Bayesian‐network‐based risk modeling and inference for structures under a sequence of main and aftershocks

Mun, Changuk; Song, Junho

doi:10.1002/eqe.3604

Cited by 10 publications

(1 citation statement)

References 39 publications

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“…This paper aims to further utilize BN for modeling and analyzing APSHA. In addressing this issue, Mun and Song (2022) suggested a risk modeling approach that employs BN to consider a sequence of mainshocks and aftershocks. Tubaldi et al (2022) utilized a BN‐based probability framework to update the aftershock risk of bridges.…”

Section: Introductionmentioning

confidence: 99%

Aftershock probabilistic seismic hazard analysis based on enhanced Bayesian network considering frequency information

Liu,

2024

Computer aided Civil Eng

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Bayesian network (BN) is an important tool in probabilistic seismic risk analysis (PSRA) due to its holistic nature and powerful probabilistic inference capabilities. However, while the information that can be stored by BN includes variable values, probability distributions, and relationships between variables, it does not involve event quantities. The results obtained from PSRA and probabilistic seismic hazard analysis (PSHA) are commonly frequency‐related (the number of occurrences per unit time), fundamentally describing event numbers. BN assumes a fixed event number relationship between nodes, but the aftershock counts vary for different mainshock magnitudes. This limitation further restricts its effectiveness in lifespan PSRA for structures. Therefore, this study aims to propose an enhanced BN (EBN) for modeling aftershock PSHA (APSHA) considering frequency information. This paper first introduces the modeling method using BN, including PSHA and a simplified model of APSHA using Båth's law. Next, an EBN that can effectively address frequency issues between the mainshock and aftershocks is presented for modeling APSHA considering the modified Omori's law, while the mathematical principles of both forward and backward inferences for the EBN are elaborated in detail. Then, the article carefully analyzes the utilization of the EBN for APSHA modeling under different scenarios where the information regarding the mainshock is known to varying extents, followed by the extension of the EBN in a time domain to obtain time‐dependent aftershock hazard. Finally, an example of APSHA in a specific location in China is illustrated.

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Section: Introductionmentioning

confidence: 99%

Aftershock probabilistic seismic hazard analysis based on enhanced Bayesian network considering frequency information

Liu,

2024

Computer aided Civil Eng

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Seismic vulnerability assessment of electrical substation system based on the hybrid fragility functions and Bayesian network

Fu,

Guo,

et al. 2024

Earthq Engng Struct Dyn

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Substations function as neural hubs within power systems and play pivotal roles in the aggregation, transformation, and distribution of electrical energy. Previous experiences indicate that substation systems are highly susceptible to damage under earthquakes, resulting in a subsequent decrease in power supply functionality. To mitigate the risk of earthquake‐induced damage, a novel approach based on Bayesian theory is proposed to assess the seismic vulnerability of complex engineering systems. The proposed method initially obtains the prior distribution of seismic fragility parameters for electrical equipment through numerical simulations of coupled finite element models. Subsequently, seismic damage survey data and Bayesian updating rules are applied to update the prior probability, obtaining a hybrid fragility function for electrical equipment. The Bayesian network was constructed using logical relations among internal electrical components in the substation, aiming to quantify the seismic vulnerability of the system across different functionality indicators. Finally, the causal inference technique was employed to quantify the importance of various components and equipment. A realistic case study on a typical 220/110/35 kV substation system was performed using the proposed method. The results demonstrate that the method improves the confidence level of the equipment fragility curves, reduces the computational workload of the system vulnerability analysis, and provides a theoretical basis for improving substation performance and formulating post‐disaster maintenance plans.

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Main-Aftershock Modeling According to Measured Records

Liu

Jiang

Liu

et al. 2022

Lecture Notes in Civil Engineering

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Bayesian‐network‐based risk modeling and inference for structures under a sequence of main and aftershocks

Cited by 10 publications

References 39 publications

Aftershock probabilistic seismic hazard analysis based on enhanced Bayesian network considering frequency information

Aftershock probabilistic seismic hazard analysis based on enhanced Bayesian network considering frequency information

Seismic vulnerability assessment of electrical substation system based on the hybrid fragility functions and Bayesian network

Main-Aftershock Modeling According to Measured Records

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