Search

Offshore structures, such as oil and gas platforms and offshore wind turbines, are subjected to wind and wave loads simultaneously during their service lifetime. Since the wind and wave states are of significant randomness and dependence, the probabilistic modeling of joint wind and wave conditions plays an essential role in the safety design of offshore structures. Currently, three different methods can be adopted to establish the joint probabilistic model, which, however, are somewhat inconvenient in applications. The recently emerged generative adversarial networks has been demonstrated to be effective in dealing with high-dimensional random variables in several fields. In this study, the implicit joint probabilistic model of joint wind and wave load conditions is developed based on the Wasserstein generative adversarial network with gradient penalty. Long-term metocean reanalysis data of the site in the South China Sea is used to train and validate the model. After one million training steps, high-quality samples that are quite similar to the original data can be generated by the developed model. In addition, statistical comparisons of the generated samples obtained by the C-vine copula approach and the developed generative adversarial network model are performed as well, which demonstrates the effectiveness and superiority of the developed model. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Strong mainshocks are typically accompanied by numerous aftershocks, and the investigation of the structural failure mechanisms under the mainshock-aftershock sequence becomes particularly crucial. However, the number of recorded mainshock-aftershock sequences is limited. Therefore, the purpose of this article is to provide a reasonable method for directly generating the aftershock time histories from mainshock time histories. Using convolutional network as the basic network layer and conditional generative adversarial network as the structure, two models, one-dimensional convolution (1D-C-DCGAN) and two-dimensional convolution (2D-C-DCGAN) are established respectively by utilizing the deep convolutional generative adversarial network to learn the relationship between the mainshock-aftershock time histories. Then, they are trained with 972 pairs of the selected mainshock-aftershock time histories, and prediction results are discussed in comparison. The results show that the two models are proficient in generating AS acceleration time histories that are closely related to the sample trend, in which the 2D-C-DCGAN model performing better in overall waveform prediction, but with local spikes. In the comparison of intensity measures and response spectra, by examining coefficients such as R2, RMSE, MAPE, the two models outperformed the mainstream model (ASK14) on the dataset, and the 2D-C-DCGAN model is more accurate than the 1D-C-DCGAN model. The distributions of intensity measure predicted by 2D-C-DCGAN model are closer to the measured intensity measures, and its predicted response spectra are smoother and better matched to the measured response spectra. This advantage can be attributed to the effectiveness of convolution operations on two-dimensional data, allowing the convolutional capabilities to be fully utilized. [ABSTRACT FROM AUTHOR]

With the escalating development and utilization of subterranean spaces, the seismic hazards faced by underground structures are progressively increasing. However, owing to the challenges associated with acquiring underground seismic data and historical seismic design norms, pertinent regulations and research in this domain are scarce. This study focused on three crucial intensity measures in the seismic design process of underground structures peak ground acceleration (PGA), peak ground velocity (PGV), and peak ground displacement (PGD). The research leverages seismic data obtained from the California Strong Motion Instrumentation Program (CSMIP) to train and evaluate a conditional generative adversarial network (CGAN) model. This model was employed to establish a multivariate joint conditional probability distribution among the intensity measures at varying depths, facilitating the stochastic prediction of shallow intensity measures. In contrast to empirical formulas, the CGAN model eliminates the need for a predefined equation structure and enables the simultaneous prediction of multiple intensity measures. The performance of the model was evaluated by comparing the predictive accuracy of the CGAN model and empirical fitting formulas across diverse site conditions and depth intervals using metrics such as relative error coefficients. It can be concluded that the proposed CGAN model can accurately predict shallow seismic intensity measures, and the predictions conform to a specific conditional distribution while retaining the stochastic nature of seismic motion. Compared with empirical formula models, the CGAN model exhibited an enhanced predictive capability. • CGAN model extracts inter-depth seismic intensity measures distribution. • Predictions of the CGAN model are more accurate than empirical formula model. • CGAN model outperforms for larger seismic intensity measures. • CGAN model exhibits varied performance with site and depth differences. [ABSTRACT FROM AUTHOR]