On efficient seismic fragility assessment using sequential Bayesian inference and truncation scheme: A case study of shear wall structure

Lee, Sangwoo; Kwag, Shinyoung; Ju, Bu-seog

doi:10.1016/j.compstruc.2023.107150

Cited by 3 publications

(3 citation statements)

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“…IM may include spectral acceleration or peak ground acceleration (PGA). In this study, PGA is used as IM, considering consistency with previous studies [11,21,22] for fragility analysis of shear walls. A lognormal cumulative distribution for a specific IM often defines the fragility function.…”

Section: Cloud Analysis (Ca)mentioning

confidence: 97%

“…An analysis of the shear wall under cyclic loading was performed through the ABAQUS implicit solver. The developed model is shown in Figure 2(b) [20][21][22]. The walls were modeled by a 4-node shell element with reduced integration (S4R) and a mesh size of 100 mm, assuming that the rebar is perfectly bonded with the concrete.…”

Section: Introductionmentioning

confidence: 99%

“…To International Journal of Energy Research represent the nonlinear behavior of the system, a concrete damage plasticity model proposed by Lubliner et al [23] and Lee and Fenves [24] was applied to the rectangular shear walls. It has been used in many studies on the nonlinear behavior of concrete structures [20][21][22][25][26][27][28][29][30][31][32]. In addition, the uniaxial concrete tension stiffening model and compression hardening model that were proposed by Maekawa and Okamura [33] and Izumo [34], respectively, were used in this study.…”

Section: Introductionmentioning

confidence: 99%

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A Development of Seismic Fragility Curve of Box-Type Squat Shear Walls Using Data-Driven Surrogate Model Techniques

Lee,

Kwag,

2024

International Journal of Energy Research

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Recent experience with the strongest earthquakes greater than magnitude 5.4 in Korea leads to public interest in the safety and resilience of critical infrastructures. There are many nuclear power plants near the epicenters of the earthquakes. Nuclear power plants are essential infrastructures that provide stable and enough energy for human life, and simultaneously, control systems for the safety and security of nuclear power plants are critical due to the risk of nuclear accidents on public health and the environment. The nuclear area uses probabilistic risk assessment to estimate the risk of structures, systems, and components in nuclear power plants, and the evaluation of the fragility curve is a key process for probabilistic risk assessment. The challenges of a seismic fragility analysis lie in estimating the influence of various uncertainties in material, geometry, and earthquake and improving the existing fragility analysis methods, which require time-consuming nonlinear time history analysis. Thus, this paper conducts a multivariate seismic fragility analysis using surrogate models for reinforced concrete squat shear walls and proposes a simplified closed form equation for a chosen surrogate seismic demand model. The surrogate models are trained and validated by several approaches: response surface method, support vector machine, Gaussian process regression, and neural network. In addition, a correlation analysis is used to evaluate the relative importance of the variables to the seismic demand to simplify the surrogate model further. Finally, simplified surrogate models based on the importance of the variables are proposed as closed form of polynomials, and the performance of these models on the fragility analysis is evaluated.

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Section: Cloud Analysis (Ca)mentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%