There has been a drastic progression in the field of Data Science in the last few decades and other disciplines have been continuously benefitting from it. Structural Health Monitoring (SHM) is one of those fields that use Artificial Intelligence (AI) such as Machine Learning (ML) and Deep Learning (DL) algorithms for condition assessment of civil structures based on the collected data. The ML and DL methods require plenty of data for training procedures; however, in SHM, data collection from civil structures is very exhaustive; particularly getting useful data (damage associated data) can be very challenging. This paper uses 1-D Wasserstein Deep Convolutional Generative Adversarial Networks using Gradient Penalty (1-D WDCGAN-GP) for synthetic labeled vibration data generation. Then, implements structural damage detection on different levels of synthetically enhanced vibration datasets by using 1-D Deep Convolutional Neural Network (1-D DCNN). The damage detection results show that the 1-D WDCGAN-GP can be successfully utilized to tackle data scarcity in vibration-based damage diagnostics of civil structures. Keywords: Structural Health Monitoring (SHM), Structural Damage Diagnostics, Structural Damage Detection, 1-D Deep Convolutional Neural Networks (1-D DCNN), 1-D Generative Adversarial Networks (1-D GAN), Deep Convolutional Generative Adversarial Networks (DCGAN), Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP)
翻译:在过去几十年中,数据科学领域出现了急剧的进展,其他学科也不断从中受益。结构健康监测(SHM)是利用人工智能(AI),如机器学习(ML)和深学习(DL)算法,根据收集的数据对民用结构进行条件评估。ML和DL方法需要大量数据用于培训程序;然而,在SHM,从民用结构收集数据非常详尽;特别是获得有用的数据(与损害相关的数据)可能非常具有挑战性。本文使用1-D 瓦瑟斯坦深相相交深相相交深相交深相调频网络(1-D WDCGAN-GP),用于合成标志性振动数据生成。随后,通过使用1D 深相电动神经网络(1-DDCNNNNN),结构损害检测结果显示, 1D WDCGAN-GGP(1-GGGG) 以震动诊断民用结构结构结构的破坏数据稀缺程度。 关键词:结构健康监测(SDAR-DRADRA)、结构破坏网络(1S-DRDRVADS)、结构破坏1(DRisalation Studalation Sudy)