Damage identification using convolutional neural networks from instantaneous displacement measurements via image processing

IF 5.7 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Lucas H. G. Resende, R. Finotti, F. Barbosa, Hernán Garrido, A. Cury, Martín Domizio
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引用次数: 0

Abstract

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.
通过图像处理,利用卷积神经网络从瞬时位移测量中识别损伤
这项工作研究了使用卷积神经网络(cnn)和瞬时位移测量在梁损伤识别中的有效性。这项研究包括对实验室横梁进行8种不同的损伤情况,并在自由振动测试中使用高速摄像机捕捉沿横梁长度的60个点的垂直位置。获得的数据随后用于以监督的方式训练CNN,以估计每个点的损伤程度。结果表明,CNN模型在对所有损伤场景的数据进行训练后,能够正确地定位和量化损伤水平。在稳健性评估中证明了所提出方法的合理性,即使其中两个从训练数据集中排除,也可以正确识别所有八个损坏场景。
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来源期刊
CiteScore
12.80
自引率
12.10%
发文量
181
审稿时长
4.8 months
期刊介绍: Structural Health Monitoring is an international peer reviewed journal that publishes the highest quality original research that contain theoretical, analytical, and experimental investigations that advance the body of knowledge and its application in the discipline of structural health monitoring.
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