摘要
目 的
1. 使用基于振动的损伤检测方法进行结构健康监测. 2. 基于材料力学性能评价提出一种新的结构健康监测方法.
创新点
1. 通过一个被称为损失函数 (LF) 的新指标描述材料粘弹性参数与振动参数之间的相关性. 2. 使用卷积神经网络 (CNN) 提取自动特征和损坏敏感性, 以评估结构状况.
方 法
1. 测量真实桥梁的振动响应. 2. 在频域中进行信号处理以揭示振动能量损失. 3. 基于深度学习和 CNN 对桥梁状况进行分类.
结 论
1. 在真实结构中总是会发生能量扩散. 2. 基于振动能量损失变化的 LF 评估, 可以对桥梁进行健康监测. 3. 基于深度学习的能量扩散评估是可实现的, 并且在多个实际桥梁中具有较高的可实施性.
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References
Anitescu C, Atroshchenko E, Alajlan N, et al., 2019. Artificial neural network methods for the solution of second order boundary value problems. Computers, Materials & Continua, 59(1):345–359. https://doi.org/10.32604/cmc.2019.06641
Gao YQ, Mosalam KM, 2018. Deep transfer learning for image-based structural damage recognition. Computer-aided Civil and Infrastructure Engineering, 33(9):748–768. https://doi.org/10.1111/mice.12363
Guo HW, Zhuang XY, Rabczuk T, 2019. A deep collocation method for the bending analysis of Kirchhoff plate. Computers, Materials & Continua, 59(2):433–456. https://doi.org/10.32604/cmc.2019.06660
Nguyen TD, Nguyen HQ, Pham TB, et al., 2021. A novel proposal in using viscoelastic model for bridge condition assessment. In: Bui TQ, Cuong LT, Khatir S (Eds.), Structural Health Monitoring and Engineering Structures. Springer, Singapore. https://doi.org/10.1007/978-981-16-0945-9_27
Samaniego E, Anitescu C, Goswami S, et al., 2020. An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications. Computer Methods in Applied Mechanics and Engineering, 362:112790. https://doi.org/10.1016/j.cma.2019.112790
Wang ZL, Cha YJ, 2021. Unsupervised deep learning approach using a deep auto-encoder with a one-class support vector machine to detect damage. Structural Health Monitoring, 20(1):406–425. https://doi.org/10.1177/1475921720934051
Yu YJ, Cao H, Yan XY, et al., 2020. Defect identification of wind turbine blades based on defect semantic features with transfer feature extractor. Neurocomputing, 376:1–9. https://doi.org/10.1016/j.neucom.2019.09.071
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Nhi NGO-KIEU designed the research. Thao NGUYEN-DA, Toan PHAM-BAO, and Tam NGUYEN-NHAT processed the data. Toan PHAM-BAO wrote the first draft of the manuscript. Nhi NGO-KIEU helped to organize the manuscript. Hung NGUYEN-XUAN revised and edited the final version.
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Nhi NGO-KIEU, Thao NGUYEN-DA, Toan PHAM-BAO, Tam NGUYEN-NHAT, and Hung NGUYEN-XUAN declare that they have no conflict of interest.
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Ngo-Kieu, N., Nguyen-Da, T., Pham-Bao, T. et al. Deep learning-based signal processing for evaluating energy dispersal in bridge structures. J. Zhejiang Univ. Sci. A 22, 672–680 (2021). https://doi.org/10.1631/jzus.A2000414
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DOI: https://doi.org/10.1631/jzus.A2000414