Time-dependent fragility analysis of a nuclear containment structure under internal pressure and chloride-induced corrosion

Ding, Jia-Yi; Fei, Zi-Jian; Feng, De-Cheng

doi:10.1016/j.nucengdes.2023.112695

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…(3) Structural failure criterion based on global strain The third structural failure criterion is based on the global strain of the CS [8,23,24]. The global strain is defined as follows:…”

Section: Structural Failure Criteriamentioning

confidence: 99%

“…Ren et al [23] assessed the vulnerability of the CS subjected to overpressure loads (hereafter, overpressure vulnerability) using the probability density evolution method and conducted a reliability assessment of the CS under different failure modes. Ding et al [24] evaluated the overpressure vulnerability and failure probability of the CS at different service lifetimes considering chloride-induced corrosion. Fan et al [25] investigated the overpressure vulnerability of the CS using a simplified analytical method and discussed the effect of factors such as concrete strength, steel liner thickness, and reinforcement amount on failure probability.…”

Section: Introductionmentioning

confidence: 99%

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Failure Behavior and Vulnerability of Containment Structures Subjected to Overpressure Loads Considering Different Failure Criteria

Guo,

Li,

Gong

2024

Buildings

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This paper investigates the failure behavior and vulnerability of the containment structure (CS) under internal pressure according to different functional and structural failure criteria. Through nonlinear numerical analysis, the deformation of the structure, degree of concrete cracking, and the distribution of stresses and strains in the prestressing tendons and steel liner under different failure criteria are compared. Combined with probabilistic analysis methods, the vulnerability curves, HCLPF capacities, and total failure probabilities of CS determined by different failure criteria are systematically discussed. Results show that there are some differences in the failure behavior and pressure capacity of CS under different failure criteria for both functional and structural failure modes. Under the same failure mode, the variability of the pressure capacities obtained using different criteria is relatively small. Generally, the total failure probabilities of CS subjected to overpressure loads determined by different failure criteria exhibit significant differences. If it is considered that all the investigated failure criteria have a certain ability to predict containment failure, the probabilistic assessment results derived from the functional failure criterion based on the fracture strain of steel liners and the structural failure criterion based on the global strain are deemed to be more reasonable.

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“…(3) Structural failure criterion based on global strain The third structural failure criterion is based on the global strain of the CS [8,23,24]. The global strain is defined as follows:…”

Section: Structural Failure Criteriamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Failure Behavior and Vulnerability of Containment Structures Subjected to Overpressure Loads Considering Different Failure Criteria

Guo,

Li,

Gong

2024

Buildings

View full text Add to dashboard Cite

show abstract

“…Thus, the simulated RCF here is a commonly used regular frame structure without considering the damage within existing structures (Ning & Xie, 2023; Park et al., 2018). The performance of the proposed approach would be further investigated on more complex scenarios such as an existing RC structure with planar or vertical irregularities in the following studies considering the degradation damages such as corroded reinforcements (Ding et al., 2023; Di Sarno & Pugliese, 2020).…”

Section: Case Studymentioning

confidence: 99%

Prior knowledge‐infused neural network for efficient performance assessment of structures through few‐shot incremental learning

Chen,

Feng,

Taciroglu

2024

Computer aided Civil Eng

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Structural seismic safety assessment is a critical task in maintaining the resilience of existing civil and infrastructures. This task commonly requires accurate predictions of structural responses under stochastic intensive ground accelerations via time‐costly numerical simulations. While numerous studies have attempted to use machine learning (ML) techniques as surrogate models to alleviate this computing burden, a large number of numerical simulations are still required for training ML models. Therefore, this study proposes a prior knowledge‐infused neural network (PKNN) for achieving efficient structural seismic response predictions and seismic safety assessment at a low computation cost. In this approach, first, the prior knowledge inherently within a theoretical dynamic model of a structure is infused into a neural network. Then, by utilizing the few‐shot incremental learning technique, this network would be further fine‐tuned by only a few numerical simulations. The resulting PKNN would be able to accurately predict the seismic response of a structure and facilitate seismic safety assessment. In this study, the PKNN's accuracy and computational efficiency are validated on a typical frame structure. The results revealed that the proposed PKNN could be used for accurately predicting the structural seismic responses and assessing the structural safety under a low computational cost.

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Copula-based cloud analysis for seismic fragility and its application to nuclear power plant structures

Lyu,

Fei,

Feng

2024

Engineering Structures

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Time-dependent fragility analysis of a nuclear containment structure under internal pressure and chloride-induced corrosion

Cited by 6 publications

References 29 publications

Failure Behavior and Vulnerability of Containment Structures Subjected to Overpressure Loads Considering Different Failure Criteria

Failure Behavior and Vulnerability of Containment Structures Subjected to Overpressure Loads Considering Different Failure Criteria

Prior knowledge‐infused neural network for efficient performance assessment of structures through few‐shot incremental learning

Copula-based cloud analysis for seismic fragility and its application to nuclear power plant structures

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