Structural Damage Identification Based on Convolutional Neural Network Group Considering the Sensor Fault
Author:
Publisher
Springer Science and Business Media LLC
Subject
Civil and Structural Engineering
Link
https://link.springer.com/content/pdf/10.1007/s12205-023-0683-y.pdf
Reference32 articles.
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2. Ananthi G, Msd B, Roy K, Lim J (2021) Influence of intermediate stiffeners on the axial capacity of cold-formed steel back-to-back built-up unequal angle sections–sciencedirect. Structures 32:827–848, DOI: https://doi.org/10.1016/j.istruc.2021.03.059
3. Ananthi G, Roy K, Lim J (2022) Behaviour and strength of back-to-back built-up cold-formed steel unequal angle sections with intermediate stiffeners under axial compression. Steel & Composite Structures: An International Journal 42(1):1–22, DOI: https://doi.org/10.12989/scs.2022.42.1.001
4. Azimi M, Eslamlou AD, Pekcan G (2020) Data-driven structural health monitoring and damage detection through deep learning: State-of-the-art review. Sensors 20(10):2778, DOI: https://doi.org/10.3390/s20102778
5. Boudiaf M, Rony J, Ziko M, Granger Eric, Pedersoli M, Piantanida P, Ayed BI (2020) A unifying mutual information view of metric learning: Cross-entropy vs. pairwise losses, Computer Vision-ECCV, 2020(12351):548–564, DOI: https://doi.org/10.1007/978-3-030-58539-6_33
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