Conditional generative adversarial networks for spectrum-compatible ground motion generation of scenario earthquakes

Zhenning Ba; Yang Ma; Jingxuan Zhao; Fangbo Wang

doi:10.1016/j.engappai.2025.110755

ScienceGate Book Chapters

JOURNAL ARTICLE

Conditional generative adversarial networks for spectrum-compatible ground motion generation of scenario earthquakes

Zhenning Ba Yang Ma Jingxuan Zhao Fangbo Wang

Year: 2025 Journal: Engineering Applications of Artificial Intelligence Vol: 152 Pages: 110755-110755 Publisher: Elsevier BV

DOI: 10.1016/j.engappai.2025.110755

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Adversarial system Generative grammar Generative adversarial network Motion (physics) Ground motion Spectrum (functional analysis) Artificial intelligence Deep learning Seismology Geology

Metrics

Cited By

14.76

FWCI (Field Weighted Citation Impact)

Refs

0.97

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Seismic Imaging and Inversion Techniques

Physical Sciences → Earth and Planetary Sciences → Geophysics

Seismology and Earthquake Studies

Physical Sciences → Computer Science → Artificial Intelligence

Structural Health Monitoring Techniques

Physical Sciences → Engineering → Civil and Structural Engineering

Conditional generative adversarial networks for spectrum-compatible ground motion generation of scenario earthquakes

Metrics

Citation History

Topics

Related Documents

Renewable Scenario Generation Based on Improved Conditional Generative Adversarial Networks

Conditional Style-Based Generative Adversarial Networks for Renewable Scenario Generation

Conditional generative adversarial networks for the generation of strong ground motion parameters using KiK-net ground motion records

Spectrum-compatible artificial accelerograms via conditional variational autoencoder with generative adversarial networks

Icon Generation with Conditional Generative Adversarial Networks