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Damage Evaluation of Building Surface via Novel Deep Learning Framework

Shan Xu1,*, Huadu Tang1, Ding Wang1, Ruiguang Zhu1, Liwei Wang1, Shengwang Hao1

1 School of Civil Engineering & Mechanics, Yanshan University, Qinhuangdao, 066004, China

* Corresponding Author: Shan Xu. Email: email

The International Conference on Computational & Experimental Engineering and Sciences 2023, 27(4), 1-3. https://doi.org/10.32604/icces.2023.09930

Abstract

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.

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Cite This Article

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.



cc 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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