A Study On Material Parameters Identification And Injury Evaluation Of Biologic Tissues Under Impact Loading

With the increase of road traffic accidents, impact injury biomechanics, which focus on the research of human protection, has been attracting more and more attentions. Finite element method, one of the valuable tools for the injury biomechanics study, is useful for the study of injury mechanisms and toleration. The accurate material properties are very important and necessary in the development of finite element models for biomechanics problems. Only if the accurate material parameters have been obtained, it is possible to improve the accuracy of finite element models and more accurately predict the crash injury and better protect the human body. However, because of the complicated structure of biological tissue, it’s usually hard to directly obtain the material parameters. Therefore, the computational inverse technology of the biological tissue based on the mechanical experiments is an important way to obtain the material parameters.From the perspective of biomechanics, this dissertation conducts a systematical research for the application of inverse technology to the identification of material parameters of biological tissue biological tissue based on the theories and methods of computational inverse technology and the experiments of animal and human tissue. Fully utilizing the advantages of multi-disciplinary, this study integrates the modern medical imaging, three dimensional graphic reconstruction and finite element analysis technologies. Based on the high bio-fidelity finite element models, this dissertation also aims at contributing some useful researches and trials on the investigation of collision damage. Based on massive literature review, the major contents of this dissertation are listed as follows:(1) A computational inverse technique for elastic-plastic material parameters identification of hard tissue based on three-point bending test, specimen-specific elements models and surrogate models is studied. The solution of elastic-plastic material parameters of hard tissue is described as a physical inverse problem, and the mathematic model of inverse problem is built with the combination of finite element method. The complex geometry and internal structure of biological tissue is obtained by advance medical imaging, such as Micro-CT. The data of three-point bending experiment of the animal and human tissue, combination the filter technique, finite element models, surrogate models, and optimization strategies, are utilized to solve the inverse problem. In order to validate the accuracy and efficiency of the proposed method for the injury biomechanics problems, the computational inverse technique is applied to the practical biomechanics problems, including the elastic-plastic material parameters identifications of rat skull and human femur. The high capacity and better performance of the proposed method are demonstrated by the applications in the material identification of hard tissue.(2) Based on the research of the inverse technique for hard tissue, considering the complex geometric features of soft tissue, the difficulty of material mechanical test of soft tissue, the shortcomings of traditional curve-fitting method and simplified-FE-Optimal methods, a computational inverse technique for viscoelastic material parameters identification of soft tissue is studied. The data of unconfined compression tests of human soft tissue, combination the specimen-specific finite element models obtained by laser scanning and sequential response surface method, are utilized to solve the inverse problem. The efficiency and convergence of the presented method are demonstrated by the material parameters identification of human thalamus. This new approach makes it possible to identify material constants of ultra-soft biological tissues without using a large number of test samples.(3) Considering the complexity of material test for soft tissue and the uncertain parameters in the boundary conditions, based on the interval analysis method, an efficient soft tissue material identification method containing uncertain parameters is presented. In order to evaluate the influence of the uncertain parameters, the objective function of the uncertain inverse problem is established, and then the interval is used to characterize the uncertain parameters (friction coefficient) based on interval mathematics. The material identification of soft tissue considering uncertain parameters is transformed into two deterministic inverse problems through the interval analysis method, and then the intelligent optimization algorithm is adopted to stably solve the problem. Thus, the upper and lower bounds of the material parameters can be easily obtained. The presented method provides a potential robust tool for developing high bio-fidelity and flexibility human finite element model in the road traffic field.(4) Considering the limitation of traumatic brain injury severity classifications, based on the radial basis functions surrogate models, the relationships between different combinations of external impact parameters (CCI impact depth and impactor diameter) and regional injury intensity based on FE model-predicted strain values is presented. The investigation of strain-based regional traumatic brain injury intensity is used to describe varying regional injury intensity. This study systematically predicts regional intensity of primary brain injury according to tissue strain distributions in the hope that strain distribution maps become a common platform to compare CCI severities with different setups. The current study presented a potential application of using FE brain models to measure the brain injury, and it’s also important to motitivate the study of injury mechanism and tolerance.(5) Utilizing the finite element method, surrogate models and optimization strategy, the occupant injury mechanism and the impact parameters are studied based on real world vehicular lateral crash reconstruction. The results of the accident reconstruction demonstrate that the proposed method can efficiently reverse the collision parameters to make the simulation data match the pratical deformation of the vehicle. The overall occupant kinematics at the time of the peak vehicle deformation was calculated and the maximum principal strain in the aorta predicted by the occupant model was tabulated. Although aortic pressure increases in the simulations, it may not be solely responsible for aortic failure. In simulated left lateral crashes, peak maximum principal strain primarily occurred in the isthmus of the aorta, distal to the orifice of the left subclavian artery. Results of design of computer experiments concluded that principal direction of force and impact velocity play a crucial role in increasing the maximum principal strain, a potential injury mechanism responsible for aortic rupture.