This paper proposes a dual experimental/computational data-driven analysis framework for apparent strength estimation of composite structures consisting of randomly arranged unidirectional fiber-reinforced plastics. In the proposed framework, multiscale stochastic analysis is performed with random field modeling of local apparent quantities such as apparent elastic modulus or strength. Significant improvements are needed in terms of computational accuracy, uncertainty quantification, random field modeling, and computational efficiency for the quantitative strength estimation by numerical analysis. For this problem, a novel computational framework assisted by the dual data-driven approach is established in this research. In the proposed approach, the accuracy of the strength estimation analysis for deterministic conditions is improved by an experimental data-driven approach based on the in-situ microscopic full-field displacement measurement. A computational data-driven approach based on random field modeling assisted by machine learning is employed for non-deterministic conditions. In this paper, the outline of the proposed dual data-driven multiscale stochastic analysis framework is introduced first. Subsequently, the details of the proposed experimental data-driven approach for determining the microscopic fracture criteria are presented, and the computational data-driven approach for improving the effectiveness and efficiency of the random field modeling-based probabilistic analysis is described. The presented approach is applied to the strength estimation of a randomly arranged unidirectional fiber-reinforced composite plate under transverse tensile loading, and its validity and effectiveness are discussed with comparisons between the experimental and numerical results obtained assuming several computational conditions.

中文翻译：

---

一种基于随机场建模的复合材料结构强度估计分析的实验/计算数据驱动双方法


本文提出了一种双重实验/计算数据驱动的分析框架，用于对由随机排列的单向纤维增强塑料组成的复合材料结构进行表观强度估计。在所提出的框架中，通过对局部表观量（如表观弹性模量或强度）进行随机场建模来执行多尺度随机分析。在计算精度、不确定性量化、随机场建模和数值分析定量强度估计的计算效率方面需要有重大改进。针对这个问题，本研究建立了一种由双重数据驱动方法辅助的新型计算框架。在所提出的方法中，通过基于原位微观全场位移测量的实验数据驱动方法提高了确定性条件的强度估计分析的准确性。在机器学习的辅助下，采用基于随机场建模的计算数据驱动方法，用于非确定性条件。在本文中，首先介绍了所提出的双数据驱动的多尺度随机分析框架的大纲。随后，详细介绍了所提出的用于确定微观断裂标准的实验数据驱动方法，并描述了用于提高基于随机场建模的概率分析的有效性和效率的计算数据驱动方法。 将该方法应用于随机排列的单向纤维增强复合材料板在横向拉伸载荷下的强度估计，并通过在几种计算条件下获得的实验结果和数值结果的比较讨论了其有效性和有效性。