compromising geometric accuracy is a challenging task. Important brain features, such as the cortical folds and ventricles, were captured only in a handful of FE head models that were primarily developed from two meshing techniques, i.e., surface-based meshing with conforming elements to capture the interfacial boundaries and voxel-based meshing by converting the segmented voxels into elements with and without mesh smoothing. Despite these advancements, little knowledge existed of how similar the strain responses were between surface- and voxel-based FE head models. This study uniquely addressed this gap by presenting three anatomically detailed models - a surface-based model with conforming meshes to capture the cortical folds-subarachnoid cerebrospinal fluid and brain-ventricle interfaces, and two voxel-based models (with and without mesh smoothing) - derived from the same imaging dataset. All numerical settings in the three models were exactly the same, except for the meshes. These three models were employed to simulate head impacts. The results showed that, when calculating commonly used injury metrics, including the percentile strains below the maximum (e.g., 99 percentile strain) and the volume of brain element with the strain over certain thresholds, the responses of the three models were virtually identical. Different strain patterns existed between the surface- and the voxel-based models at the interfacial boundary (e.g., sulci and gyri in the cortex, regions adjacent to the falx and tentorium) with strain differences exceeding 0.1, but remarkable similarities were noted at the non-interfacial region. The mesh smoothing procedure marginally reduced the strain discrepancies between the voxel- and surface-based model. This study yielded new quantitative insights into the general similarity in the strain responses between the surface- and voxel-based FE head models and underscored that caution should be exercised when using the strain at the interface to predict injury.