Forecasting of stochastic ductile failures in fabrication and service stages are challenging tasks of advanced structures in practical engineering. Due to the prohibitive costs of repetitive experimental tests to quantify optimal failure-related parameters, numerous studies have turned to simulation-based uncertainty quantification. However, the credibility of these approaches is frequently doubted by the function-generated distribution of random data involved. Thus, an accurate assessment of the material parameters ascertains the appropriate estimation of uncertain parameters, which is essential for the risk/safety assessment of structures in different stages. In this paper, a full-field experiment-aided virtual modelling framework for inverse-based prediction of stochastic elastoplastic failure (EVMF-ISP) is proposed to deliver precise insights regarding the effective distribution range of mechanical parameters. To this end, all available experiment observations serve as the reference for assessing the prior and posterior probability density of the unknown parameters through the real-time inverse uncertainty quantification (UQ) module. The framework can be divided into three parts, where initially a pre-virtual model (PRVM) is formulated, and Bayesian inference is implemented to propagate the experiment observations backwards to ascertain the uncertain parameters. Then, an advanced multidimensional slice sampling method is developed to deal with the derived complex posterior probability density function (PDF) of mechanical parameters. In the end, a reliable stochastic elastoplastic analysis can be conducted with the revised uncertain samples and finalised with post-virtual models (POVMs) for the concerned structures. Such that, accurate and efficient determination of nonlinear response of structures can be directly predicted. The EVMF-ISP framework is logically presented and thoroughly illustrated with practical applications.

中文翻译：

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用于弹塑性结构逆向随机预测的全场实验辅助虚拟建模框架


预测制造和使用阶段的随机延性失效是实际工程中先进结构的挑战性任务。由于量化最佳故障相关参数的重复实验测试成本高昂，因此许多研究转向基于模拟的不确定性量化。然而，这些方法的可信度经常因所涉及的随机数据的函数生成分布而受到怀疑。因此，对材料参数的准确评估可以确定不确定参数的适当估计，这对于不同阶段结构的风险/安全评估至关重要。在本文中，提出了一种用于基于逆向预测随机弹塑性失效（EVMF-ISP）的全场实验辅助虚拟建模框架，以提供有关机械参数有效分布范围的精确见解。为此，所有可用的实验观察结果都可以作为通过实时逆不确定性量化（UQ）模块评估未知参数的先验和后验概率密度的参考。该框架可分为三个部分，首先制定预虚拟模型（PRVM），并实施贝叶斯推理以向后传播实验观察结果以确定不确定参数。然后，开发了一种先进的多维切片采样方法来处理导出的机械参数的复杂后验概率密度函数（PDF）。最后，可以使用修改后的不确定样本进行可靠的随机弹塑性分析，并通过相关结构的后虚拟模型 (POVM) 进行最终确定。 这样，可以直接预测结构的非线性响应的准确和有效的确定。 EVMF-ISP框架逻辑清晰，并通过实际应用进行了详尽的说明。