预言
结构健康监测
深度学习
领域(数学分析)
翻译(生物学)
聚类分析
计算机科学
机器学习
结构工程
状态监测
加速度
生成语法
工程类
人工智能
模式识别(心理学)
数据挖掘
数学
物理
数学分析
电气工程
信使核糖核酸
基因
化学
经典力学
生物化学
作者
Furkan Luleci,F. Necati Çatbaş,Onur Avcı
标识
DOI:10.1016/j.ymssp.2023.110370
摘要
The advances in data science in the last few decades have benefitted many other fields, including Structural Health Monitoring (SHM). Artificial Intelligence (AI), such as Machine Learning (ML) methods for vibration-based damage diagnostics of civil structures, have been utilized extensively due to the observed high performances in learning from complex data structures. AI-based data-driven techniques used for damage diagnostics and prognostics applications are centered on historical data of the structures and require a substantial amount of data for data-driven prediction models. Although some of these methods are generative-based models, they are used to perform typical ML tasks such as classification, regression, or clustering after learning the data domain. In this study, a variant of Generative Adversarial Networks (GAN), a generative model, Cycle-Consistent Wasserstein Deep Convolutional GAN with Gradient Penalty (CycleWDCGAN-GP) is developed to investigate the domain translation between undamaged and damaged acceleration data (1-D) from one element to the same element as well as to other elements. The outcomes of this study demonstrate that the proposed methodology could be used to generate possible responses of a structure for potentially damaged conditions. In other words, with the proposed methodology, it will be possible to understand and generate the damaged condition while the structure is still healthy.
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