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Ground-motion simulations using two-dimensional convolution condition adversarial neural network(2D-cGAN)
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AI
DOI：10.1016/j.soildyn.2023.108444.png)
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摘要
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In this paper, an integrated framework based on conditional adversarial neural network (cGAN) is established to simulate ground motions for earthquake scenarios with different magnitude, distance, and local site conditions. A ground motion dataset is created for training the network, within which most data are observed accelerograms collected from PEER and gaps for certain magnitude-site conditions are filled with accelerograms simulated using stochastic finite-fault modeling. During the learning of cGAN, the dataset is classified first with multi-labels according to local site conditions, and the multi-labels are set to be the conditional information Y for the network. Effects of using one-dimensional convolution (Conv1D) and two-dimensional convolution (Conv2D) in cGAN are tested in terms of the time-domain and frequency-domain characteristics of the generated ground motion. The test proves the strengths of Conv2D in capturing high-dimensional temporal and spectral characteristics from one-dimensional time series of ground motion. Ground motions generated using 2D-cGAN network are compared to the measures predicted by widely-used ground motion prediction equations, through which the reliability of 2D-cGAN network on ground motion simulation is verified.
Keyword:
Ground motion simulation
Site condition
Artificial intelligence (AI)
Two-dimensional convolution (Conv2D)
Conditional adversarial neural network (cGAN)
期刊
IF:
4.6
论文数:
7.7K
被引数:
2.5W
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