A DATA-INFORMED MODEL OF PERFORMANCE SHAPING FACTORS FOR USE IN HUMAN RELIABILITY ANALYSIS

Many Human Reliability Analysis (HRA) models use Performance Shaping Factors (PSFs) to incorporate human elements into system safety analysis and to calculate the Human Error Probability (HEP). Current HRA methods rely on different sets of PSFs that range from a few to over 50 PSFs, with varying degrees of interdependency among the PSFs. This interdependency is observed in almost every set of PSFs, yet few HRA methods offer a way to account for dependency among PSFs. The methods that do address interdependencies generally do so by varying different multipliers in linear or log-linear formulas. These relationships could be more accurately represented in a causal model of PSF interdependencies. This dissertation introduces a methodology to produce a Bayesian Belief Network (BBN) of interactions among PSFs. The dissertation also presents a set of fundamental guidelines for the creation of a PSF set, a hierarchy of PSFs developed specifically for causal modeling, and a set of models developed using currently available data. The models, methodology, and PSF set were developed using nuclear power plant data available from two sources: information collected by the University of Maryland for the Information-Decision-Action model [1] and data from the Human Events Repository and Analysis (HERA) database [2] , currently under development by the United States Nuclear Regulatory Commission. Creation of the methodology, the PSF hierarchy, and the models was an iterative process that incorporated information from available data, current HRA methods, and expert workshops. The fundamental guidelines are the result of insights gathered during the process of developing the methodology; these guidelines were applied to the final PSF hierarchy. The PSF hierarchy reduces overlap among the PSFs so that patterns of dependency observed in the data can be attribute to PSF interdependencies instead of overlapping definitions. It includes multiple levels of generic PSFs that can be expanded or collapsed for different applications. The model development methodology employs correlation and factor analysis to systematically collapse the PSF hierarchy and form the model structure. Factor analysis is also used to identify Error Contexts (ECs) – specific PSF combinations that together produce an increased probability of human error (versus the net effect of the PSFs acting alone). Three models were created to demonstrate how the methodology can be used provide different types of data-informed insights. By employing Bayes' Theorem, the resulting model can be used to replace linear calculations for HEPs used in Probabilistic Risk Assessment. When additional data becomes available, the methodology can be used to produce updated causal models to further refine HEP values

Exploration of methods for using SACADA data to estimate HEPs: Final Report

This report provides summary of the project "Exploration of methods for using SACADA data to estimate HEPs." The goal of the project was to conduct exploratory research on how to use the U.S. Nuclear Regulatory Commission's SACADA (Scenario, Authoring, Characterization, and Debriefing Application) database to develop an algorithm for estimating human error probabilities (HEPs). The approach used by the University of Maryland SyRRA lab uses a combination of Bayesian statistical methods and Bayesian Network models to conduct data analysis on SACADA data and to construct hybrid models informed by both data and engineering models. The end results provided various algorithms for mapping and binning SACADA data to be used within HEP estimation, and demonstrated a variety of options which create a framework for streamlined updating of HEPs as the amount and variety of SACADA data increases. This report summarizes the project's major accomplishments, and gathers the abstracts and references for the publication submissions and reports that were prepared as part of this work

A 'synthetic-sickness' screen for senescence re-engagement targets in mutant cancer backgrounds.

Senescence is a universal barrier to immortalisation and tumorigenesis. As such, interest in the use of senescence-induction in a therapeutic context has been gaining momentum in the past few years; however, senescence and immortalisation remain underserved areas for drug discovery owing to a lack of robust senescence inducing agents and an incomplete understanding of the signalling events underlying this complex process. In order to address this issue we undertook a large-scale morphological siRNA screen for inducers of senescence phenotypes in the human melanoma cell line A375P. Following rescreen and validation in a second cancer cell line, HCT116 colorectal carcinoma, a panel of 16 of the most robust hits were selected for further validation based on significance and the potential to be targeted by drug-like molecules. Using secondary assays for detection of senescence biomarkers p21, 53BP1 and senescence associated beta-galactosidase (SAβGal) in a panel of HCT116 cell lines carrying cancer-relevant mutations, we show that partial senescence phenotypes can be induced to varying degrees in a context dependent manner, even in the absence of p21 or p53 expression. However, proliferation arrest varied among genetic backgrounds with predominantly toxic effects in p21 null cells, while cells lacking PI3K mutation failed to arrest. Furthermore, we show that the oncogene ECT2 induces partial senescence phenotypes in all mutant backgrounds tested, demonstrating a dependence on activating KRASG13D for growth suppression and a complete senescence response. These results suggest a potential mechanism to target mutant KRAS signalling through ECT2 in cancers that are reliant on activating KRAS mutations and remain refractory to current treatments

A Dynamic Bayesian Network Structure for Joint Diagnostics and Prognostics of Complex Engineering Systems

Dynamic Bayesian networks (DBNs) represent complex time-dependent causal relationships through the use of conditional probabilities and directed acyclic graph models. DBNs enable the forward and backward inference of system states, diagnosing current system health, and forecasting future system prognosis within the same modeling framework. As a result, there has been growing interest in using DBNs for reliability engineering problems and applications in risk assessment. However, there are open questions about how they can be used to support diagnostics and prognostic health monitoring of a complex engineering system (CES), e.g., power plants, processing facilities and maritime vessels. These systems’ tightly integrated human, hardware, and software components and dynamic operational environments have previously been difficult to model. As part of the growing literature advancing the understanding of how DBNs can be used to improve the risk assessments and health monitoring of CESs, this paper shows the prognostic and diagnostic inference capabilities that are possible to encapsulate within a single DBN model. Using simulated accident sequence data from a model sodium fast nuclear reactor as a case study, a DBN is designed, quantified, and verified based on evidence associated with a transient overpower. The results indicate that a joint prognostic and diagnostic model that is responsive to new system evidence can be generated from operating data to represent CES health. Such a model can therefore serve as another training tool for CES operators to better prepare for accident scenarios.https://doi.org/10.3390/a1303006

Use of limited data to construct Bayesian networks for probabilistic risk assessment.

Probabilistic Risk Assessment (PRA) is a fundamental part of safety/quality assurance for nuclear power and nuclear weapons. Traditional PRA very effectively models complex hardware system risks using binary probabilistic models. However, traditional PRA models are not flexible enough to accommodate non-binary soft-causal factors, such as digital instrumentation&control, passive components, aging, common cause failure, and human errors. Bayesian Networks offer the opportunity to incorporate these risks into the PRA framework. This report describes the results of an early career LDRD project titled %E2%80%9CUse of Limited Data to Construct Bayesian Networks for Probabilistic Risk Assessment%E2%80%9D. The goal of the work was to establish the capability to develop Bayesian Networks from sparse data, and to demonstrate this capability by producing a data-informed Bayesian Network for use in Human Reliability Analysis (HRA) as part of nuclear power plant Probabilistic Risk Assessment (PRA). This report summarizes the research goal and major products of the research

Causal Pathways Leading to Human Failure Events in Information-Gathering System Response Activities

Human Failure Events (HFEs) are complex, multi layer events culminating with a human machine team’s failure to complete a plant objective. HFEs can be further described by Crew Failure Modes CFMs which document specific ways the objective tasks may be un successfully performed. In turn, these CFMs are affected by Performance Influencing Factors (PIFs ), some of which exert a more direct influence than others. However, in current Human Reliability Analysis HRA methods, the multitude s of causal relationshi ps between PIFs, CFMs, and HFEs are not explicitly modeled. This work seeks to fill that gap by developing structured causal models that document direct and indirect pathways from PIFs, through CFMs, and into HFEs. This work is intended to expand the curre nt application of causal based HRA modeling beyond control room environments to external environments under natural hazard scenarios. A Bayesian network of information gathering operator activities in response to a system demand is developed by following the causal mapping methodology defined in Zwirglmaier et al. 2017 )). The relationships in this structure are substantiated with existing psychological and organizational literature, thereby allowing for the identification of the main causal pathways leadin g to a particular CFM, and therefore an HFE. The work draws upon proximate causes of failure from the NRC’s NUREG 2114 , CFMs in the Phoenix HRA method, and PIFs from Groth’s 2012 hierarchy. Capturing these causal pathways provides the foundation for an imp roved causal basis of HRA, which represents a promising strategy for enhancing the accuracy and technical basis of HRA. Future efforts will include validation of the structures, constructing similar models for decisionDepartment of Energy, Office of Nuclear Energy, Award Number DE-NE0008974. Department of Energy, Office of Nuclear Energy, University Nuclear Leadership Program Fellowship

Identifying Human Failure Events (HFEs) for External Hazard Probabilistic Risk Assessment

In recent years, several advancements in nuclear power plant (NPP) probabilistic risk assessment (PRA) have been driven by increased understanding of external hazards, plant response, and uncertainties. However, major sources of uncertainty associated with external hazard PRA remain. One source discussed in this study is the close coupling of physical impacts on plants and overall plant risk under hazard events due to the significant human actions that are carried out to enable plant response and recovery from natural hazards events. This makes human reliability and human-plant interactions important elements in to consider in enhancing PRA to address external hazards. One of the challenges in considering human responses is that most existing human reliability analysis (HRA) models, such as SPAR-H and THERP, were not developed for assessing ex-control room actions and hazard response. To support this new scope for HRA, HRA models will need to be developed or modified to support identification of human activities, causal factors, and uncertainties inherent in external hazard response, thereby providing insights regarding event timing and physical event conditions as they relate to human performance. In this study, the first step of such work is performed by identifying human failure events (HFEs) for human response to flooding hazards. These HFEs are human actions or inactions that are involved in human response to flooding hazards and could contribute to the loss of a critical function for the plant in the scenario being examined. Several resources are used to identify these HFEs, including flooding reports from the Nuclear Regulatory Commission (e.g. NUREG/CR-7256: Effects of Environmental Conditions on Manual Actions for Flood Protection and Mitigation), interviews with experienced PRA and HRA analysts, and tabletop walkdowns of flooding scenarios with a project team. Also, task decomposition analyses using the cognitive-based Phoenix HRA model are also used to identify HFEs. This paper will discuss early results of these analyses.Department of Energy, Office of Nuclear Energy, Award Number DE-NE000897

Construction and Verification of a Bayesian Network for Third-Party Excavation Risk Assessment (BaNTERA)

According to the Pipeline and Hazardous Material Safety Administration (PHMSA), thirdparty damage is a leading cause of natural gas pipeline accidents. Although the risk of third-party damage has been widely studied in the literature, current models do not capture a sufficiently comprehensive set of up-to-date root cause factors and their dependencies. This limits their ability to achieve an accurate risk assessment that can be traced to meaningful elements of an excavation. This paper presents the construction, verification, and validation of a probabilistic Bayesian network model for third-party excavation risk assessment, BaNTERA. The model was constructed and its performance verified using the best available industry data and previous models from multiple sources. Historical industry data and nationwide statistics were compared with BaNTERA’s damage rate predictions to validate the model. The result of this work is a comprehensive risk model for the third-party damage problem in natural gas pipelines.U.S. Department of Transportation Pipeline and Hazardous Materials Safety Administratio