Method for evaluating brain tissue injury risk based on von mises stress peak value and brain tissue mps peak value of human brain tissue and application thereof
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- TIANJIN UNIV OF SCI & TECH
- Filing Date
- 2026-03-09
- Publication Date
- 2026-06-09
Smart Images
Figure CN122177447A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of computer numerical simulation and human injury biomechanics research technology, and relates to a method and its application for assessing brain tissue injury risk based on the von Mises stress peak and MPS peak of human brain tissue. Background Technology
[0002] Globally, approximately 69 million people suffer from traumatic brain injury (TBI) each year, with about 75% experiencing mild traumatic brain injury (mTBI), also known as concussion. Traffic accidents are a significant cause of TBI in children and adolescents. Research indicates that adolescents' immature central nervous system, larger head-to-body ratio, thinner skull, larger subarachnoid space, and changes in cerebral blood volume are all potential risk factors unique to adolescents, making them more susceptible to brain injury compared to young adults. Furthermore, anthropometric dimensions and sex are significantly correlated with concussion incidence, and impact-induced brain strain is a major cause of brain injury, including mTBI. While kinematic parameters have been studied as predictors of TBI for decades, head kinematic data alone cannot directly correlate with tissue-level brain impact responses. The industry generally believes that using validated head models to simulate impacts and translate external impact kinematics into tissue-level impact responses (such as stress and strain) to more accurately predict traumatic brain injury is an effective way to assess the risk of brain tissue damage.
[0003] Compared to adolescents and young adults, children have a higher head-to-body ratio and less mature development of their central nervous system and skull, making them more susceptible to head and brain injuries during impact responses. Furthermore, children's neck bones and muscles are still developing, significantly reducing the restraining effect of the neck structure on the head. In traffic accidents, head and neck flexion is more pronounced in children, increasing the probability of head collisions with the vehicle interior or other occupants. Therefore, children are more prone to head and neck injuries due to the high acceleration of motor vehicles in traffic accidents. To better characterize head injury risk, many scholars have established a functional relationship between head kinematic parameters and the Abbreviated Injury Scale (AIS) based on head impact tests. However, the correlation between head motion loads and tissue-level injury risk, as well as the injury risk curve, remains unclear, and studies have shown that kinematic parameters cannot effectively reflect the "brain strain" and "brain stress" generated by relative brain tissue motion. Therefore, assessing brain tissue injury risk through peak von Mises stress (VMS) and peak MPS (maximum stress) of brain tissue is of great significance. Summary of the Invention
[0004] To address the shortcomings of the aforementioned technologies, the present invention aims to provide a method for assessing brain tissue injury risk based on the von Mises stress peak and MPS peak of human brain tissue, the steps of which include:
[0005] Step A: Select a human finite element model with head finite element simulation capabilities, and construct the von Mises stress peak and MPS peak values of the brain tissue, respectively, and then compare them with the head linear injury index (HIC). 15 The correlation between ) and the head rotation injury index (BrIC) includes the following sub-steps:
[0006] Step A1: Construct a system based on the Random Forest (RF) algorithm, with HIC as the input feature. 15 And BrIC, a brain tissue von Mises stress peak prediction model whose output feature is the brain tissue von Mises stress peak. The constructed random forest algorithm is used to calculate the model's coefficient of determination (R²) using a 10x cross-validation method. 2 ), to ensure that the prediction model does not overfit; if R 2 If the convergence rate is lower than the preset convergence criterion (preferably, the convergence criterion is R²≥0.9), the number of decision trees and the maximum depth parameter of the random forest algorithm are fine-tuned and cross-validation is performed again at a factor of ten until the preset convergence criterion is met.
[0007] Step A2: Construct a system based on the Random Forest algorithm, with HIC as the input feature. 15 And BrIC, a brain tissue MPS peak prediction model whose output feature is the peak value of brain tissue MPS. The constructed random forest algorithm is used to calculate the model's coefficient of determination (R²) using a 10x cross-validation method. 2 ), to ensure that the prediction model does not overfit; if R 2 If the convergence rate is lower than the preset convergence criterion (preferably, the convergence criterion is R²≥0.9), the number of decision trees and the maximum depth parameter of the random forest algorithm are fine-tuned and cross-validation is performed again at a factor of ten until the preset convergence criterion is met.
[0008] Specifically, the original dataset of the prediction model is extracted from the experimental results of the human finite element model under different simulation conditions, and is divided into training set, test set and validation set according to proportion.
[0009] Preferably, the different simulation conditions include multiple different collision conditions, multiple different occupant percentile sizes, multiple different sitting posture angles, multiple different seat rotation angles, and multiple different seat belt force limits.
[0010] Preferably, the original dataset is divided into a training set, a test set, and a validation set in a ratio of 6:2:2.
[0011] Specifically, the tenfold cross-validation refers to randomly allocating the original dataset ten times according to a set ratio, constructing a prediction model using a random forest model for each random allocation, and calculating the ratio of the regression sum of squares to the total sum of squares of deviations from the mean for the validation set under the same parameters, i.e., the model coefficient of determination R. 2 .
[0012] Step B: Using the Permutation Feature Importance algorithm, calculate the importance score of each input feature in the prediction model constructed in Step A, and normalize it to determine the HIC. 15 The weighting of BrIC in predicting peak von Mises stress and peak MPS in brain tissue.
[0013] Step C: Based on the recognized HIC 15 The functional relationship between BrIC and the risk of head injury at simplified injury level 2 (AIS level 2) and simplified injury level 3 (AIS level 3), the original dataset in step A, and the HIC in step B. 15 The weighting of BrIC in predicting peak von Mises stress and peak MPS in brain tissue is as follows: The probability distribution of AIS2 and AIS3 brain tissue injury risk based on the peak von Mises stress in brain tissue is calculated, and the distribution maps of peak von Mises stress - AIS2 injury risk probability and peak von Mises stress - AIS3 injury risk probability are plotted with the peak von Mises stress as the independent variable and the corresponding AIS2 and AIS3 injury risk probabilities as the dependent variables. The probability distribution of AIS2 and AIS3 brain tissue injury risk based on peak MPS is also calculated, and the distribution maps of peak MPS - AIS2 injury risk probability and peak MPS - AIS3 injury risk probability are plotted with the peak MPS as the independent variable and the corresponding AIS2 and AIS3 injury risk probabilities as the dependent variables. This includes the following sub-steps:
[0014] Step C1: According to HIC 15 The functional relationship between BrIC and the risk of head injury at AIS level 2 and AIS level 3 was calculated in the same trial. 15 The corresponding head AIS level 2 and AIS level 3 injury risks and the corresponding head AIS level 2 and AIS level 3 injury risks for BrIC;
[0015] Specifically, the HIC 15The functional relationship between BrIC and the risk of head injury at AIS level 2 and AIS level 3 is as follows:
[0016]
[0017]
[0018]
[0019]
[0020] Step C2: According to the formula P (peak von Mises stress in brain tissue) = a P(HIC 15 ) + b P(BrIC) is used to calculate the risk of AIS2 and AIS3 brain tissue damage corresponding to the peak von Mises stress in the same brain tissue from the same test. The formula is P(peak von Mises stress) = c P(HIC 15 ) + d P(BrIC) calculates the risk of brain injury at AIS grade 2 and AIS grade 3 corresponding to the peak MPS of the same brain tissue in the experiment; where a, b and c, d are HIC values respectively. 15 The weighting of BrIC in the peak von Mises stress and peak MPS in brain tissue;
[0021] Step C3: Using the von Mises stress peak value of brain tissue as the dependent variable, calculate the risk of AIS2 and AIS3 level brain tissue damage and plot the probability distribution map of brain tissue damage based on the von Mises stress peak value; using the MPS peak value of brain tissue as the independent variable, calculate the risk of AIS2 and AIS3 level brain tissue damage and plot the probability distribution map of brain tissue damage based on the MPS peak value.
[0022] Step D: Based on the brain tissue injury risk probability distribution map obtained in C, obtain the functional relationship between the peak von Mises stress of brain tissue and the risk of AIS2 and AIS3 brain tissue injury, and the functional relationship between the peak MPS of brain tissue and the risk of AIS2 and AIS3 brain tissue injury, including the following sub-steps:
[0023] Step D1: Based on the brain tissue damage risk probability distribution map obtained in step C, fit it using linear and sigmoid functions;
[0024] Step D2: Based on the fitting results, calculate the difference between the dependent variable corresponding to the fitted curve and the original data when the independent variables are the same, as well as the coefficient of determination R of the fitted function. 2 If R2 If the convergence criterion is lower than the preset fitting standard (preferably, the convergence standard is R²≥0.8), then the parameters of the fitting function are adjusted and refitted until the preset fitting standard is met, so as to obtain the AIS2 and AIS3 brain tissue injury risk functions corresponding to the peak von Mises stress and peak MPS of brain tissue.
[0025] This invention also proposes an application of a method for assessing brain tissue damage risk based on the von Mises peak stress and MPS peak stress of human brain tissue in automobile crash simulation, which mainly includes the following steps:
[0026] Step 1: Pre-simulation processing: Build a finite element model of the human body and the corresponding simulation environment, and apply boundary conditions according to the preset working conditions;
[0027] Step II: Data Extraction and Calculation: Run the collision simulation and output the peak von Mises stress and peak MPS of the brain tissue. Substitute them into the brain tissue damage risk function to obtain the probability of brain tissue damage risk.
[0028] The method and its application for assessing brain tissue injury risk based on the von Mises stress peak and MPS peak of human brain tissue disclosed in this invention have the following beneficial effects: This invention can overcome the predicament that existing injury risk curves and injury risk functions cannot assess the risk of brain tissue injury, and for the first time extends the probability of head injury risk from the whole to the tissue structure level.
[0029] This invention proposes a method for assessing the risk of brain tissue injury based on the von Mises stress peak and MPS peak of human brain tissue. The independent variables for constructing the brain tissue injury risk curve and injury risk function are the brain tissue injury indicators widely recognized in the field of automotive safety: the von Mises stress peak and MPS peak of brain tissue.
[0030] The present invention proposes a research method for assessing the risk of brain tissue injury based on the von Mises peak stress and MPS peak stress of human brain tissue. Through simulation based on the human finite element model, combined with the brain tissue injury risk curve and injury risk function, the method accurately determines the level of human brain tissue injury and the probability of injury risk, thereby further studying ways to reduce the risk of brain tissue injury. Attached Figure Description
[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0032] Figure 1 This is a flowchart illustrating the method and application of assessing brain tissue damage risk based on the von Mises stress peak and MPS peak of human brain tissue according to the present invention.
[0033] Figure 2 This is a risk distribution map and risk curve of brain tissue AIS2 level injury based on the von Mises stress peak value.
[0034] Figure 3 This is a risk distribution map and risk curve of AIS3 level brain injury based on the von Mises stress peak value of brain tissue.
[0035] Figure 4 This is a risk distribution map and risk curve of brain tissue AIS2 level injury based on the peak MPS of brain tissue.
[0036] Figure 5 This is a risk distribution map and risk curve of brain tissue AIS3 level injury based on the peak MPS of brain tissue.
[0037] Figure 6 This is a schematic diagram of the simulation model of the occupants and constraint system in Embodiment 2 of the present invention.
[0038] Figure 7 This is a schematic diagram of the simulation settings in Embodiment 2 of the present invention.
[0039] Figure 8 HIC in Embodiment 3 of the present invention 15 Damage risk diagram.
[0040] Figure 9 This is a schematic diagram of BrIC damage risk in Embodiment 3 of the present invention.
[0041] Figure 10 This is a schematic diagram illustrating the risk of von Mises stress peak damage in brain tissue in Embodiment 3 of the present invention.
[0042] Figure 11 This is a schematic diagram of the peak MPS damage risk in brain tissue in Embodiment 3 of the present invention. Detailed Implementation
[0043] The invention will be further described in detail below with reference to the specific embodiments and accompanying drawings. Except for the specific details mentioned below, the processes, conditions, and experimental methods for implementing this invention are all common knowledge and general knowledge in the art, and this invention does not have any particular limitations.
[0044] This invention discloses a method for assessing brain tissue injury risk based on the von Mises peak stress and MPS peak stress of human brain tissue. Using the von Mises peak stress and MPS peak stress as a basis, and through the constructed injury risk curve and injury risk function, the method calculates the AIS2 and AIS3 injury risks of human brain tissue under different working conditions. This extends the assessment of human head injury risk from the overall level to the tissue structure level, making the research on the mechanism and risk of head injury more detailed and comprehensive, and providing technical and theoretical support for further comprehensive research on the risk of head and brain tissue injury under different working conditions.
[0045] This invention discloses a method for assessing brain injury risk based on the peak von Mises stress (VMS) and peak MPS (maximum stress level) of human brain tissue. It solves for the VMS and MPS values of brain tissue using a finite element model of the human body with detailed anatomical structure. Random forest algorithm and numerical regression analysis are then used to plot the probability distribution of brain injury risk and construct an injury risk function to assess the risk of brain injury. This provides a basis for exploring the risk and mechanism of brain injury under different working conditions, as well as for comprehensively assessing head injury risk in virtual testing. The method and application of this invention for assessing brain injury risk based on VMS and MPS values enrich head injury risk assessment, extending it from a global perspective to the tissue level, and providing technical support for further research on head injury mechanisms and the reliability of safety restraint systems.
[0046] The method for assessing brain injury risk based on von Mises stress peak and MPS peak of human brain tissue disclosed in this invention mainly includes the following steps: First, constructing von Mises stress peak, MPS peak and HIC of brain tissue respectively. 15 The correlation with BrIC was investigated; secondly, probability distribution maps of brain tissue AIS2 and AIS3 injury risks were plotted based on the peak von Mises stress and peak MPS of brain tissue, respectively; then, based on the probability distribution maps of injury risks, numerical regression analysis was used to fit injury risk curves and injury risk functions and validated them, resulting in four validated injury risk curves and injury risk functions. The specific process is as follows: Figure 1 As shown. The specific steps include:
[0047] (1) von Mises stress peak, MPS peak and HIC in brain tissue 15 Correlation with BrIC determined
[0048] Determining the von Mises peak stress, MPS peak stress, and HIC in brain tissue 15The correlation with BrIC is a core element in mapping the probability distribution of brain tissue injury risk. First, a Random Forest (RF) model is selected to construct the input feature as HIC. 15 The model uses BrIC and its output features to predict the peak von Mises stress and peak MPS of brain tissue, respectively. Secondly, the model's coefficient of determination (R²) is calculated using a 10x cross-validation method on the constructed random forest algorithm. 2 If R 2 If the R-value is less than 0.9, the parameters of the random forest algorithm (number of decision trees and decision tree parameters) are fine-tuned and cross-validation is performed again at a factor of ten until R-values are reached. 2 Greater than or equal to 0.9; if R 2 A value greater than or equal to 0.9 indicates that the existing model has been successfully built. Finally, the contribution of the input features is calculated using the permutation feature importance algorithm to determine their weight proportion in the prediction model. Specific steps include: Initialization preparation: Determine the total number of features and set the number of permutations (to reduce random errors and improve result stability); Calculate importance for each feature: Save the original value of the current feature to avoid subsequent permutation operations from damaging the original data, calculate the baseline error (calculate the mean squared error (MSE) based on the original features and model prediction results, as a benchmark for error comparison); Multiple feature permutations and error calculation: Randomly shuffle (permutate) all values of the current feature, replace them with the feature matrix, use the permuted feature matrix for prediction, calculate the mean squared error after this permutation, accumulate the errors after multiple permutations, and finally calculate the average permutation error; Calculate feature importance score: Use "average permutation error - baseline error" as the importance score of the current feature (the higher the score, the more important the feature), restore the original value of the current feature, and ensure that the calculation of the next feature is based on the original data; Calculate relative importance proportion: Sum the importance scores of all features, divide the score of each feature by the sum, and obtain the importance proportion of each feature (the sum is 1), which facilitates intuitive comparison.
[0049] (2) Draw a probability distribution map of brain tissue injury risk.
[0050] Mapping the probability distribution of brain tissue injury risk is a prospective task for fitting brain tissue injury risk curves. Based on HIC 15 The weighting of BrIC in the peak von Mises stress and peak MPS in brain tissue, and HIC 15The functional relationship between BrIC and the risk of AIS2 and AIS3 brain injury was established. The probabilities of AIS2 and AIS3 brain injury based on von Mises stress and MPS peak values were calculated. Finally, the probability distribution of AIS2 and AIS3 brain injury risk was established based on the von Mises stress peak value. Using the von Mises stress peak value as the independent variable and the corresponding AIS2 and AIS3 injury risk probabilities as the dependent variables, the probability distribution maps of von Mises stress peak value - AIS2 and AIS3 injury risk were plotted. The probability distribution of AIS2 and AIS3 brain injury risk based on MPS peak values was also obtained. Furthermore, using the MPS peak value as the independent variable and the corresponding AIS2 and AIS3 injury risk probabilities as the dependent variables, the probability distribution maps of MPS peak value - AIS2 and AIS3 injury risk were plotted. Figures 2 to 5 As shown. First, according to HIC 15 The weighting of BrIC in the peak von Mises stress (BVMS) and peak MPS (maximum stress) of brain tissue can be used to decompose the brain tissue injury risk corresponding to the peak von Mises stress and peak MPS into linear head injury risk and rotational head injury risk: P(peak von Mises stress) = a P(HIC 15 ) + b P(BrIC), P(peak MPS in brain tissue) = c P(HIC 15 ) + d P(BrIC), where a, b and c, d are HIC values respectively. 15 The weighting of BrIC in the peak von Mises stress and peak MPS in brain tissue; secondly, according to HIC 15 The functional relationship between BrIC and the risk of head injury at AIS level 2 and AIS level 3 can be used to calculate HIC in the same trial. 15 The corresponding head AIS2 and AIS3 injury risks and the head AIS2 and AIS3 injury risks corresponding to BrIC; then, according to the formula P (peak von Mises stress in brain tissue) = a P(HIC 15 ) + b P(BrIC) can be used to calculate the risk of AIS2 and AIS3 brain tissue damage corresponding to the peak von Mises stress in the same brain tissue from the same test. The formula is P(peak von Mises stress) = c P(HIC 15 ) + d P(BrIC) can be used to calculate the risk of AIS2 and AIS3 brain tissue damage corresponding to the MPS peak value of the same experimental brain tissue. Finally, using the von Mises stress peak value of brain tissue and the calculated risk of AIS2 and AIS3 brain tissue damage as the dependent variable, a probability distribution map of brain tissue damage based on the von Mises stress peak value of brain tissue is plotted. Using the MPS peak value of brain tissue and the calculated risk of AIS2 and AIS3 brain tissue damage as the dependent variable, a probability distribution map of brain tissue damage based on the MPS peak value of brain tissue is plotted.
[0051] (3) Construction and verification of brain tissue injury risk curve and injury risk function
[0052] Based on the probability distribution map of brain tissue injury risk and numerical regression analysis, the probability distribution map of brain tissue injury risk was first fitted using linear and sigmoid functions. Secondly, the difference between the dependent variable and the original data corresponding to the fitted curve when the independent variables were the same, as well as the coefficient of determination of the fitted function, were calculated. The parameters of the fitted function were adjusted to ensure that the coefficient of determination of the fitted curve was higher than 0.80. Then, based on the brain tissue injury risk fitting function and the brain tissue von Mises stress peak and MPS peak injury threshold, the difference between the injury risk function prediction result and the injury threshold was calculated. It was then determined that the brain tissue injury prediction curve constructed based on the brain tissue von Mises stress peak and MPS peak had good reliability, thus obtaining the brain tissue injury risk curve and injury risk function based on the brain tissue von Mises stress peak and MPS peak.
[0053] Example
[0054] Example 1
[0055] This embodiment uses relevant data from the TUST IBMs 6YO (T6-year-old children's injury biomimetic model) model as the initial dataset, generates corresponding brain tissue injury risk curves and injury risk functions, and verifies the effectiveness of the functions with actual experimental data.
[0056] The TUST IBMs 6YO model features detailed anatomical structures, and its biomimetic accuracy has been validated through cadaver and volunteer trials. Constructed using hexahedral solid elements and quadrilateral shell elements, the model employs shared nodes, face-to-face contact, and self-contact to connect different tissue structures. The model has 738,000 elements, 751,000 nodes, a height of 116 cm, a sitting height of 63 cm, and a weight of 26.7 kg.
[0057] The original dataset in this embodiment is based on the TUST IBMs 6YO model and car seat and child safety seat models. Different simulation conditions are set, including: different collision conditions (100% frontal collision, 50% frontal overlap offset collision, 25% frontal small offset collision), different child occupant percentile sizes (3%, 25%, 50%, 75%, 97%), different sitting angles (20°, 30°, 45°), different seat rotation angles (0°, 90°, 180°, 270°), and different seat belt limiting forces (2.6kN, 3.6kN, 4.6kN). Simulation experiments are constructed and the test results are extracted to obtain the dataset.
[0058] In this embodiment, the number of decision trees in the random forest algorithm of the brain tissue von Mises stress peak prediction model is 200, and the maximum depth of the decision trees is 42.
[0059] In this embodiment, the number of decision trees in the random forest algorithm of the brain tissue MPS peak prediction model is 100, and the maximum depth of the decision trees is 40.
[0060] In this embodiment, the obtained HIC 15 The weights of BrIC and HIC in the peak von Mises stress of brain tissue were 0.65 and 0.35, respectively; 15 The weights of BrIC and BrIC in the peak MPS of brain tissue were 0.30 and 0.70, respectively.
[0061] In this embodiment, the obtained brain tissue damage risk function is:
[0062] von Mises peak stress in brain tissue:
[0063]
[0064]
[0065] Peak MPS in brain tissue:
[0066]
[0067]
[0068] The obtained brain tissue damage risk function was validated:
[0069] Step A: Based on the functional relationship between the peak von Mises stress of brain tissue and the risk of brain tissue injury at AIS2 and AIS3 levels, and the functional relationship between the peak MPS of brain tissue and the risk of brain tissue injury at AIS2 and AIS3 levels, input the peak von Mises stress and peak MPS injury thresholds of brain tissue from the original dataset to verify the accuracy of the injury risk function.
[0070] Step B: Compare the difference between the predicted damage risk values and damage thresholds based on the peak von Mises stress and peak MPS values of brain tissue. The results show that the brain tissue damage risk function constructed based on the peak von Mises stress and peak MPS values of brain tissue has good reliability. That is, the damage risk function constructed based on the peak von Mises stress of brain tissue can better characterize the degree and risk of brain tissue damage (15kPa, P(AIS2) = 50% vs 15kPa, concussion; 38kPa, P(AIS3) = 50% vs 38kPa, severe brain injury). The damage risk function constructed based on the peak MPS values of brain tissue has high consistency with existing research results (0.15, P(AIS2) = 34% vs 0.15, mild brain injury; 0.20, P(AIS3) = 34% vs 0.20, severe brain injury).
[0071] Example 2
[0072] This embodiment demonstrates the application of the brain tissue damage risk function obtained in Embodiment 1 in automobile crash simulation.
[0073] Occupant head injury indicators primarily focus on kinematic parameters, and assessments of head injury risk are all related to these indicators. However, occupant head kinematic injury indicators cannot effectively assess the risk of brain tissue damage. Therefore, current head injury risk curves cannot accurately assess the degree and risk of injury at the tissue level in the occupant's head. Clearly, this hinders the optimization of occupant restraint systems and the study of injury mechanisms.
[0074] like Figure 6 As shown, the TUST IBMs 6YO model was placed within a finite element model of a car seat to study the brain tissue injury mechanism of occupants in real-world collision accidents. The car seat finite element model was created by 3D scanning of the car seat, meshing it in ANSA 22.0, and assigning appropriate materials and properties. Specific operations included:
[0075] (1) Adopt The INCLUDE_TRANSFORM keyword will make the H-point of the Human Body Model (HBM) coincide with the H-point of the child seat;
[0076] (2) Using the Safety-Seatsquash command in Primer, the HBM model is made to fit completely against the child seat, with no contact force initially generated between them. Based on the relative positions of the HBM model, the car seat, and the car interior, a three-point seat belt is created using the Safety-Seatbelts command in Primer. The seat belt positioning points are adjusted so that the shoulder strap passes sequentially through the D-ring, midpoint of the sternum, and latch, and the lap belt passes sequentially through the latch, anterior superior iliac spine, and anchor point. There is no initial penetration between the HBM model and the three-point seat belt. The seat belt is then assigned corresponding properties, including a seat belt thickness of 1.5mm, a shell element model, and a material selection of... MAT_Seatbelt material;
[0077] (3) Through ANSA The keywords BOUNDARY_PRESCRIBED_ACCELEROMETER_RIGIDI and The INITIAL_VELOCITY keyword applies constraints and boundary conditions to the HBM model and constraint system, and sets the contact between the HBM model and the car seat to ensure the transmission of interaction forces;
[0078] (4) Apply boundary conditions to the simulation model according to the new vehicle evaluation procedure and the crash test requirements of the China Automobile Insurance Association to simulate real vehicle collision scenarios.
[0079] (5) Output the peak von Mises stress and peak MPS of the brain tissue in the HBM model in the simulation test, and calculate the probability of brain tissue damage risk according to the brain tissue damage risk function obtained in Example 1.
[0080] Example 3
[0081] This embodiment provides an application of the brain tissue injury risk function obtained in Embodiment 1 in a study on the correlation between head biomechanical parameters and injury risk in child occupants.
[0082] (1) Simulation test setup
[0083] The finite element model of the car seat and child restraint system used in the simulation experiment was developed and validated by a certain automobile company. Pre-compression commands ensured full contact between the car seat and the child restraint system, and between the child occupant and the child restraint system, with friction coefficients of 0.2 and 0.25, respectively. The child occupant was restrained by an integrated seatbelt, with a seatbelt limiting force of 3.6 kN and a seatbelt pretension force of 2.3 kN. The friction coefficients between the seatbelt and the child restraint system, the seatbelt and the child occupant, and the seatbelt and the car seat were 0.15, 0.2, and 0.2, respectively. To better investigate the impact of the diversity of the cabin environment on the risk of head injury to child occupants in an intelligent driving environment, this study fully considered factors such as collision type, seat back angle, seat rotation angle, child percentile size, and seatbelt limiting force. The boundary conditions of the simulation model were applied according to the frontal collision test requirements of C-NCAP (2024 version) and C-IASI (2024 version), with a gravitational acceleration of 9.8 m / s². 2 .like Figure 7 As shown.
[0084] (2) Evaluation of head injury
[0085] Through HIC 15 The injury risk functions corresponding to BrIC are used to calculate the probability of head injury risk for AIS level 2 and AIS level 3 under different factors, such as... Figure 8 and Figure 9 As shown. Comparative analysis of HIC under different influencing factors. 15 Based on the corresponding AIS2 and AIS3 injury risk probabilities, it can be seen that the impact of collision type, seat back tilt angle, and seat belt limiting force on P(AIS2) is less than that of child percentile size and seat rotation angle. Figure 8 As shown, the risk of head injury decreases with increasing percentile size in children, and there is a statistically significant difference from baseline size. The risk of head injury increases with changes in seat rotation angle compared to the standard seat orientation (Seat Rotation Angle = 0°). When the seat rotation angle is 90° and 270°, P(AIS2) approaches 100%. The trend of P(AIS3) is highly consistent with P(AIS2), but the effect of seat back tilt angle on P(AIS3) is more significant. The probability of AIS2 and AIS3 injury risk corresponding to BrIC under different influencing factors shows that each factor has a significant impact on P(AIS2) and P(AIS3), and there is a statistically significant difference from the baseline mean. Figure 9As shown in the figure, the risk of head injury is negatively correlated with vehicle rotational load and child percentile size, and positively correlated with seat back tilt angle and seat belt limiting force. The risk of head injury is lower when the seat rotation angle is 0° and 180° compared to 90° and 270°. Furthermore, the effect of seat back angle on P(AIS3) is more significant than that on P(AIS2) (11%, 17% vs 17%, 25%). The BrIC-based risk of head injury under different operating conditions does not exceed 40% (P(AIS2)) and 15% (P(AIS3)), respectively.
[0086] (3) Evaluation of brain tissue damage
[0087] Based on the injury risk function constructed in this invention, which uses the peak von Mises stress and peak MPS of brain tissue as independent variables, the probability of AIS2 and AIS3 injury risk of brain tissue under different factors is calculated, as follows: Figure 10 and Figure 11 As shown in the diagram, a comparative analysis of the risk of brain injury caused by von Mises stress revealed that every factor, except for the seatbelt force limiting factor, significantly impacted the risk of brain injury. Increased vehicle collision rotational load and child percentile size reduced the risk of brain injury, but increasing the seat back tilt angle and seat rotation angle increased the risk. Specifically, the risk of head injury was higher at seat rotation angles of 90° and 270°. Figure 10 As shown. A comparative analysis of the risk of brain injury caused by MPS revealed that, except for the child's percentile size and the seat back tilt angle, the effects of other factors on the risk of brain injury were similar to those of von Mises stress. As the child's percentile size increased, the risk of brain injury first decreased and then increased. Increasing the seat back angle led to an increasing trend in P(AIS2), but had no significant effect on P(AIS3). When assessing the risk of head injury based on MPS, P(AIS2) and P(AIS3) were less than 55% and 32% respectively under different factors, as shown. Figure 11 As shown.
[0088] (4) Conclusion
[0089] Analysis of the calculated head injury risk probabilities for AIS2 and AIS3 levels reveals that increasing vehicle rotational load better restricts head rotation in child occupants, thereby reducing the probability of injury caused by rotational load. Increasing the percentile size of the child occupant model allows for a better fit with three-point seat belts, particularly for smaller child occupants (3...). rd Compared to the percentile child occupants (P3), the average and large occupants (50%)... th Percentile P50 to 97 th(Percentile P97) reduces the risk of head injury for child occupants by 5%-20%. Increasing the recline angle provides better protection against linear head movement but weakens restraint against rotational loads. Seat rotation angle is a key factor influencing head injury risk; only when the seat rotation angle is 0° can the child restraint system effectively protect against both linear and rotational head loads.
[0090] Through brain tissue von Mises stress, MPS and HIC 15 The correlation between BrIC and brain tissue reveals that von Mises stress and MPS in brain tissue are generated by the interaction of linear and rotational loads. Therefore, when the seat rotation angle is 180°, the probability of brain tissue injury is much higher than the probability of head rotation injury caused by BrIC. Analysis of the brain tissue AIS2 and AIS3 injury risk probabilities calculated based on the peak von Mises stress and peak MPS in brain tissue shows that linear head load is a significant factor causing head injury in frontal collisions. Furthermore, because von Mises stress is more significantly affected by linear head loads, its assessment of brain tissue injury risk is more consistent with the actual brain tissue injury risk in frontal collisions compared to MPS.
[0091] Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
[0092] As used in this invention, the term "comprising" is an open-ended expression, meaning it includes the contents specified in this invention but does not exclude other aspects.
[0093] As used in this invention, the term "and / or" includes any one or more of the related listed items and all combinations thereof.
[0094] The scope of protection of this invention is not limited to the above embodiments. Any variations and advantages that can be conceived by those skilled in the art without departing from the spirit and scope of the inventive concept are included in this invention and are protected by the appended claims.
Claims
1. A method for assessing brain tissue injury risk based on von Mises peak stress and MPS peak stress in human brain tissue, characterized in that, Includes the following steps: Step A: Select a human finite element model with head finite element simulation capabilities, using the head linear injury index HIC. 15 Using the head rotation injury index BrIC as input features, and the peak von Mises stress and peak MPS of brain tissue as output features, respectively, a prediction model for the peak von Mises stress and peak MPS of brain tissue based on the random forest algorithm is constructed. Step B: Calculate HIC using the permutation feature importance algorithm. 15 The importance scores of BrIC in the brain tissue von Mises stress peak prediction model and the brain tissue MPS peak prediction model were normalized to obtain HIC. 15 The respective weightings of BrIC in predicting peak von Mises stress and peak MPS in brain tissue; Step C: Based on the recognized HIC 15 The functional relationship between BrIC and head injury risk, and the functional relationship between BrIC and head injury risk, combined with the aforementioned HIC 15 The weight ratios of BrIC in predicting the peak von Mises stress and peak MPS of brain tissue are calculated. The AIS2 and AIS3 injury risk probabilities corresponding to the peak von Mises stress and peak MPS of brain tissue for each data point in the human finite element model simulation dataset are calculated. The brain tissue injury risk probability distribution map is plotted with the peak von Mises stress and peak MPS of brain tissue as independent variables and the AIS2 and AIS3 injury risk probabilities as dependent variables. Step D: Based on the brain tissue injury risk probability distribution map obtained in Step C, curve fitting is performed using numerical regression to obtain the AIS2 level brain tissue injury risk function and the AIS3 level brain tissue injury risk function with the peak von Mises stress of brain tissue as the independent variable, as well as the AIS2 level brain tissue injury risk function and the AIS3 level brain tissue injury risk function with the peak MPS of brain tissue as the independent variable.
2. The method for assessing the risk of brain tissue damage as described in claim 1, characterized in that, In step A, the construction of the prediction model includes the following sub-steps: Step A1: Build with HIC 15 A random forest prediction model with BrIC as input features and von Mises stress peak in brain tissue as output features is used. The model's coefficient of determination R² is calculated using a 10x cross-validation method. If R² is lower than the preset convergence criterion, the number of decision trees and the maximum depth parameter of the random forest algorithm are adjusted and 10x cross-validation is performed again until the convergence criterion is met. Step A2: Build with HIC 15 A random forest prediction model with BrIC as input features and the peak MPS of brain tissue as output features is used. The model's coefficient of determination R² is calculated using a 10x cross-validation method. If R² is lower than the preset convergence criterion, the number of decision trees and the maximum depth parameter of the random forest algorithm are adjusted and 10x cross-validation is performed again until the convergence criterion is met.
3. The method for assessing the risk of brain tissue damage as described in claim 2, characterized in that, The preset convergence criterion is R²≥0.
9.
4. The method for assessing the risk of brain tissue damage as described in claim 2, characterized in that, The original dataset of the prediction model is extracted from the simulation results of the human body finite element model under various simulation conditions. The various simulation conditions include combinations of various collision types, various occupant percentile sizes, various sitting angles, various seat rotation angles, and various seat belt force limiting conditions. And / or, The tenfold cross-validation refers to randomly assigning the original dataset ten times, using a random forest model to predict the results of each random assignment, and calculating the ratio of the regression sum of squares of the validation set to the total sum of squares of deviations from the mean as the model determination coefficient R².
5. The method for assessing the risk of brain tissue damage as described in claim 4, characterized in that, The original dataset is divided into training set, test set and validation set in a 6:2:2 ratio.
6. The method for assessing the risk of brain tissue damage as described in claim 1, characterized in that, In step B, the permutation feature importance algorithm includes the following steps: Step B1: Calculate the baseline error: Calculate the mean squared error based on the original feature matrix and the prediction model as the baseline; Step B2: Feature-by-feature permutation: Randomly shuffle all values of each input feature, replace them with the feature matrix, make predictions, calculate the mean square error after permutation, repeat multiple times, and take the average permutation error. Step B3: Calculate the importance score: use the difference between the average permutation error and the baseline error as the importance score for this feature; Step B4: Normalization: Divide the importance score of each feature by the sum of the scores of all features to obtain the weight percentage of each feature.
7. The method for assessing the risk of brain tissue injury as described in claim 1, characterized in that, In step C, the HIC 15 The functional relationships between BrIC and head injury risk and between BrIC and head injury risk are as follows: ； ； ； ； And / or, In step C, the formula P (peak von Mises stress in brain tissue) = a P(HIC 15 ) + b P(BrIC) is used to calculate the risk of AIS2 and AIS3 brain tissue damage corresponding to the peak von Mises stress in the same brain tissue from the same test. The formula is P(peak von Mises stress) = c P(HIC 15 ) + d P(BrIC) calculates the risk of brain injury at AIS grade 2 and AIS grade 3 corresponding to the peak MPS of the same brain tissue in the experiment; where a, b and c, d are HIC values respectively. 15 The weighting of BrIC in the peak von Mises stress and peak MPS stress in brain tissue.
8. The method for assessing the risk of brain tissue injury as described in claim 1, characterized in that, In step D, the numerical regression method includes linear function fitting and sigmoid function fitting. The fitting result with a determination coefficient R² not lower than a preset fitting threshold is taken as the final damage risk function. If R² does not meet the preset fitting threshold, the fitting function parameters are adjusted and refitted.
9. The method for assessing the risk of brain tissue injury as described in claim 9, characterized in that, The preset fitting standard is R²≥0.
8.
10. The application of a method for assessing the risk of brain tissue injury as described in any one of claims 1-9 in automobile crash simulation, characterized in that, Includes the following steps: Step 1: Construct a coupled simulation environment for the human body finite element model and constraint system, and apply boundary conditions according to the preset collision conditions; Step II: Run the collision simulation, extract the peak von Mises stress and peak MPS of the brain tissue from the simulation results, substitute them into the brain tissue damage risk function, and calculate the probability of brain tissue AIS2 and AIS3 damage risk.