ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

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Accidents and incidents, such as the capsizing of the anchor handling vessel Bourbon Dolphin in 2007 and the unintended list of the drilling rig Scarabeo 8 in 2012, underline the need for addressing sensemaking in safety-critical situations in the maritime domain to reduce risks. Sensemaking and risks must be understood as a part of the organizational context of the incidents. This paper presents the results of a comprehensive qualitative literature review conducted to establish more knowledge on sensemaking in the context of safety-critical situations and on the relation between the concepts of sensemaking and resilience. In the obtained literature sensemaking is used as a frame of reference for understanding accidents; it is used in relation to critical situations or complex operations in general; it is described by some as a process creating situational awareness; and it is explained by others mainly in terms of how it relates to resilience. Sensemaking creates the context for being resilient; at the same time sources of resilience help to make sense of the situation. Few authors provide explicit characteristics of sensemaking in safety-critical situations, where discrepancies can be supported by redundant systems or by training to ensure the needed questioning attitude. There is a lack of literature regarding sensemaking in safety-critical situations and in relation to resilience that also addresses important aspects of training and system design. This article is available in the ASME Digital Collection at .

Human reliability analysis is a method, which evaluates the human error probabilities of human failure events, which may arise when human actions are required in operation of the considered facility or system. The objective of the work is to show the contribution of human failure events in probabilistic safety assessment with focus on comparison of this contribution over the different plant operating states. The method integrates the diagnosis and action part, which are evaluated for all steps, which are needed for modeling of the human action under investigation considering the parameters, which impact the human failure probability and which are obtained from database. The results show that the risk contribution of human failure events is notably higher in low power and shutdown states than in full power operation. The results show that the human factor in shutdown plant states is the most important risk contributor regarding the contribution to the core damage frequency and regarding the risk reduction worth and risk achievement worth. Only at some plant operating states, some other groups of equipment show larger risk achievement worth than group of human failure events. This article is available in the ASME Digital Collection at .

Many unfortunate and unintended adverse industrial incidents occur across the U.S. each year, and the nuclear industry is no exception. Depending on the severity, these incidents can be problematic for people, the facilities, and surrounding environments. These incidents occur for a number of varying reasons, but more often than not, human error is an accomplice. This article explores whether the complexity and changing technologies, which affect the way operators interact within the systems of the nuclear facilities, exacerbate the severity of incidents caused by human error. A review of nuclear incidents in the U.S. from 1955 to 2010 reaching level three or higher on the International Nuclear Event Scale (INES) scale was conducted. The cost of each incident at facilities that had recently undergone technological changes affecting plant operator's jobs was compared to those facilities which had not undergone changes. A t-test was applied and determined a statistically significant difference between the two groups. This affirmed that technological advances at nuclear facilities that affect how operators interact within the plant system increase the severity of resulting incidents. Next, a follow-on study was conducted to determine the impact from the incorporation of new technologies into nuclear facilities. The data indicated that spending more money on upgrades increased the capacity of the facility as well as the number of incidents reported, but the incident severity was minor. This article is available in the ASME Digital Collection at .

The development of gas fields in the Peruvian Amazon jungle required the construction of new pipelines in unexplored tropical forest areas, which faced unexpected logistic and operative difficulties, including several leaks and ruptures. This article briefly describes those early failures and how collaboration among people with notable different interests, work cultures, and idiosyncrasies could eventually handle the pressure by regulatory agencies, shareholders, media, environmental NGOs, and political parties. All failures in the forest were due to soil movements, related to a poor understanding of topological and geotechnical characteristics of the hilly forest, and how water flow influenced soil stability. The reasons for public outrage after the failures are discussed, including politically fueled nationalistic sentiments, transnationally driven natural resource exploitation and indigenous power struggles. The oil and gas development was itself the main reason for most trouble; but leaks and failures severely weakened the position of the operator, who then implemented some methods to regain public consensus. This article is available in the ASME Digital Collection at .

Integrated operations (IO) is an ongoing change process in the oil and gas industry. New technological opportunities enable working in new ways that involve an integration of onshore and offshore personnel. This paper analyzes the results of two rounds of data gathering in an onshore drilling support center, in terms of the development of resilience. The first round took place in 2004/2005 and the second in 2012. This study presents a framework for the analysis of resilience and has used the case company as a mean of testing the framework. Our findings indicate that the support center has taken a huge step in the direction of becoming more resilient. The drilling company has tested a number of designs and sizes of support centers, each of which has different pros and cons. For the drilling discipline to develop resilience, it is essential that the number of rigs supported by a center is not too large, as they must not become involved in too many rigs and drilling operations. Our findings also indicate that the suggested framework provides a good overall picture of the development of resilience in the case company. This article is available in the ASME Digital Collection at .

Engineers have developed different design methodologies capable of identifying failure modes of engineering systems. The most common methods used in industry are failure modes and effects analysis, and failure modes effects and criticality analysis. Nevertheless, such methodologies have a significant limitation regarding incorporating the final user in the analysis and are not suited to identifying potential failure modes caused by physical human–system interactions. Engineering methods usually have a lack of sufficient attention to human–system interactions during the early design stages, even though introducing human factors principles is recognized as an essential analysis during the design process. As a result, designers rely on developing detailed and expensive physical or virtual prototypes to evaluate physical human–system interactions and identify potential failure modes caused by such interactions incorporating design modifications after a prototype is developed can be time-consuming, costly, and if significant changes are needed, the entire prototype requires to be constructed again. Identifying system–user interactions and possible failure modes associated with such interactions before developing a prototype can significantly improve the design process. In previous work, the authors introduced the function–human error design method (FHEDM), a tool capable of distinguishing possible human–system interaction failure modes using a functional basis framework. In this work, we examined the implementation of FHEDM within 148 products extracted from the design repository. The results are grouped in the composite function–user interaction error (FUIE) matrix, which can be used as a preliminary design database presenting information regarding the possible human error present in function-flow combinations. This article is available in the ASME Digital Collection at .

Organizational and management errors and failures represent a very significant causal influence in industrial accidents. A method, organizational failure analysis (OFA) is described for in-depth identification of organizational deficiencies and failures that can lead to accidents. The method was developed on the basis of an extensive data collection from safety management audits, accident and incident investigations, and emergency training exercises led by the author over a period of 26 years. From these data, a number of models of organizational performance and management behavior are derived. The models allow a semi-automated application of the method, which is important for the application to large organizations. The method is described with examples, and the results of several studies aimed at validating the method are given. This article is available in the ASME Digital Collection at .

The human factor of lighting is examined through the elements of sunlight, blue light, and how it affects the circadian rhythm, and ultimately how it impacts the work schedule of workers. The intricacies of light and human vision are explored to zero in on the issues with disrupting the circadian rhythm for these employees. Modifications can be incorporated into the design of the working environment to improve efficiency, productivity, safety, and job satisfaction in an effort to deal with these issues. Recommendations are provided in an effort to mitigate or reduce the disturbances to the circadian rhythm and improve overall performance for night shift workers. This article is available in the ASME Digital Collection at .

While a majority of system vulnerabilities such as performance losses and accidents are attributed to human errors, a closer inspection would reveal that often times the accumulation of unforeseen events that include both component failures and human errors contribute to such system failures. Human error and functional failure reasoning (HEFFR) is a framework to identify potential human errors, functional failures, and their propagation paths early in design so that systems can be designed to be less prone to vulnerabilities. In this paper, the application of HEFFR within the complex engineering system domain is demonstrated through the modeling of the Air France 447 crash. Then, the failure prediction algorithm is validated by comparing the outputs from HEFFR and what happened in the actual crash. Also, two additional fault scenarios are executed within HEFFR and in a commercially available flight simulator separately, and the outcomes are compared as a supplementary validation. This article is available in the ASME Digital Collection at .

Human error is one of the primary reasons for accidents in complex industries like aviation, nuclear power plant management, and health care. Physical and cognitive workload, flawed information processing, and poor decision making are some of the reasons that make humans vulnerable to error and lead to failures and accidents. In many accidents and failures, oftentimes, vulnerabilities that are embedded in the system, in the form of design deficiencies and poor human factors, lead to latent or catastrophic failures, but the last link is a human operator who gets blamed or worse, injured. This paper introduces an early design human performance assessment framework to identify what type of digital prototyping methodologies are appropriate to detect the deviation of the operator's performance due to an emergency condition. Fire in a civilian aircraft cockpit was introduced as a performance shaping factor (PSF). Ergonomics performance was evaluated using two prototyping strategies: (1) a computational prototyping framework includes digital human modeling (DHM) and computer-aided design; and (2) a novel mixed prototyping framework includes motion capture, DHM, and virtual reality. Results showed that the mixed prototyping framework can simulate emergency scenarios with increased realism and also has the potential to incorporate subjective aspects of ergonomics outcomes, overcoming the underlying lack of design knowledge in conventional early design methodologies. This article is available in the ASME Digital Collection at .

Fire is one of the most critical initiating events that can lead to core damage in nuclear power plants (NPPs). To evaluate the potential vulnerability of plants to fire hazards, fire probabilistic risk assessment (PRA) is commonly conducted. Manual fire protection features, performed by the first responders (e.g., fire brigade), play a key role in preventing and mitigating fire-induced damage to the plant systems. In the current fire PRA methodology of NPPs, there are two main gaps in the modeling of manual fire protection features: (i) the quantification of the first responder performance is solely based on empirical data (industry-wide historical fire events), and so the plant-specific design and conditions cannot be explicitly considered; and (ii) interactions of first responders with fire propagation are not fully captured. To address these challenges, the authors develop a model-based approach, grounded on human reliability analysis (HRA) and coupled with the fire dynamics simulator (FDS), to model the first responder performance more realistically and consider the interface between the first responder performance and fire propagation more explicitly. In this paper, the HRA-based approach is implemented in an integrated PRA (I-PRA) methodological framework for fire PRA and applied to a switchgear room fire scenario of an NPP. The proposed model-based approach (a) adds more realism to fire PRA and so to risk assessment in NPPs and (b) provides opportunities for sensitivity and importance measure analyses with respect to design conditions; therefore, contributes to risk management in NPPs. This article is available in the ASME Digital Collection at .

To advance emergency response (ER) modeling in probabilistic risk assessment (PRA), this research offers a new methodology that explicitly incorporates the spatiotemporal evolution of underlying physical and social phenomena and their bidirectional interactions. While this methodology is applicable for various ER scenarios on different spatial and temporal scales, this paper focuses on advancing ER modeling for a nuclear power plant (NPP) internal fire. This paper provides a thorough review and categorization of existing studies on internal fire ER modeling for NPPs and highlights the contributions of this research. This paper then develops a new methodology for fire ER modeling by integrating an agent-based model of first responder performance (FRP) with a fire hazard propagation (FHP) model through a shared geographical information system (GIS)-based spatial simulation environment. This research is the first to explicitly incorporate space (in addition to time) into the FRP modeling within ER modeling of NPP fire probabilistic risk assessment (fire PRA). In addition, this research develops a GIS-based interface between FRP and FHP that has the capability of transferring both spatial and temporal information in a bidirectional way. Although this paper is focused on a fire ER scenario, the new methodology developed in this paper can contribute to modeling external control room (ExCR) human performance in other contexts, such as diverse and flexible coping strategy (FLEX), maintenance, and offsite first responders in level 3 PRA. This article is available in the ASME Digital Collection at .

The determination of weights in decision-making problems can be deduced as a complex process of preference formation. Preferences are expressions of behavioral attitudes and are affected by external circumstances, such as risk and ambiguity. The objective of this research is to examine the impact of both the human factor and the weighting methods on the weighting process in decision-making problems. Based on relevant literature a new methodology is proposed and applied to identify with the use of a psychometric function the behavioral attitudes of decision-making analysts against risk and ambiguity. Furthermore, the examination of process-related features such as the weighting method, the weighting scale and the weighting problem's presentation provides additional knowledge on the understanding of the weighting process in decision-making problems. Thus, an original survey is designed, aiming at: (a) the identification of the respondents' attitudinal preferences based on multiple personality tests and (b) the elicitation of weight assignments through the use of different weighting tasks and subtasks. The findings reveal that the weightings and their consistency are significantly affected by the elicitation method, the nature of the weighting scale and the problem's framing. It is also interesting that the decision analysts' behavioral traits, in association with the problem's methodological aspects, affect the weight assignments, thus providing evidence for the potential to predict weightings in the decision-making process. This article is available in the ASME Digital Collection at .

This paper discusses the challenges with collecting positivistic empirical data (objective, observable, reliable, replicable, experimental, and true) in human reliability analysis (HRA), and illustrates it by presenting the difficulties in collecting empirical data on the performance-shaping factor (PSF) complexity. The PSF complexity was chosen to illustrate the difficulties with empirically collecting data because it is included in many HRA guidelines and it has been discussed as an important PSF to understand error rates in large accident scenarios. This paper discusses the challenges with collecting empirical data from a pure positivistic paradigm with experiments, as well as from literature reviews and data from event reports, training, and operations. The paper concludes that because of all the challenges with the positivistic empirical data collections methods in HRA, we should discuss whether experts' judgements could be a better approach to obtain HRA data and error rates. In a postpositivistic view, qualitative data or experts' judgments could also be looked at as empirical data if the data were collected in a systematic and transparent way. This article is available in the ASME Digital Collection at .

Risk analyses require proper consideration and quantification of the interaction between humans, organization, and technology in high-hazard industries. Quantitative human reliability analysis approaches require the estimation of human error probabilities (HEPs), often obtained from human performance data on different tasks in specific contexts (also known as performance shaping factors (PSFs)). Data on human errors are often collected from simulated scenarios, near-misses report systems, and experts with operational knowledge. However, these techniques usually miss the realistic context where human errors occur. The present research proposes a realistic and innovative approach for estimating HEPs using data from major accident investigation reports. The approach is based on Bayesian Networks used to model the relationship between performance shaping factors and human errors. The proposed methodology allows minimizing the expert judgment of HEPs, by using a strategy that is able to accommodate the possibility of having no information to represent some conditional dependencies within some variables. Therefore, the approach increases the transparency about the uncertainties of the human error probability estimations. The approach also allows identifying the most influential performance shaping factors, supporting assessors to recommend improvements or extra controls in risk assessments. Formal verification and validation processes are also presented. This article is available in the ASME Digital Collection at .