Limitations of traditional tools for beyond design basis external hazard PRA

Vaishanav, Pragya; Gupta, Abhinav; Bodda, Saran Srikanth

doi:10.1016/j.nucengdes.2020.110899

Cited by 7 publications

(2 citation statements)

References 18 publications

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“…The concept of Bayesian inference has been widely used in many applications to estimate unknown parameters with uncertainty (Bodda et al, 2020a,b;Keller et al, 2014;Vaishanav et al, 2020;Viallet et al, 2017).…”

Section: Estimation Methods and Information Criteriamentioning

confidence: 99%

A Methodological Approach to Update Ground Motion Prediction Models Using Bayesian Inference

Bodda

Keller

Gupta

et al. 2021

Pure Appl. Geophys.

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In recent decades, prediction of ground motion at a specific site or a region is of primary interest in probabilistic seismic hazard assessment (PSHA). Historically, several ground motion prediction equation (GMPE) models with different functional forms have been published using strong ground motion records available from NGA-West and European databases. However, low-to-moderate seismicity regions, such as Central & Eastern United States and western Europe, is characterized by limited strong-motion records in the magnitude-distance range of interest for PSHA. In these regions, the available data for the development of empirical GMPEs is very scarce and limited to small magnitude events. For these regions, the general practice in PSHA is to consider a set of GMPEs developed from data sets collected in other regions with high seismicity. This practice generates an overestimation of the seismic hazard for the low seismicity regions. There are two potential solutions to overcome this problem: (i) a new GMPE model can be developed; however, development of such a model can require significant amount of data which is not usually available, and (ii) the existing GMPE models can be recalibrated based on the data sets collected in the new region rather than developing a new GMPE model. In this paper, we propose a methodological approach to recalibrate the coefficients in a GMPE model using different algorithms to perform Bayesian inference. The coefficients are recalibrated for a subset of European Strong-Motion (ESM) database that corresponds to low-to-moderate seismicity records. In this study, different statistical models are compared based on the functional form given by the chosen GMPE, and the best model and algorithm are recommended using the concept of information criteria.

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Section: Estimation Methods and Information Criteriamentioning

confidence: 99%

A Methodological Approach to Update Ground Motion Prediction Models Using Bayesian Inference

Bodda

Keller

Gupta

et al. 2021

Pure Appl. Geophys.

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“…Probabilistic Risk Assessment (PRA) is a crucial step in the risk analysis of complex systems, especially of safety-critical systems. [16][17][18] It can identify potential accident scenarios of man-machine systems using Boolean logic methods [e.g., fault trees (FTs), event trees (ETs), event sequence diagrams (ESDs)] to characterize deterministic relationships between basic events, 19 and then assess their likelihoods and consequences and improve system safety and operation. 20 However, current PRA methods focus more on serial tasks in a certain risk scenarios without MOL.…”

Section: Introductionmentioning

confidence: 99%

Risk assessment of man‐machine systems under safety‐critical multitasking situations

Che,

Li,

Zeng

et al. 2024

Quality & Reliability Eng

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Multitasking is increasingly common in highly complex and safety‐critical systems, especially under abnormal situations. Mental overload (MOL) may occur and result in forgetting/mistaking tasks uncertainly. Such probabilistic errors could have catastrophic consequences and contribute greatly to the multitasking risk of man‐machine systems. In this paper, to better identify the risks of safety‐critical multitasking situations, MOL mechanism is investigated and a risk analysis method considering MOL is proposed. MOL occurs when the demand for resources estimated by Multiple Resources Model exceeds an operator's ability. Then, the operator's performance degrades, and s/he tends to mistake or abandon parts of tasks. Based on the MOL mechanism, a MOL‐performance dependency (MOL‐PDEP) gate is proposed to incorporate MOL into risk analysis. Its inputs are concurrent tasks, and it triggers the related hazardous human behavior events with certain probabilities if MOL occurs. Through this gate, the dependence among these events and their nondeterministic cause relationships to MOL are added to traditional fault tree (FT), which presents a challenge issue to FT analysis. An implicit method is proposed to analyze the FT with MOL‐PDEP gate and calculate the accident probability. A case study on a helicopter crash accident demonstrates the effectiveness of the proposed method.

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Evaluation of Fault Tree Analysis Algorithms for Probabilistic Risk Assessment: A Systematic Comparative Study

Afshan,

Bodda,

Gupta

et al. 2024

Lecture Notes in Civil Engineering

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Limitations of traditional tools for beyond design basis external hazard PRA

Cited by 7 publications

References 18 publications

A Methodological Approach to Update Ground Motion Prediction Models Using Bayesian Inference

A Methodological Approach to Update Ground Motion Prediction Models Using Bayesian Inference

Risk assessment of man‐machine systems under safety‐critical multitasking situations

Evaluation of Fault Tree Analysis Algorithms for Probabilistic Risk Assessment: A Systematic Comparative Study

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