期刊
AUTOMATION IN CONSTRUCTION
卷 140, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.autcon.2022.104371
关键词
Concrete spall severity; Gradient boosting machine; Local binary pattern; Metaheuristic; Deep learning
资金
- Vietnam National Foundation for Science and Technology Development (NAFOSTED) [101.99-2019.326]
This paper presents alternative solutions for classifying concrete spall severity based on computer vision approaches. XGBoost optimized by the Aquila metaheuristic and used with ARCS-LBP achieved an outstanding classification performance with a classification accuracy rate of roughly 99% for real-world concrete surface images.
This paper presents alternative solutions for classifying concrete spall severity based on computer vision approaches. Extreme Gradient Boosting Machine (XGBoost) and Deep Convolutional Neural Network (DCNN) are employed for categorizing image samples into two classes: shallow spall and deep spall. To delineate the properties of a concrete surface subject to spall, texture descriptors including local binary pattern, center symmetric local binary pattern, local ternary pattern, and attractive repulsive center symmetric local binary pattern (ARCS-LBP) are employed as feature extraction methods. In addition, the prediction performance of XGBoost is enhanced by Aquila optimizer metaheuristic. Meanwhile, DCNN is capable of performing image classification directly without the need for texture descriptors. Experimental results with a dataset containing real-world concrete surface images and 20 independent model evaluations point out that the XGBoost optimized by the Aquila metaheuristic and used with ARCS-LBP has achieved an outstanding classification performance with a classification accuracy rate of roughly 99%.
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