论文标题
使用条件生成对抗网络生成参数化的结构数据
On generating parametrised structural data using conditional generative adversarial networks
论文作者
论文摘要
结构健康监测（SHM）中最常见的方法之一是使用数据驱动的模型来对结构及其状况进行预测和推断。这样的方法几乎完全依赖于数据的质量。在SHM纪律中，数据并不总是足以在给定任务中以令人满意的精度构建模型。更糟糕的是，关于不同环境条件下结构的行为，数据集可能完全缺少数据。在当前的工作中，为了面对此类问题，使用了使用生成对抗网络（GAN）算法的变体的人工数据生成。上述变化是条件gan或cgan的变化。该算法不仅用于生成人造数据，而且还根据一些已知参数学习了歧管的转换。假设结构的响应由多种歧视中的点表示，则该空间的一部分将是由于影响结构的外部条件的变化而形成的。这个想法在SHM中证明是有效的，因为它被利用以生成环境系数特定值的结构数据。该方案在此处应用于模拟结构，该结构在不同的温度和湿度条件下运行。对CGAN在某些范围内的某些温度的某些离散值进行了培训，并能够以令人满意的精度为该范围内的每个温度生成数据。与类似问题中的经典回归相比，新颖性是，CGAN允许未知的环境参数影响结构，并可以为已知参数的每个值生成整个数据歧管，而未知参数的每个值则在生成的歧管中变化。
A powerful approach, and one of the most common ones in structural health monitoring (SHM), is to use data-driven models to make predictions and inferences about structures and their condition. Such methods almost exclusively rely on the quality of the data. Within the SHM discipline, data do not always suffice to build models with satisfactory accuracy for given tasks. Even worse, data may be completely missing from one's dataset, regarding the behaviour of a structure under different environmental conditions. In the current work, with a view to confronting such issues, the generation of artificial data using a variation of the generative adversarial network (GAN) algorithm, is used. The aforementioned variation is that of the conditional GAN or cGAN. The algorithm is not only used to generate artificial data, but also to learn transformations of manifolds according to some known parameters. Assuming that the structure's response is represented by points in a manifold, part of the space will be formed due to variations in external conditions affecting the structure. This idea proves efficient in SHM, as it is exploited to generate structural data for specific values of environmental coefficients. The scheme is applied here on a simulated structure which operates under different temperature and humidity conditions. The cGAN is trained on data for some discrete values of the temperature within some range, and is able to generate data for every temperature in this range with satisfactory accuracy. The novelty, compared to classic regression in similar problems, is that the cGAN allows unknown environmental parameters to affect the structure and can generate whole manifolds of data for every value of the known parameters, while the unknown ones vary within the generated manifolds.
