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Damage Evaluation of Building Surface via Novel Deep Learning Framework
1 School of Civil Engineering & Mechanics, Yanshan University, Qinhuangdao, 066004, China
* Corresponding Author: Shan Xu. Email:
The International Conference on Computational & Experimental Engineering and Sciences 2023, 27(4), 1-3. https://doi.org/10.32604/icces.2023.09930
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Damage evaluation is an important index for the evaluation of buildings health. To provide a rapid crack evaluation in practical applications, a crack identification and damage evaluation via deep learning framework is proposed in this paper. We built a combined dataset from Kaggle and site photos. A pre-trained U-net model is used to perform the training of model. With updated weights, the identification of cracks could be performed on non-labelled photos.Keywords
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APA Style
Xu, S., Tang, H., Wang, D., Zhu, R., Wang, L. et al. (2023). Damage evaluation of building surface via novel deep learning framework. The International Conference on Computational & Experimental Engineering and Sciences, 27(4), 1-3. https://doi.org/10.32604/icces.2023.09930
Vancouver Style
Xu S, Tang H, Wang D, Zhu R, Wang L, Hao S. Damage evaluation of building surface via novel deep learning framework. Int Conf Comput Exp Eng Sciences . 2023;27(4):1-3 https://doi.org/10.32604/icces.2023.09930
IEEE Style
S. Xu, H. Tang, D. Wang, R. Zhu, L. Wang, and S. Hao, “Damage Evaluation of Building Surface via Novel Deep Learning Framework,” Int. Conf. Comput. Exp. Eng. Sciences , vol. 27, no. 4, pp. 1-3, 2023. https://doi.org/10.32604/icces.2023.09930
Copyright © 2023 The Author(s). Published by Tech Science Press.This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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