Data-theoretic methodology and computational platform to quantify organizational factors in socio-technical risk analysis
Data-theoretic methodology and computational platform to quantify organizational factors in socio-technical risk analysis
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DOI：
10.1016/j.ress.2018.12.020
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
J. Pence;T. Sakurahara;Xuefeng Zhu;Z. Mohaghegh;M. Ertem;Cheri Ostroff;E. Kee
中科院分区：
文献类型：
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作者：
J. Pence;T. Sakurahara;Xuefeng Zhu;Z. Mohaghegh;M. Ertem;Cheri Ostroff;E. Kee
正如文献表明的那样，组织因素是高后果行业风险的重要贡献者。因此，建立一个具有可靠建模技术和数据分析的理论框架来量化组织绩效对风险情景的影响，对于提高概率风险评估（PRA）的现实性具有重要意义。社会技术风险分析（SoTeRiA）框架从理论上将组织的结构（如安全实践）和行为（如安全文化）方面与PRA联系起来。引入了集成PRA （I-PRA）方法框架来实施SoTeRiA，以便将潜在的组织失败机制量化到风险情景中。本研究的重点是i- pra的数据理论模块，该模块包括两个子模块：(i) DT-BASE：在SoTeRiA中建立详细的因果关系，以理论为基础，并配备半自动基线量化，利用从学术文章、行业流程和监管标准中提取的信息；（ii） DT-SITE：基于特定地点的事件数据库和对DT-BASE基线量化的贝叶斯更新，进行自动化数据提取和推理方法，以量化SoTeRiA因果要素。一个案例研究演示了核电厂组织“培训”因果模型的量化，该模型与人力可靠性分析的培训/经验有关，同时还进行了识别关键因素的敏感性分析。
Organizational factors, as literature indicates, are significant contributors to risk in high-consequence industries. Therefore, building a theoretical framework equipped with reliable modeling techniques and data analytics to quantify the influence of organizational performance on risk scenarios is important for improving realism in Probabilistic Risk Assessment (PRA). The Socio-Technical Risk Analysis (SoTeRiA) framework theoretically connects the structural (e.g., safety practices) and behavioral (e.g., safety culture) aspects of an organization with PRA. An Integrated PRA (I-PRA) methodological framework is introduced to operationalize SoTeRiA in order to quantify the incorporation of underlying organizational failure mechanisms into risk scenarios. This research focuses on the Data-Theoretic module of I-PRA, which has two sub-modules: (i) DT-BASE: developing detailed causal relationships in SoTeRiA, grounded on theories and equipped with a semi-automated baseline quantification utilizing information extracted from academic articles, industry procedures, and regulatory standards, and (ii) DT-SITE: conducting automated data extraction and inference methods to quantify SoTeRiA causal elements based on site-specific event databases and by Bayesian updating of the DT-BASE baseline quantification. A case study demonstrates the quantification of a nuclear power plant's organizational “training” causal model, which is associated with the training/experience in Human Reliability Analysis, along with a sensitivity analysis to identify critical factors.