A convolutional neural network-based architecture for health monitoring of joint damages in a steel plane frame structure under temperature variability-Reference-Cited by-同舟云学术

A convolutional neural network-based architecture for health monitoring of joint damages in a steel plane frame structure under temperature variability

Published:2023-10-04 Issue:2 Volume:25 Page:2077-2089
ISSN:1563-0854
Container-title:Asian Journal of Civil Engineering
language:en
Short-container-title:Asian J Civ Eng

Author:

Naresh Maloth,Kumar Vimal,Pal Joy

Publisher

Springer Science and Business Media LLC

Subject

Civil and Structural Engineering

Link

https://link.springer.com/content/pdf/10.1007/s42107-023-00895-9.pdf

Reference54 articles.

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2. Barkhordari, M. S., Barkhordari, M. M., Armaghani, D. J., Rashid, A. S. A., & Ulrikh, D. V. (2022). Hybrid Wavelet Scattering Network-Based Model for Failure Identification of Reinforced Concrete Members. Sustainability (Switzerland), 14(19). https://doi.org/10.3390/su141912041

3. Beheshti Aval, S. B., Ahmadian, V., Maldar, M., & Darvishan, E. (2020). Damage detection of structures using signal processing and artificial neural networks. Advances in Structural Engineering, 23(5), 884–897. https://doi.org/10.1177/1369433219886079

4. Chang, C. M., Lin, T. K., & Chang, C. W. (2018). Applications of neural network models for structural health monitoring based on derived modal properties. Measurement: Journal of the International Measurement Confederation, 129(March), 457–470. https://doi.org/10.1016/j.measurement.2018.07.051

5. Chen, M., & Xu, B. (2012). Bolted joint looseness damage detection using electromechanical impedance measurements by PZT sensors. Third International Conference on Smart Materials and Nanotechnology in Engineering, 8409,840925. https://doi.org/10.1117/12.923329

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