Effects of Structural and Seismic Factors on the Constant-Strength Ductility Spectra Based on NGA-West2 Database

Hu, Jinjun; Lai, Qinghui; Liu, Xuan; Xie, Lili

doi:10.1155/2020/8820582

Cited by 3 publications

(1 citation statement)

References 18 publications

(29 reference statements)

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“…Ji et al [11] studied the effect of the ductility demand coefficient on the constant-ductility strength demand spectrum, and they found that, with an increase or decrease in the ductility demand coefficient, the strength demand coefficient and the seismic resistance coefficient showed a specific variation law. In addition, the effects of k 2 , ζ, the hysteretic model, and pinching on the constant-strength ductility demand spectrum were also studied [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Effects of Different Factors on Damage Potential Ranking

Lai

Xie

et al. 2023

Sustainability

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A quantitative evaluation of the damage potential of ground motions to structures can provide a basis for the selection of input ground motions. To determine the main factors influencing the damage potential ranking of ground motions, the corresponding effect factors were analyzed. First, the structural period range from 0.05 to 10 s was divided into three types of period ranges based on an improved Newmark–Hall spectrum. The intensity measures (IMs) that can characterize the damage potential in every period range were determined. Second, the effect of yield strength coefficient (Cy), vibration period (T), and type of site on the damage potential ranking are explained. A recommended damage potential ranking is given in the same period range. Finally, to demonstrate the rationality of the recommended damage potential ranking in this paper, two representative reinforced concrete (RC) shear structure models are established for analysis. For the same type of structures, the damage potential rankings under different Cy and T conditions have high correlation with the recommended damage potential ranking, and the discreteness is very low. When considering the site factors, the corresponding correlation and dispersion change little. Based on the analysis of two typical structural models, the R2 between the recommended damage potential ranking and structural response ranking were 0.89 and 0.94, respectively. It is proven that the methods of Cy, T, and the type of site are reasonable when establishing the recommended damage potential ranking in this paper. This study provides a theoretical basis for simplifying the evaluation of ground motion damage potential and for selecting input ground motions.

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Section: Introductionmentioning

confidence: 99%

Analysis of the Effects of Different Factors on Damage Potential Ranking

Lai

Xie

et al. 2023

Sustainability

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A deep learning approach to rapid regional post‐event seismic damage assessment using time‐frequency distributions of ground motions

Tian

et al. 2021

Earthq Engng Struct Dyn

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Every year, earthquakes result in severe economic losses and a significant number of casualties worldwide. In limiting the losses that occur after these extreme events, timely and accurate assessment of seismic damages and mobilizing proportionate post‐event relief efforts play crucial roles. Traditional on‐site investigation generally results in prolonged evaluation windows. Several computational alternatives exist that show promise in addressing the downsides of the traditional approach. Damage estimates based on pre‐computed fragility libraries can provide near‐real time seismic damage quantification, but at present, they are coarse and involve considerable uncertainties. Estimates based on nonlinear time‐history analyses simulate the seismic response in greater detail, yet due to the computation and data requirements, their use at the regional scale is challenging. Given this perspective, herein, a rapid regional post‐event seismic damage assessment procedure based on convolutional neural network (CNN) is proposed. In this approach, an inventory of buildings, anticipated ground motion datasets, and corresponding damage levels for a region are brought together into a scenario bank. The time‐frequency distribution graphs of the ground motions, which serve as detailed visual representations of their frequency‐domain as well as time‐domain features, are generated. These data are then used to train CNN models, which could predict the damage states. The proposed methodology is verified through two numerical studies—one for an individual building, and the other, regional case, involving the buildings in the Tsinghua University campus. The results confirm that the proposed method offers prediction results with sufficient accuracy in near real‐time.

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