Effective response strategies to earthquake disasters are crucial for disaster management in
smart cities. However, in regions where earthquakes do not occur frequently, model …

Earthquake event detection in seismic time series data is an important and challenging
problem. The current state-of-the-art machine-learning based detection methods mostly …

Earthquake waveforms are necessary for simulation, verification, and validation during the
development of structural and earthquake engineering. However, due to the fact that the …

Earthquake warning systems are adopted as the last resort for providing automated actions
preventing secondary hazards due to earthquakes. However, existing methodologies do not …

Deep learning (DL) methodologies have been recently employed to solve various civil and
earthquake engineering problems. Nevertheless, due to the limited number of reliable data …

Within seismology, geology, civil and structural engineering, deep learning (DL), especially
via generative adversarial networks (GANs), represents an innovative, engaging, and …

This study proposes a probabilistic model for earthquake ground motion prediction, named
ground motion generation model, which can generate ground motion time history data …

Earthquake prediction is considered a vital endeavour for human safety. Effective
earthquake prediction can drastically reduce human damage, which is of utmost importance …

In the present study, we present an intelligent earthquake signal detector that provides
added assistance to automate traditional disaster responses. To effectively respond in a …

In this paper, an integrated framework based on conditional adversarial neural network
(cGAN) is established to simulate ground motions for earthquake scenarios with different …