A Big Data-Theoretic Approach to Quantify Organizational Failure Mechanisms in Probabilistic Risk Assessment

概率风险评估中量化组织失败机制的大数据理论方法

• 批准号: 1535167
• 负责人: Zahra Mohaghegh
• 金额: $ 60.92万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1535167&HistoricalAwards=false
• 关键词: Big; Data; Theoretic; Approach; Quantify

Nontechnical DescriptionCatastrophic events such as Fukushima and Katrina have made it clear that integrating physical and social causes of failure into a cohesive modeling framework is critical in order to prevent complex technological accidents and to maintain public safety and health. In this research, experts in Probabilistic Risk Assessment (PRA), Organizational Behavior and Information Science and Data Analytics disciplines will collaborate to provide answers to the following key questions: (a) what social and organizational factors affect technical system risk? (b) how and why do these factors influence risk? and (c) how much do they contribute to risk? Existing PRA models do not include a complete range of organizational factors. This research investigates organizational root causes of failure and models their paths of influence on technical system performance, resulting in more comprehensive incorporation of underlying organizational failure mechanisms into PRA. The field of PRA has progressed the quantification of equipment failure and human error for modeling risk of complex systems; however, the current organizational risk contributors lack reliable data analytics that go beyond safety climate and safety culture surveys. This research fills that gap by developing predictive causal modeling and big-data theoretic technologies for PRA, expanding the classic approach of data management for risk analysis by utilizing techniques such as text mining, data mining and data analytics. In addition to scientific contributions to organizational science, PRA, and data analytics, this research provides regulatory and industry decision-makers with important organizational factors that contribute to risk and leads to optimized decision making. Other applications include real-time monitoring of organizational safety indicators, efficient safety auditing, in-depth root cause analysis, and risk-informed emergency preparedness, planning and response. The multidisciplinary approach of this project can serve as an educational model, empowering students to pursue research across disciplinary boundaries. Finally, the proposed research represents a successful model of industry-academia collaboration. A nuclear power plant has committed to this project and provides unique access to data and information necessary to complete the research. The proposed methodology is generic and applicable for any high-risk industry (e.g., aviation, healthcare, oil and gas), and will be used for the improvement of organizational safety performance in order to protect workers, the public and the environment. Technical DescriptionOrganizations produce, process and store a large volume of wide-ranging, unstructured data as a result of business activities and compliance requirements (i.e., corrective action programs, root cause analysis reports, oversight and inspection data, etc.). This research leverages those data resources for the quantification of organizational failure mechanisms and their integration with the technical system risk scenarios generated by PRA. The research is based on a socio-technical risk theory to prevent misleading results from solely data-informed approaches. Combining socio-technical risk theory, systematic modeling and semantic data analytics strategies will greatly enhance risk analysis of complex systems. We will conduct our research based on following steps: (1) Expand factors, sub-factors, and causal relationships in the Socio-Technical Risk Analysis (SoTeRiA) framework, (2) Develop measurement techniques for factors, sub-factors and their causal relationships in SoTeRiA (e.g., integrating text mining with the Bayesian Belief Network; conducting scientific reduction to identify important factors; measuring of important factors), (3) Establish a dynamic, predictive socio-technical causal modeling technique, (4) Perform uncertainty analysis, (5) Conduct verification and validation, (6) Integrate the quantitative socio-technical causal model with PRA, and (7) Conduct sensitivity and importance measure analyses. As the pioneer study on the integration of big data with PRA, this research addresses and quantifies risk emerging from the interface of social and technical systems.

非技术性描述灾难性事件，如福岛和卡特里娜飓风已经清楚地表明，整合失败的物理和社会原因到一个有凝聚力的建模框架是至关重要的，以防止复杂的技术事故和维护公共安全和健康。在这项研究中，概率风险评估（PRA），组织行为学和信息科学和数据分析学科的专家将合作回答以下关键问题：（a）哪些社会和组织因素影响技术系统风险？(b)这些因素如何以及为什么影响风险？以及（c）它们对风险的贡献有多大？ 现有的PRA模型不包括完整的组织因素。本研究调查组织失败的根源和模型的技术系统性能的影响路径，从而更全面地纳入潜在的组织失败机制到PRA。PRA领域已经在量化设备故障和人为错误以建模复杂系统的风险方面取得了进展;然而，目前的组织风险贡献者缺乏超越安全气候和安全文化调查的可靠数据分析。这项研究通过为PRA开发预测因果建模和大数据理论技术填补了这一空白，通过利用文本挖掘，数据挖掘和数据分析等技术扩展了风险分析的经典数据管理方法。除了对组织科学，PRA和数据分析的科学贡献外，这项研究还为监管和行业决策者提供了重要的组织因素，这些因素有助于风险并优化决策。其他应用包括组织安全指标的实时监控、高效的安全审计、深入的根本原因分析以及风险知情的应急准备、规划和响应。该项目的多学科方法可以作为一种教育模式，使学生能够跨越学科界限进行研究。 最后，提出的研究是一个成功的模式，产学合作。一家核电厂已承诺参与该项目，并提供了完成研究所需的数据和信息的独特访问权限。建议的方法是通用的，适用于任何高风险行业（例如，航空、医疗保健、石油和天然气），并将用于改善组织安全绩效，以保护工人、公众和环境。技术说明由于业务活动和合规性要求（即，纠正措施计划、根本原因分析报告、监督和检查数据等）。本研究利用这些数据资源的量化组织失败的机制，并与PRA产生的技术系统风险的情况下，他们的整合。该研究基于社会技术风险理论，以防止仅基于数据的方法产生误导性结果。将社会技术风险理论、系统建模和语义数据分析策略相结合，将大大增强复杂系统的风险分析。我们将根据以下步骤进行研究：（1）在社会技术风险分析（SoTeRiA）框架中扩展因素，子因素和因果关系，（2）开发SoTeRiA中因素，子因素及其因果关系的测量技术（例如，将文本挖掘与贝叶斯信度网络相结合，进行科学的约简，识别重要因素;重要因素的测量），（3）建立动态的、预测性的社会技术因果建模技术，（4）执行不确定性分析，（5）进行验证和确认，（6）将定量社会技术因果模型与PRA集成，（7）进行敏感性和重要性度量分析。作为大数据与PRA整合的先驱研究，本研究解决并量化了社会和技术系统界面中出现的风险。

项目成果

Data-theoretic methodology and computational platform to quantify organizational factors in socio-technical risk analysis

• DOI: 10.1016/j.ress.2018.12.020
• 发表时间: 2019-05
• 期刊: Reliab. Eng. Syst. Saf.
• 作者: J. Pence;T. Sakurahara;Xuefeng Zhu;Z. Mohaghegh;M. Ertem;Cheri Ostroff;E. Kee