Cumulative absolute velocity (CAV) seismic hazard assessment for Taiwan

Wu, Min; Wang, J. P.; Chiang, Pei En

doi:10.1080/13632469.2020.1803161

Cited by 11 publications

(4 citation statements)

References 29 publications

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“…12 (Figure 6(c)), the cumulative energy ratio reaches 46% using the first 30 components. An alternative widely used intensity measure is the Cumulative Absolute Velocity (CAV), which shows the continuous accumulation of the acceleration during an earthquake ( [34], [35]). The ith component-to-original ground motion CAV ratio, Γi, for the near-fault and far-fault ensembles, is given by: ( )…”

Section: Decomposition Results and Discussionmentioning

confidence: 99%

Multi-pulse decomposition for nonlinear seismic analysis of structural systems

Ahmadi

Salami

Risi

et al. 2022

Soil Dynamics and Earthquake Engineering

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Section: Decomposition Results and Discussionmentioning

confidence: 99%

Multi-pulse decomposition for nonlinear seismic analysis of structural systems

Ahmadi

Salami

Risi

et al. 2022

Soil Dynamics and Earthquake Engineering

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Section: Introductionmentioning

confidence: 99%

“…However, due to the complexity of earthquake ground motion and the uncertainty in seismic resistance, PGV and PGA only represent the amplitude characteristics of ground motions, and they are not sufficient in assessing earthquake damage (Campbell & Bozorgnia, 2019; M. H. Wu et al., 2022). Cumulative absolute velocity (CAV) includes information on both the amplitude and duration of the ground motion and has been applied in many scenarios with much success since its inception.…”

Section: Introductionmentioning

confidence: 99%

“…To avoid the mistake of predicting potential damage from a small earthquake based on predicted PGA, the Electric Power Research Institute first proposed to use CAV to assess the earthquake's impact on nuclear power plants (EPRI, 1988). In addition, it has also been applied to assess the damage of building structures (Bradley, 2015; Cabanas et al., 1997; EPRI, 2006), seismic hazard (M. H. Wu et al., 2022), soil liquefaction (Bray & Macedo, 2017; Kramer & Mitchell, 2006), and the basin effect (J. P. Wang et al., 2018). In the Istanbul EEW system, CAV based on the actual ground motion is compared against pre‐set thresholds to determine the alarm levels to be issued (Alcik et al., 2009).…”

Section: Introductionmentioning

confidence: 99%

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Cumulative absolute velocity prediction for earthquake early warning with deep learning

Wang

Zhao

Qian

et al. 2023

Computer aided Civil Eng

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A rapid and accurate estimate of earthquake damage is a key component in a successful earthquake early warning (EEW) system. The cumulative absolute velocity (CAV) is an important and widely used parameter to measure ground motion intensity, but it cannot be correctly estimated via the traditional approach with the limited information available in typical EEW systems. Therefore, current EEW systems cannot effectively use CAV to predict earthquake damage. Herein, a CAV prediction model (DLcav) based on convolutional neural networks was proposed for EEW systems. DLcav is an end‐to‐end solution to continuously predict CAV using arriving seismic waves of increasing length and supplemented with additional auxiliary information. The effectiveness of DLcav to predict CAV was tested based on Japanese ground motion records, and the generalization ability of DLcav was assessed using the ground motion records from Chile. The results demonstrate that DLcav can rapidly predict CAV with good accuracy, which will help better estimate earthquake damage in EEW systems.

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