The Research On Hybrid Inverse Method For Material Characteristic Parameters Identification And Applications

With the rapid development of science and technology, the advanced materials have possessed more and more functions as well as complicated properties, and the application fields are more and more extensive. It is of great scientific and practical significance for the research on the properties of materials. At present, there are two important approaches to study the properties of materials, namely experiment and numerical computation. Experiment is the basic way to study the properties of materials, from which it can obtain the mechanical responses of materials under different load conditions. With the help of the computer technology and numerical method, numerical computation can reproduce the behaviors of materials and obtain some variables which cannot be measured in experiments. And it can also provide lots of process data. It is very important and necessary to identify the material characteristic parameters for numerical calculation, which determines the accuracy and reliability of numerical results. Traditionally, the material parameters can be determined by a fitting method based on a great deal of experimental data. However, it is expensive and time-consuming to obtain lots of experimental data due to the limitations of experimental methods and testing techniques. Therefore, the computational inverse method for parameter identification combining with a few experiments is an important way to identify the material characteristic parameters.This dissertation conducts a systematical research on the computational inverse method for material parameter identification, especially focusing on parameter sensitivity, the accuracy of inverse results, computational efficiency and applications. It aims at contributing some useful researches and trials on the computational inverse algorithms and applications for material parameters identification. Firstly, the key techniques of computational inverse method for parameter identification are studied, and a hybrid inverse method based on the curve-estimating homotopy algorithm and genetic algorithm is presented to improve the computational accuracy and efficiency. Then, in order to validate the availability and reliability of the hybrid inverse method, it is applied to parameters identification for concrete, which is a multi-phase compound brittle material. The static and dynamic experiments for concrete are conducted to obtain effective experimental data for parameters identification. The key characteristic parameters of concrete are determined through the hybrid inverse method combining with the experimental data. Moreover, the inverse results are used to estimate the characteristic parameters of another concrete material, and the validations of the estimated results are performed. The major research works of this dissertation are as follows:(1) A hybrid inverse method based on curve-estimating homotopy algorithm and genetic algorithm is presented. In this method, genetic algorithm is first used to select a set of better solutions close to the optima; then the improved homotopy method is applied using these better solutions as the initial guesses. Finally, the identification results can be determined by the improved homotopy method. Additionally, a Sobol’s direct integration method based on the optimal polynomial response surface is developed for global sensitivity analysis in order to determine the inversed variables. And based on Euler estimate-Newton correction homotopy algorithm, the curve estimate-Newon correction homotopy method is developed to promote the computational efficiency and accuracy. The numerical examples are investigated and the identified results show that this hybrid inverse method can give full play to the advantages of the two algorithms and quickly determine the parameters.(2) In order to validate the availability and reliability of the hybrid inverse method, it is applied to parameter identification for concrete, which is the classical brittle material. First of all, the static and dynamic experiments for concrete are conducted to obtain effective and reliable experimental data for parameter identification. Simultaneously, the influences of basic strength, aggregate size and content on the static and dynamic behaviors of concrete are studied. And the high-speed video technique is adopted to capture the dynamic deformation of concrete during split Hopkinson pressure bar (SHPB) tests. The results show that the aggregate size and content have some effects on the static and dynamic properties of concrete with different basic strength. The influence of aggregate content on the elastic modulus is larger than that on quasi-static compressive strength under quasi-static compressure. The dynamic compressive properties of concrete, including dynamic strength, peak strain and energy absorption, have significant strain-rate effect. These variables increase with the increase of strain rate.(3) Combining with the numerical model of concrete, a computational inverse technique based on quasi-static compressure test and curve-estimating homotopy method is developed for parameter identification for interfacial transition zone. In this approach, concrete is considered as a three-phase composites consisting of cement mortar, coarse aggregate and interfacial transition zone, and a numerical model for concrete is constructed. The elastic modulus of interfacial transition zone is determined by curve estimate-Newton correction homotopy method based on the experimental data on the elastic area. Additionally, the effect of distribution of aggregates on elastic modulus of concrete is investigated. The results show that the interfacial transition zone should be considered as the concrete is analyzed as multi-phase composites.(4) A computational inverse technique based on SHPB test and the hybrid inverse method is developed to determine the dynamic characteristic parameters for concrete. In this approach, the effective reflect wave and transmission wave responses from SHPB tests are directly used as input for the inverse procedure, which can avoid the problem of decoupling stress wave effect and the strain rate effect, decoupling structure effect and material effect. Firstly, the effectiveness and reliability of the hybrid method are demonstrated by the application in the parameters identification for mortar with low strength. Then, the key characteristic parameters of concrete with different basic strength, size and content of aggregate are determined by this method. The results show that the hybrid inverse method has high capacity and better performance and is a potentially useful tool to effectively help identify material characteristic parameters combining with a few experiments.(5) A nonlinear network model linking the characteristic parameters of concrete and the parameters of basic strength, aggregate size and content is constructed. The characteristic parameters of another concrete are estimated through this model. The numerical computations for penetration problem are performed to evaluate the estimated results. To further evaluate these results, a computational inverse method based on penetration experiment and hybrid inverse method is developed to identify the parameters of concrete. In this approach, basic function approximation model combining with a local-density method is used to replace the actual computational model for improving computational efficiency. Moreover, the anti-penetration performances of concrete with different basic strength, aggregate size and content are investigated through numerical computation.