![]() ![]() ![]() Heng L, Yunfeng Z (2020) Deep learning based crack damage identification technique for thin plate structures using guided lamb wave signals. ![]() Liu H, Zhang Y (2019) Deep learning-based brace damage identification for concentrically braced frame structures under seismic loadings. Sergio C-M, Philip K, Enrique LD, Viviana M (2019) Deep convolutional neural network-based structural damage localization and quantification using transmissibility data. Tang Z, Chen Z, Bao Y, Li H (2019) Convolutional neural network-based data anomaly identification method using multiple information for structural health monitoring. Khodabandehlou H, Pekcan G, Fadali MS (2019) Vibration-based structural condition assessment using convolution neural networks. Comput Aided Civ Infrastruct Eng 35:597–614 J Civ Struct Heal Monit 8:689–718Īzimi M, Pekcan G (2020) Structural health monitoring using extremely compressed data through deep learning. J Civ Struct Heal Monit 1:679–692ĭas S, Saha P (2018) Structural health monitoring techniques implemented on IASC–ASCE benchmark problem: a review. J Sound Vib 417:182–197ĭas S, Saha P (2020) Performance of hybrid decomposition algorithm under heavy noise condition for health monitoring of structure. 16th international conference on control, automation, robotics and vision (ICARCV), IEEE, Shenzhen, Chinaīagheri A, Ozbulut OE, Harris DK (2018) Structural system identification based on variational mode decomposition. Shen YJ, Wu Q, Huang DJ et al (2020) Fault detection method based on multi-scale convolutional neural network for wind turbine gearbox. Xu ZF, Li C, Yang Y (2020) Fault diagnosis of rolling bearing of wind turbines based on the variational mode decomposition and deep convolutional neural networks. Zhang YL, Xie XY, Li HQ et al (2022) Subway tunnel damage identification based on in-service train dynamic response, variational mode decomposition, convolutional neural networks and long short-term memory. Yang J, Yang F, Zhou Y et al (2021) A data-driven structural damage detection framework based on parallel convolutional neural network and bidirectional gated recurrent unit. Yang J, Zhang L, Chen C et al (2020) A hierarchical deep convolutional neural network and gated recurrent unit framework for structural damage detection. įu L, Tang Q, Gao P et al (2021) Damage identification of long-span bridges using the hybrid of convolutional neural network and long short-term memory network. IOP Conf Ser Earth Environ Sci 626:012017. Zou JZ, Yang JX, Wang GP et al (2021) Bridge structural damage identification based on parallel CNN-GRU. Sony S, Gamage S, Sadhu A et al (2022) Vibration-based multiclass damage identification and localization using long short-term memory networks. Liu T, Xu H, Ragulskis M et al (2020) A data-driven damage identification framework based on transmissibility function datasets and one-dimensional convolutional neural networks: verification on a structural health monitoring benchmark structure. Comput Aided Civ Infrastruct Eng 34(9):822–839 Zhang Y, Miyamori Y, Mikami S et al (2019) Vibration-based structural state identification by a 1-dimensional convolutional neural network. Mech Syst Signal Process 147:107077Īzimi M, Eslamlou AD, Pekcan G (2020) Data-driven structural health monitoring and damage detection through deep learning: state-of-the-art review. The effectiveness of the proposed method was verified by two model experimental data.Īvci O, Abdeljaber O, Kiranyaz S et al (2021) A review of vibration-based damage identification in civil structures: from traditional methods to machine learning and deep learning applications. This method can not only eliminate the influence of noise and components irrelevant to damage in original signals but also allow for the CNN model to extract more feature information from a spectrogram compared to conventional methods. Finally, the spectrograms were inputted to a CNN model for structural damage identification. Subsequently, the effective IMF components were processed by HT to obtain spectrograms containing the feature information of the signals. First, VMD was applied to decompose an acceleration response signal into a series of intrinsic mode functions (IMFs) so as to select effective IMF components. To improve the identification accuracy of structural damage using vibration response signals and deep learning models, we developed a novel structural damage identification method based on variational mode decomposition (VMD)–Hilbert transform (HT) and a convolutional neural network (CNN). ![]()
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