Deep learning (DL) methodologies have been recently employed to solve various civil and earthquake engineering problems. Nevertheless, due to the limited number of reliable data in the field of earthquake engineering, it is not convenient to obtain accurate results using DL. To tackle this challenge, the generative adversarial network (GAN) approach may be considered a reliable possible candidate. GANs have been introduced as an efficient way to train generative models. GANs exhibited their capabilities as well as versatility in the field of image production. For nonlinear dynamic analyses of structures, artificial ground accelerograms that are compatible with a target response spectrum are usually generated. In this paper, an efficient algorithm is proposed by which numerous artificial spectrum-compatible earthquake accelerograms are generated using a few ground motion records. For this purpose, a specific well-established generative model, namely, the deep convolutional GAN (DCGAN), is adopted for the first time and used. It is shown that DCGAN can easily generate desirable artificial ground accelerograms by having a limited number of seismic records as input to train the network. To quantitatively demonstrate the quality of the artificial ground accelerograms generated by the DCGAN, several computer experiments are presented, among which the robustness and feasibility of the proposed method are examined by using only four earthquake accelerograms as the worst scenario. Moreover, the efficiency of the DCGAN is illustrated by comparing various seismic parameters and the spectral response of the generated accelerograms with those of the actual accelerograms. The outcomes illustrate the efficiency and robustness of the presented DCGAN.

中文翻译：

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深度卷积生成对抗网络，用于使用有限数量的地面运动记录生成大量与人工频谱兼容的地震加速度图

深度学习 (DL) 方法最近已被用于解决各种土木和地震工程问题。然而，由于地震工程领域可靠数据的数量有限，使用 DL 获取准确结果并不方便。为了应对这一挑战，生成对抗网络 (GAN) 方法可能被认为是一种可靠的候选方法。GAN 已被引入作为训练生成模型的有效方法。GAN 展示了它们在图像制作领域的能力和多功能性。对于结构的非线性动力学分析，通常会生成与目标响应谱兼容的人造地面加速度图。在本文中，提出了一种有效的算法，通过该算法可以使用少量地面运动记录生成大量与人工频谱兼容的地震加速度图。为此，首次采用并使用了一种特定的成熟生成模型，即深度卷积 GAN (DCGAN)。结果表明，通过将有限数量的地震记录作为输入来训练网络，DCGAN 可以轻松生成理想的人造地面加速度图。为了定量证明 DCGAN 生成的人工地面加速度图的质量，提出了几个计算机实验，其中仅使用四个地震加速度图作为最坏情况来检查所提出方法的稳健性和可行性。而且，通过比较各种地震参数和生成的加速度图的光谱响应与实际加速度图的光谱响应，说明了 DCGAN 的效率。结果说明了所提出的 DCGAN 的效率和鲁棒性。