An official website of the United States government
Although the natural gas pipeline network is the most efficient and secure transportation mode for natural gas, it remains susceptible to external and internal risk factors. It is vital to address the associated risk factors such as corrosion, third-party interference, natural disasters, and equipment faults, which may lead to pipeline leakage or failure. The conventional quantitative risk assessment techniques require massive historical failure data that are sometimes unavailable or vague. Experts or researchers in the same field can always provide insights into the latest failure assessment picture. In this paper, fuzzy set theory is employed by obtaining expert elicitation through linguistic variables to obtain the failure probability of the top event (pipeline failure). By applying a combination of T- and S-Norms, the fuzzy aggregation approach can enable the most conservative risk failure assessment. The findings from this study showed that internal factors, including material faults and operational errors, significantly impact the pipeline failure integrity. Future directions should include sensitivity analyses to address the uncertainty in data to ensure the reliability of assessment results.
more »
« less
Optimizing natural gas pipeline risk assessment using hybrid fuzzy bayesian networks and expert elicitation for effective decision-making strategies
Natural gas pipelines are susceptible to external and internal risk factors, such as corrosion, environmental conditions, external interferences, construction and design faults, and equipment failures. Bayesian Networks (BN) is a promising risk assessment approach widely used to evaluate these risk factors. One of BN's inherent limitations is its inability to accurately capture statistical dependencies and causal relationships, which can be overcome by incorporating expert elicitation into BN. To account for uncertainty and vagueness in assessing pipeline failure risks, fuzzy set theory (FST) can be combined with BN, commonly known as Fuzzy Bayesian Networks (FBN). This study developed an FBN framework that uses linguistic variables to calculate fuzzy probability (FPr) through domain expert elicitation, and crisp probabilities (CPr) are computed using historical incident data from the Pipeline and Hazardous Materials Safety Administration (PHMSA). Based on the findings from the case study of the Midwest region of the USA, external factors, i.e., third-party interference, outside force, and other incidents, significantly impact pipeline performance and reliability. Diagnosis inference indicates that in the Midwest region of the USA, pipeline material and age are critical factors leading to corrosion failure by threatening pipeline integrity. The findings from this study suggested that a targeted risk mitigation strategy is paramount for minimizing the risks associated with pipeline networks.
more »
« less
- Award ID(s):
- 1750316
- PAR ID:
- 10522975
- Publisher / Repository:
- ScienceDirect
- Date Published:
- Journal Name:
- Gas Science and Engineering
- Volume:
- 125
- Issue:
- C
- ISSN:
- 2949-9089
- Page Range / eLocation ID:
- 205283
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Recent advances in post-windstorm reconnaissance have accelerated the amounts of perishable building performance data being collected after extreme windstorms, necessitating better frameworks for knowledge discovery from the data. One particularly promising approach to this need is Bayesian Networks (BN), which have grown in their application in natural hazards research due to their ability to explicitly model causal factors. In this study, a Naïve Bayes Network (NBN) was first developed to observe the influence of wind speed ratio, roof shape, number of stories, roof cover, and pre/post-IBC (2002) on the damage class of a structure and predict the probability of each damage class given a specified scenario. This initial model was derived solely from empirical data and the parameters of influence are modelled with conditional independence, and limiting the model’s use. An illustrative hybrid Bayesian Network is also proposed which combines empirical data, known wind engineering theory, and expert opinion to formulate a more holistic model of structural performance in windstorms better suited for parameter inference and building performance predictions.more » « less
-
Vigilance, the ability to sustain attention, is critical in healthcare, yet resident physicians face significant sleep deprivation, increasing their risk of vigilance decrement and medical errors. This study aimed to develop a predictive model of vigilance in this population using contextual factors, physiological measures, and eye-tracking data. Fifteen resident physicians participated in psychomotor vigilance tests (PVT) under sleep-deprived and non-sleep-deprived conditions, and completed questionnaires assessing sleep, anxiety, and workload. Bayesian Networks (BN) were employed to model vigilance, featuring layers for contextual factors (sleep, anxiety), performance (PVT reaction time), and observable features (eye movement, physiological responses). The three-layered BN integrating both contextual and multi-sensor (eye-tracking and physiological) data demonstrated the best prediction accuracy, compared to BNs with fewer layers and/or only one sensor type. This demonstrates that combining continuous physiological and eye-tracking data with contextual information enhances the prediction of vigilance decrement in resident physicians. This study contributes to the development of predictive tools for mitigating vigilance decrement and the future design of intervention strategies in demanding clinical settings.more » « less
-
ABSTRACT In 2012, a regional risk assessment was published that applied Bayesian networks (BN) to the structure of the relative risk model. The original structure of the relative risk model (RRM) was published in the late 1990s and developed during the next decade. The RRM coupled with a Monte Carlo analysis was applied to calculating risk to a number of sites and a variety of questions. The sites included watersheds, terrestrial systems, and marine environments and included stressors such as nonindigenous species, effluents, pesticides, nutrients, and management options. However, it became apparent that there were limits to the original approach. In 2009, the relative risk model was transitioned into the structure of a BN. Bayesian networks had several clear advantages. First, BNs innately incorporated categories and, as in the case of the relative risk model, ranks to describe systems. Second, interactions between multiple stressors can be combined using several pathways and the conditional probability tables (CPT) to calculate outcomes. Entropy analysis was the method used to document model sensitivity. As with the RRM, the method has now been applied to a wide series of sites and questions, from forestry management, to invasive species, to disease, the interaction of ecological and human health endpoints, the flows of large rivers, and now the efficacy and risks of synthetic biology. The application of both methods have pointed to the incompleteness of the fields of environmental chemistry, toxicology, and risk assessment. The low frequency of exposure‐response experiments and proper analysis have limited the available outputs for building appropriate CPTs. Interactions between multiple chemicals, landscape characteristics, population dynamics and community structure have been poorly characterized even for critical environments. A better strategy might have been to first look at the requirements of modern risk assessment approaches and then set research priorities.Integr Environ Assess Manag2021;17:79–94. © 2020 SETACmore » « less
-
Abstract End users are increasingly using trigger-action platforms like If-This-Then-That (IFTTT) to create applets to connect smart-home devices and services. However, there are inherent implicit risks in using such applets—even non-malicious ones—as sensitive information may leak through their use in certain contexts ( e.g., where the device is located, who can observe the resultant action). This work aims to understand to what extent end users can assess this implicit risk. More importantly we explore whether usage context makes a difference in end-users’ perception of such risks. Our work complements prior work that has identified the impact of usage context on expert evaluation of risks in IFTTT by focusing the impact of usage context on end-users’ risk perception. Through a Mechanical Turk survey of 386 participants on 49 smart-home IFTTT applets, we found that participants have a nuanced view of contextual factors and that different values for contextual factors impact end-users’ risk perception differently. Further, our findings show that nudging the participants to think about different usage contexts led them to think deeper about the associated risks and raise their concern scores.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.jgsce.2024.205283
