|Abstract:||Organizational and social factors remain elusive and latent contributors to incidents and accidents in high-consequence industries, such as nuclear power, aviation, oil and gas, and healthcare. Probabilistic Risk Assessment (PRA) is a formal methodology for estimating risk emerging from the interactions of equipment failure and human error. This research is the product of a line of a collaborative study to theorize and quantify the explicit incorporation of organizational and social factors into PRA of complex technological systems, specifically for Nuclear Power Plants (NPPs) to; (a) make risk assessments more accurate, and (b) improve risk management and prevention by identifying and ranking critical organizational/social factors based on their influences on the technical system risk.
For NPPs, PRA can be used to generate three levels of risk information, including risk from reactor core damage (Level 1 PRA), the risk from loss of containment integrity (Level 2 PRA), and risk to the population and environment (Level 3 PRA). This dissertation is the product of multidisciplinary and collaborative PRA research activities, covering six journal manuscripts, and theorizes and quantifies organizational/social factors from two levels of analysis:
1. Meso-Level; meso-level organizational factors contribute to incidents or accidents in Level 1 PRA (e.g., Core Damage Frequency (CDF) in NPPs). Chapters 2 to 4 of the thesis cover the following contributions to the meso-level analysis:
a. Presents a discourse on the incorporation of organizational factors into PRA by summarizing key questions associated with the incorporation of organizational factors into PRA, framing the ongoing debates surrounding the topic, providing a categorical review of literature, and highlighting the directions of research required to reach a resolution for each question;
b. Expands the granularity of the Socio-Technical Risk Analysis (SoTeRiA) theoretical framework.
c. Advances the Integrated PRA (I-PRA) methodological framework to operationalize the SoTeRiA theoretical framework by developing the Data-Theoretic (DT) input module, which has two sub-modules: (1) DT-BASE, for developing detailed theory-based causal relationships in the Socio-Technical Risk Analysis (SoTeRiA) theoretical framework, equipped with a software-supported BASEline quantification utilizing information extracted from literature, industry reports, and regulatory standards, and (2) DT-SITE, conducting data analytics to refine and measure the causal factors of SoTeRiA based on system-specific historical event databases and using Bayesian analysis to update the baseline quantification. The methdology is applied using NPP database.
d. Applies DT-BASE to theorize and quantify a causal model of an NPP’s organizational “training system” and performs sensitivity analysis to identify critical factors. The computational platform of DT-BASE eases the execution of theory-building to expand theoretical details in SoTeRiA.The results indicate that among all the causal factors, “Program Design,” “Training Procedures/Facility,” and “Instructor Performance” are identified as the first, second, and third most important factors, respectively.
e. Applies DT-SITE, using the “training system” causal model from DT-BASE, to conduct text mining of Licensee Event Reports (LERs) from the U.S. nuclear power industry to generate the probability of “poor training quality.” Using the results of DT-SITE, the resulting probability of “poor training quality,” is estimated as 7.03E-07.
2. Macro-Level; macro-level social factors contribute to consequences of emergency response in Level 3 PRA (e.g., population radiological dose exposure). Chapters 5 and 6 of the thesis cover the following contributions to the macro-level analysis:
a. Develops a macro-level socio-technical risk analysis theoretical framework of factors influencing emergency response to a radiological hazard, considering onsite and offsite response organization performance, socio-technical infrastructure, multi-hazard interactions, and population protective action performance. The advanced theoretical framework contributes to the comprehensiveness of Level 3 PRA by considering a broader set of influencing factors and their multi-level interrelationships, providing opportunities for improved root cause analysis and development of radiological emergency response plans.
b. Develops an external integration between a radiological hazard and social vulnerability, a commonly used indicator in natural hazard research, and conducts risk importance measure analysis. The results reveal that the Center for Disease Control (CDC) Social Vulnerability Index (SVI) theme contributions to socio-technical risk can vary significantly by location.
c. Introduces an internally-integrated methodological framework for building and validating an HRA-based Population Departure Time Model (PDTM), and integrating it with the transportation evacuation model to generate model-based Evacuation Time Estimates (ETEs) and evacuation speed estimates as inputs to Level 3 PRA model. This integrated methodology makes an advancement toward the explicit incorporation of social factors into Level 3 through the explicit incorporation of social factors into departure time and evacuation speed estimations. The integrated methodology can help (i) create a more realistic estimation of risk from Level 3 PRA by contributing to a more realistic representation of population evacuation performance and (ii) provide the opportunity to conduct importance ranking of the social factors, influencing departure time and evacuation speed, with respect to their impacts on risk.
d. Applies the internally-integrated methodology for Level 3 PRA in a case study using results from the 2017 Sequoyah SOARCA study.
Lastly, to justify the ‘market value’ of PRA, and provide incentives for companies to make investments in PRA, for example, investing in the explicit incorporation of organizational/social factors, Chapter 7 of this dissertation analyzes the monetary value of PRA.