For a long time, research into integrated deterministic-probabilistic safety assessment has been continuously conducted to point out and overcome the limitations of classical ET (event tree)/FT (fault tree) based PSA (probabilistic safety assessment). The current paper also attempts to assert the reason why a technical transformation from classical PSA is necessary with a re-interpretation of the categories of risk. In this study, residual risk was classified into interpolating- and extrapolating-censored categories, which represent risks that are difficult to identify through an interpolation or extrapolation of representative scenarios due to potential nonlinearity between hardware and human behaviors intertwined in time and space. The authors hypothesize that such risk can be dealt with only if the classical ETs/FTs are freely relocated, entailing large-scale computation associated with physical models. The functional elements that are favorable to find residual risk were inferred from previous studies. The authors then introduce their under-development enabling techniques, namely DICE (Dynamic Integrated Consequence Evaluation) and DeBATE (Deep learning–Based Accident Trend Estimation). This work can be considered as a preliminary initiative to find the bridging points between deterministic and probabilistic assessments on the pillars of big data technology.

长期以来，综合确定性-概率安全评估的研究不断进行，以指出并克服基于经典ET（事件树）/FT（故障树）的PSA（概率安全评估）的局限性。当前的论文还试图通过重新解释风险类别来断言为什么需要从经典 PSA 进行技术转型的原因。在这项研究中，剩余风险被分为内插和外推删失类别，它们代表了由于硬件和人类行为在时间和空间上交织在一起的潜在非线性而难以通过代表性场景的内插或外推来识别的风险。作者假设只有在经典的 ET/FT 自由迁移的情况下才能应对这种风险，需要与物理模型相关的大规模计算。有利于发现剩余风险的功能元素是从以前的研究中推断出来的。然后，作者介绍了他们正在开发的支持技术，即 DICE（动态综合后果评估）和 DeBATE（基于深度学习的事故趋势估计）。这项工作可以被视为一项初步举措，旨在寻找大数据技术支柱的确定性和概率评估之间的桥梁。即 DICE（动态综合后果评估）和 DeBATE（基于深度学习的事故趋势估计）。这项工作可以被视为一项初步举措，旨在寻找大数据技术支柱的确定性和概率评估之间的桥梁。即 DICE（动态综合后果评估）和 DeBATE（基于深度学习的事故趋势估计）。这项工作可以被视为一项初步举措，旨在寻找大数据技术支柱的确定性和概率评估之间的桥梁。