Features of jumping loads are essentially high-dimensional random variables but have been simplistically modeled owing to the lack of proper mathematical tools. Generative adversarial networks (GANs) in conjunction with deep learning technology are adopted herein for modeling the jumping loads. Conditional GANs (CGANs) combined with Wasserstein GANs (WGANs) with gradient penalty (WGANs-GP) are adopted in the impulse modeling, where a multi-layer perceptron and a convolutional neural network are employed for the discriminator and generator, respectively. As for the impulse amplitude sequence and interval sequence modeling, similar CGANs combined with WGANs-GP are adopted, where recurrent neural networks are employed for both the generator and discriminator. A large amount of measured individual jumping loads are utilized in training GANs to ensure the generated samples can simulate the real ones well. After training, the individual jumping loads are simulated by connecting the generated impulse samples, based on the generated impulse amplitude sequence samples and interval sequence samples. The simulated jumping loads can be used to assess the vibration performance of assembly structures, such as grandstands, concert halls, and gym floors. Moreover, the established GANs can be extended to the modeling of other stochastic dynamic excitations.

中文翻译：

---

基于生成对抗网络的个体跳跃载荷建模的随机方法

跳跃载荷的特征本质上是高维随机变量，但由于缺乏适当的数学工具而被简单地建模。本文采用生成对抗网络 (GAN) 与深度学习技术相结合来对跳跃负载进行建模。在脉冲建模中采用条件 GAN (CGAN) 与带梯度惩罚的 Wasserstein GAN (WGAN) (WGAN-GP) 相结合，其中判别器和生成器分别采用多层感知器和卷积神经网络。对于脉冲幅度序列和区间序列建模，采用了类似的 CGANs 与 WGANs-GP 相结合，其中生成器和判别器都使用了循环神经网络。在训练 GAN 时使用了大量测量的个体跳跃载荷，以确保生成的样本能够很好地模拟真实样本。训练后，基于生成的脉冲幅度序列样本和间隔序列样本，通过连接生成的脉冲样本来模拟各个跳跃载荷。模拟的跳跃载荷可用于评估组装结构的振动性能，例如看台、音乐厅和体育馆地板。此外，已建立的 GAN 可以扩展到其他随机动态激励的建模。模拟的跳跃载荷可用于评估组装结构的振动性能，例如看台、音乐厅和体育馆地板。此外，已建立的 GAN 可以扩展到其他随机动态激励的建模。模拟的跳跃载荷可用于评估组装结构的振动性能，例如看台、音乐厅和体育馆地板。此外，已建立的 GAN 可以扩展到其他随机动态激励的建模。