Abstract
In the field of structural health monitoring, damage detection has been commonly carried out based on the modal properties and the engineering features related to the model. However, modal properties real-world structures are often affected by numerous uncertain factors, and the extracted features are also subjected to various errors, which makes the pattern recognition for damage detection still challenging. In this article, a damage detection method using the convolutional neural network (CNN) is presented for automated operation using raw measurement data without complex procedure for feature extraction. A CNN is a kind of deep neural network which typically consists of convolution, pooling, and fully-connected layers. A numerical simulation study was carried out for damage detection in cable hangers of a tied-arch bridge using ambient wind vibration data. Fourier amplitude spectra (FAS) of acceleration responses at cable anchorage points on the bridge deck are arranged as a matrix, which is used as the input to the CNN. Numerical results show that the current CNN using FAS data can detect both damage’ locations and extent accurately. Robustness of the present CNN has been proven under various observational noise levels and wind speeds.
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References
Abdeljaber O, Avci O, Kiranyaz S, Gabbouj M, Inman DJ (2017) Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. J Sound Vib 388:154–170
Cha YJ, Choi W, Büyüköztürk O (2017) Deep learning-based crack damage detection using convolutional neural networks. Comput-Aided Civ Infrastruct Eng 32(5):361–378
Deng L, Yu D (2014) Deep learning: methods and applications. Found Trends® Signal Process 7(3–4):197–387
Duan Y, Tao J, Zhang H, Wang S, Yun C (2019) Real-time hybrid simulation based on vector form intrinsic finite element and field programmable gate array. Struct Control Health Monit 26(1):e2277
Kim H, Ahn E, Shin M, Sim S (2018) Crack and noncrack classification from concrete surface images using machine learning. Struct Health Monit Int J. https://doi.org/10.1177/1475921718768747
Liang Z-J, Liao S-B, Hu B-Z (2018) 3D convolutional neural networks for dynamic sign language recognition. Comput J 61(11):1724–1736
Lin Y, Nie Z, Ma H (2017) Structural damage detection with automatic feature-extraction through deep learning. Comput-Aided Civ Infrastruct Eng 32(12):1025–1046
Simiu E, Scanlan RH (1996) Wind effects on structures: fundamentals and application to design. Wiley, Hoboken, p 605
Ting EC, Shih C, Wang Y (2004) Fundamentals of a vector form intrinsic finite element: part I. Basic procedure and a plane frame element. J Mech 20(02):113–122
Acknowledgements
This research work was supported by the National Key R&D Program of China (2018YFE0125400, 2017YFC0806100), the National Natural Science Foundation of China (U1709216, 51478419, 51522811, 51478429, and 90915008), and the Fundamental Research Funds for the Central Universities (2015XZZX004-28).
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Chen, Q.Y., Yun, C.B., Duan, Y.F. (2021). Cable Damage Identification of Tied-Arch Bridge Using Convolutional Neural Network. In: Wang, C.M., Dao, V., Kitipornchai, S. (eds) EASEC16. Lecture Notes in Civil Engineering, vol 101. Springer, Singapore. https://doi.org/10.1007/978-981-15-8079-6_7
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DOI: https://doi.org/10.1007/978-981-15-8079-6_7
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