卷积神经网络
稳健性(进化)
计算机科学
人工智能
流离失所(心理学)
鉴定(生物学)
模式识别(心理学)
人工神经网络
振动
计算机视觉
声学
物理
心理学
生物化学
化学
植物
生物
心理治疗师
基因
作者
Lucas Resende,Rafaelle Piazzaroli Finotti,Fernando Policarpo Barbosa,Hernán Garrido,Alexandre Cury,Martín Domizio
标识
DOI:10.1177/14759217231193102
摘要
This work investigates the effectiveness of using convolutional neural networks (CNNs) and instantaneous displacement measurements for damage identification in beams. The study involves subjecting laboratory beams to eight distinct damage scenarios and capturing the vertical positions of 60 points along the beam length during free-vibration tests using a high-speed camera. The data obtained was subsequently used to train a CNN in a supervised manner to estimate the level of damage at each point. Results showed that the CNN models were able to correctly localize and quantify the damage levels when trained on data from all damage scenarios. The soundness of the proposed methodology was demonstrated in a robustness assessment, where all eight damage scenarios were correctly identified even when two of them were excluded from the training dataset.
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