材料本构参数识别是理解和模拟材料在各种外载作用下力学行为的关键，而准确的力学模型则是高精度数值模拟的前提和基础。在材料本构参数识别过程中，相比于仅可获得表面变形的传统光测力学方法，由数字体图像相关方法提供的内部全场三维变形测量结果有助于实现更为稳健和准确的参数识别。本项目拟研究基于自适应数字体图像相关方法和虚场方法相结合的新型材料本构参数识别技术，以实现材料三维本构参数的稳健和准确识别。研究内容包括：(1) 发展参数自适应选择的先进数字体图像相关方法；(2) 建立基于内部三维变形场的三维虚场方法，并采用数值模拟和实验手段研究变形测量误差在本构参数识别中的误差传递过程；(3) 对三维虚场方法进行改进并建立标准化识别流程，以实现稳健和准确的材料参数识别。本项目还将通过识别多种非均质材料（如：松质骨、木材、复合和柔性泡沫塑料）的三维本构参数来验证所发展的新型识别方法的可靠性和准确性。

Constitutive parameter identification is an essential step in the modeling of the mechanical behavior of materials under external load. Such models are key in realistic numerical simulations of a wide range of structures. The use of full-field volumetric kinematic measurements provided by the fast-emerging digital volume correlation (DVC) technique, rather than surface deformation fields delivered by traditional optical techniques, will lead to more robust and accurate identification. This project aim to develop novel and robust constitutive parameter identification procedures based on a combination of advanced DVC method and the Virtual Fields Method (VFM). Research efforts will primarily focus on: (1) developing an advanced self-adaptive DVC approach for efficient and accurate internal deformation measurement from high-resolution volumetric images captured by X-ray Computed Tomography; (2) establishing a novel DVC/VFM numerical simulator and using real experiments to quantify uncertainty propagation in the complex identification chain, (3) improving and standardizing the VFM method for material parameters extraction from full-field DVC measurements with enhanced robustness and accuracy. The developed identification method will be validated on various materials (e.g., trabecular bone, syntactic and flexible foams, wood and composite materials).