Generative adversarial networks review in earthquake-related engineering fields

• Published in: Bulletin of earthquake engineering Vol. 22; no. 7; pp. 3511 - 3562
• Main Authors: Marano, Giuseppe Carlo, Rosso, Marco Martino, Aloisio, Angelo, Cirrincione, Giansalvo
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.05.2024; Springer Nature B.V; Springer Verlag
• Subjects: Artificial intelligence; Civil Engineering; Computer Science; Data augmentation; Deep learning; Earth and Environmental Science; Earth Sciences; Earthquakes; Emerging Technologies; Environmental Engineering/Biotechnology; Generative adversarial networks; Geology; Geophysical studies; Geophysics/Geodesy; Geotechnical Engineering & Applied Earth Sciences; Ground motion; Hydrogeology; Machine learning; Natural gas exploration; Oil and gas exploration; Oil exploration; S and BDA in earthquake engineering; S.I. : AI; Seismic activity; Seismic response; Seismology; Signal generation; State of the art; Structural engineering; Structural Geology; Synthetic data; Deep learning; Generative adversarial networks; Earthquake engineering; Seismology; Seismic; Geophysics

Within seismology, geology, civil and structural engineering, deep learning (DL), especially via generative adversarial networks (GANs), represents an innovative, engaging, and advantageous way to generate reliable synthetic data that represent actual samples’ characteristics, providing a handy data...