Theses and Dissertations from UMD

Permanent URI for this communityhttp://hdl.handle.net/1903/2

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM

More information is available at Theses and Dissertations at University of Maryland Libraries.

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Subject: search.filters.subject.Human Reliability

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Now showing 1 - 3 of 3
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    DYNAMIC BEHAVIOR OF OPERATING CREW IN COMPLEX SYSTEMS: AN OBJECT-BASED MODELING & SIMULATION APPROACH
    (2013) Azarkhil, Mandana; Mosleh, Ali; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    High-risk environments such as the control room of Nuclear Power Plants are extremely stressful for the front line operators; during accidents and under high task load situations, the operators are solely responsible for the ultimate decision-making and control of such complex systems. Individuals working as a team constantly interact with each other and therefore introduce team related issues such as coordination, supervision and conflict resolution. The aggregate impact of multiple human errors inside communication and coordination loops in a team context can give rise to complex human failure modes and failure mechanisms. This research offers a model of operating crew as an interactive social unit and investigates the dynamic behavior of the team under upset situations through a simulation method. The domain of interest in this work is the class of operating crew environments that are subject to structured and regulated guidelines with formal procedures providing the core of their response to accident conditions. In developing the cognitive models for the operators and teams of operators, their behavior and relations, this research integrates findings from multiple disciplines such as cognitive psychology, human factors, organizational factors, and human reliability. An object-based modeling methodology is applied to represent system elements and different roles and behaviors of the members of the operating team. The proposed team model is an extended version of an existing cognitive model of individual operator behavior known as IDAC (Information, Decision, and Action in Crew context). Scenario generation follows DPRA (Dynamic Probabilistic Risk Assessment) methodologies. The method capabilities are demonstrated through building and simulating a simplified model of a steam/power generating plant. Different configurations of team characteristics and influencing factors have been simulated and compared. The effects of team factors and crew dynamics on system risk with main focus on team errors, associated causes and error management processes and their impact on team performance have been studied through a large number of simulation runs. The results are also compared with several theoretical models and empirical studies.
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    A Predictive Model of Nuclear Power Plant Crew Decision-Making and Performance in a Dynamic Simulation Environment
    (2009) Coyne, Kevin; Mosleh, Ali; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The safe operation of complex systems such as nuclear power plants requires close coordination between the human operators and plant systems. In order to maintain an adequate level of safety following an accident or other off-normal event, the operators often are called upon to perform complex tasks during dynamic situations with incomplete information. The safety of such complex systems can be greatly improved if the conditions that could lead operators to make poor decisions and commit erroneous actions during these situations can be predicted and mitigated. The primary goal of this research project was the development and validation of a cognitive model capable of simulating nuclear plant operator decision-making during accident conditions. Dynamic probabilistic risk assessment methods can improve the prediction of human error events by providing rich contextual information and an explicit consideration of feedback arising from man-machine interactions. The Accident Dynamics Simulator paired with the Information, Decision, and Action in a Crew context cognitive model (ADS-IDAC) shows promise for predicting situational contexts that might lead to human error events, particularly knowledge driven errors of commission. ADS-IDAC generates a discrete dynamic event tree (DDET) by applying simple branching rules that reflect variations in crew responses to plant events and system status changes. Branches can be generated to simulate slow or fast procedure execution speed, skipping of procedure steps, reliance on memorized information, activation of mental beliefs, variations in control inputs, and equipment failures. Complex operator mental models of plant behavior that guide crew actions can be represented within the ADS-IDAC mental belief framework and used to identify situational contexts that may lead to human error events. This research increased the capabilities of ADS-IDAC in several key areas. The ADS-IDAC computer code was improved to support additional branching events and provide a better representation of the IDAC cognitive model. An operator decision-making engine capable of responding to dynamic changes in situational context was implemented. The IDAC human performance model was fully integrated with a detailed nuclear plant model in order to realistically simulate plant accident scenarios. Finally, the improved ADS-IDAC model was calibrated, validated, and updated using actual nuclear plant crew performance data. This research led to the following general conclusions: (1) A relatively small number of branching rules are capable of efficiently capturing a wide spectrum of crew-to-crew variabilities. (2) Compared to traditional static risk assessment methods, ADS-IDAC can provide a more realistic and integrated assessment of human error events by directly determining the effect of operator behaviors on plant thermal hydraulic parameters. (3) The ADS-IDAC approach provides an efficient framework for capturing actual operator performance data such as timing of operator actions, mental models, and decision-making activities.
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    On the Theoretical Foundations and Principles of Organizational Safety Risk Analysis
    (2007-08-02) Mohaghegh-Ahmadabadi, Zahra; Mosleh, Ali; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    This research covers a targeted review of relevant theories and technical domains related to the incorporation of organizational factors into technological systems risk. In the absence of a comprehensive set of principles and modeling guidelines rooted in theory and empirical studies, all models look equally good, or equally poor, with very little basis to discriminate and build confidence. Therefore, this research focused on the possibility of improving the theoretical foundations and principles for the field of Organizational Safety Risk Analysis. Also, a process for adapting a hybrid modeling technique, in order to operationalize the theoretical organizational safety frameworks, is proposed. Candidate ingredients are techniques from Risk Assessment, Human Reliability, Social and Behavioral Science, Business Process Modeling, and Dynamic Modeling. Then, as a realization of aforementioned modeling principles, an organizational safety risk framework, named Socio-Technical Risk Analysis (SoTeRiA)is developed. The proposed framework considers the theoretical relation between organizational safety culture, organizational safety structure/practices, and organizational safety climate, with specific distinction between safety culture and safety climate. A systematic view of safety culture and safety climate fills an important gap in modeling complex system safety risk, and thus the proposed organizational safety risk theory describing the theoretical relation between two concepts to bridge this gap. In contrast to the current safety causal models which do not adequately consider the multilevel nature of the issue, the proposed multilevel causal model explicitly recognizes the relationships among constructs at multiple levels of analysis. Other contributions of this research are in implementing the proposed organizational safety framework in the aviation domain, particularly the airline maintenance system. The US Federal Aviation Administration (FAA), which has sponsored this research over the past three years, has recognized the issue of organizational factors as one of the most critical questions in the quest to achieve 80% reduction in aviation accidents. An example of the proposed hybrid modeling environment including an integration of System Dynamics (SD), Bayesian Belief Network (BBN), Event Sequence Diagram (ESD), and Fault Tree (FT), is also applied in order to demonstrate the value of hybrid frameworks. This hybrid technique integrates deterministic and probabilistic modeling perspectives, and provides a flexible risk management tool.