Deep convolutional generative adversarial networks for the generation of numerous artificial spectrum‐compatible earthquake accelerograms using a limited number of ground motion records-Reference-Cited by-同舟云学术

Deep convolutional generative adversarial networks for the generation of numerous artificial spectrum‐compatible earthquake accelerograms using a limited number of ground motion records

Published:2022-05-24 Issue:2 Volume:38 Page:225-240
ISSN:1093-9687
Container-title:Computer-Aided Civil and Infrastructure Engineering
language:en
Short-container-title:Computer aided Civil Eng

Author:

Matinfar Mehrshad¹,Khaji Naser²,Ahmadi Goodarz³

Affiliation:

1. Faculty of Civil and Environmental Engineering Tarbiat Modares University Tehran Iran

2. Civil and Environmental Engineering Program, Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Japan

3. Department of Mechanical and Aerospace Engineering Clarkson University Potsdam New York USA

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Graphics and Computer-Aided Design,Computer Science Applications,Civil and Structural Engineering,Building and Construction

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/mice.12852

Reference77 articles.

1. Generation of artificial time‐histories compatible with given response spectra – a review;Ahmadi G.;Solid Mechanics Archives,1979

2. A novel methodology for modal parameters identification of large smart structures using MUSIC, empirical wavelet transform, and Hilbert transform

3. Generation of Near-Field Artificial Ground Motions Compatible with Median-Predicted Spectra Using PSO-Based Neural Network and Wavelet Analysis

4. Assessment of ANFIS networks on wavelet packet levels in generating artificial accelerograms

Cited by 26 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Generative adversarial network to produce numerous artificial accelerograms with pseudo-spectral acceleration as conditional input;Computers and Geotechnics;2024-10

2. Gravity dam displacement monitoring using in situ strain and deep learning;Computer-Aided Civil and Infrastructure Engineering;2024-09

3. Output-only response migration method of single-layer reticulated shells based on generative adversarial network;Engineering Applications of Artificial Intelligence;2024-09

4. A Generalized Attention Mechanism to Enhance the Accuracy Performance of Neural Networks;International Journal of Neural Systems;2024-08-31

5. Semi-Supervised Semantic Image Segmentation by Deep Diffusion Models and Generative Adversarial Networks;International Journal of Neural Systems;2024-08-15