There has been a major advance in the field of Data Science in the last few decades, and these have been utilized for different engineering disciplines and applications. Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) algorithms have been utilized for civil Structural Health Monitoring (SHM) especially for damage detection applications using sensor data. Although ML and DL methods show superior learning skills for complex data structures, they require plenty of data for training. However, in SHM, data collection from civil structures can be expensive and time taking; particularly getting useful data (damage associated data) can be challenging. The objective of this study is to address the data scarcity problem for damage detection applications. This paper employs 1-D Wasserstein Deep Convolutional Generative Adversarial Networks using Gradient Penalty (1-D WDCGAN-GP) for synthetic labelled acceleration data generation. Then, the generated data is augmented with varying ratios for the training dataset of a 1-D Deep Convolutional Neural Network (1-D DCNN) for damage detection application. The damage detection results show that the 1-D WDCGAN-GP can be successfully utilized to tackle data scarcity in vibration-based damage detection applications of civil structures. Keywords: Structural Health Monitoring (SHM), Structural Damage Detection, 1-D Deep Convolutional Neural Networks (1-D DCNN), 1-D Generative Adversarial Networks (1-D GAN), Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP)
翻译:在过去几十年中,数据科学领域取得了重大进步,这些数据用于不同的工程学科和应用,人工智能(AI)、机器学习(ML)和深层学习(DL)算法被用于民用结构健康监测(SHM),特别是用于使用传感器数据进行损坏检测应用。虽然ML和DL方法显示了复杂数据结构的高级学习技能,但需要大量培训数据。然而,在SHM,从民用结构收集数据可能花费很多时间;特别是获得有用的数据(与损坏相关的数据)可能具有挑战性。本研究的目的是解决损坏检测应用中的数据稀缺问题。本文使用1-D瓦瑟斯坦深层革命感知反差网络(1-DWDCGAN-GP),用于合成加速数据生成(1-DDCGAN-GM)方法。随后,生成的数据随着1-DGAR-Developal Neal-National-National-National-National-National-National-National-National-National-National-National-National-National-National-National-National-National-National-GPal-GPal-DRal-DRal-DRal-DRal-DRisal-DRMS)网络的训练、1SDRIS-SMDRMS-S-S-S-S-S-SMS-S-S-S-S-S-S-S-SDRisal-S-SDRDRM-S-S-SDRM-S-SDRMD-S-SD-SDRMDRD-S-S-SD-SD-SDRM-S-S-S-S-S-SD-S-S-S-S-S-SDRMDRMDRMDRMDRMDRMDRMDRMDRMDMDMDMDMDMDMDM-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S