Journal of Seismology and Earthquake Engineering

Journal of Seismology and Earthquake Engineering

Generating Design Spectrum-Compatible Artificial Accelerograms Utilizing Generative Adversarial Networks

Document Type : Research Article

Authors
Mehrshad Matinfar 1
Naser Khaji 2
1 M.Sc. in Structural Engineering, Tarbiat Modares University, Tehran, Iran
2 Professor of Civil Engineering, Hiroshima University, Hiroshima, Japan
10.48303/jsee.2025.2034252.1109
Abstract
Recent advancements in Deep Learning (DL) have significantly expanded its application to address myriad challenges in civil and earthquake engineering. However, a notable challenge persists: the scarcity of reliable data pertinent to earthquake engineering, which may compromise the accuracy of DL-derived results. In response to this challenge, Generative Adversarial Networks (GANs) have emerged as a promising solution. Initially conceptualized to improve the training of generative models, GANs have exhibited exceptional performance and adaptability, particularly in image generation, gaining substantial recognition within the academic community. In structural engineering, the generation of synthetic ground accelerograms that conform to a specified target response spectrum is essential for conducting nonlinear dynamic analyses. This paper introduces an effective algorithm for spectral matching, facilitating the generation of numerous artificial, spectrum-compatible earthquake accelerograms from a limited set of ground motion records. The proposed algorithm represents a significant advancement in the field, addressing the critical need for robust and accurate synthetic data in earthquake engineering. Consequently, the integration of GANs into this domain not only enhances the reliability of DL applications but also paves the way for more precise and comprehensive analyses, thereby contributing to the overall progress of civil and earthquake engineering disciplines.
Keywords
Artificial accelerograms
GAN
AI
Neural Networks
Deep learning
Subjects
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Journal of Seismology and Earthquake Engineering
Volume 27, Issue 2
Spring 2025
Pages 15-24

  • Receive Date 30 June 2024
  • Revise Date 09 April 2025
  • Accept Date 09 June 2025
  • Publish Date 01 April 2025