Generative adversarial networks review in earthquake-related engineering fields

Abstract

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 augmentation tool. Indeed, in many practical applications, obtaining a significant number of high-quality information is demanding. Data augmentation is generally based on artificial intelligence (AI) and machine learning data-driven models. The DL GAN-based data augmentation approach for generating synthetic seismic signals revolutionized the current data augmentation paradigm. This study delivers a critical state-of-art review, explaining recent research into AI-based GAN synthetic generation of ground motion signals or seismic events, and also with a comprehensive insight into seismic-related geophysical studies. This study may be relevant, especially for the earth and planetary science, geology and seismology, oil and gas exploration, and on the other hand for assessing the seismic response of buildings and infrastructures, seismic detection tasks, and general structural and civil engineering applications. Furthermore, highlighting the strengths and limitations of the current studies on adversarial learning applied to seismology may help to guide research efforts in the next future toward the most promising directions.

Recent advances in earthquake seismology using machine learning

“…Additionally, Generative Adversarial Networks (GANs; Goodfellow et al 2014) were attempted to generate training data (Li et al 2020b;Wang et al 2021) and perform feature extraction (Li et al 2018). Marano et al (2023) reviewed the use of GANs in seismology, including data augmentation.…”

Seismic assessment of bridges through structural health monitoring: a state-of-the-art review

“…For a general review about the generative adversarial networks in earthquake engineering at large, the interested reader can refer to (Marano et al. 2023 ).…”

“…The combination of generative adversarial networks and autoencoders toward data anomaly detection for automated SHM of bridges has been discussed by Mao et al (2021). For a general review about the generative adversarial networks in earthquake engineering at large, the interested reader can refer to (Marano et al 2023).…”

End‐to‐end generation of structural topology for complex architectural layouts with graph neural networks

“…The blossom of artificial intelligence (AI) technology (Goodfellow et al, 2016;LeCun et al, 2015) is transforming the research paradigm in the field of civil engineering (C. Wang et al, 2022). Researchers use AI models to discover patterns, extract experiences from large data (Amezquita-Sancheza et al, 2020;Málaga-Chuquitaype, 2022), and even synthesize high-quality engineering science data (Marano et al, 2023). Notably, Adeli and his coresearchers were the pioneers in applying neural networks to civil/structural engineering (Adeli & Yeh, 1989), marking a groundbreaking advancement.…”