1 Department of Construction Technologies, Ardahan University, Ardahan, Turkey.
2 Department of Computer Engineering, Erzurum Technical University, Erzurum, Turkey.
Research Article
World Journal of Advanced Research and Reviews, 2023, 18(03), 1367-1379
Received on 15 May 2023; revised on 28 June 2023; accepted on 30 June 2023
Nowadays, one of the most commonly used construction materials is concrete. As a building material, concrete can be deformed under various conditions and cracks can form on this material. Depending on their condition and position, these cracks can pose serious hazards. Therefore, the automatic detection and classification of these cracks becomes a very important issue. The detection process, which is usually performed by manual observation, is labor intensive. In this research, a new machine learning method is proposed for automatic detection of cracks in concrete surface. The proposed method utilizes DenseNet201 based deep feature extraction approach. In addition, the model includes ReliefF-based feature selection and SVM-based classification phases. SDNET2018, an open access dataset, is used to test the proposed model. Both holdout cross validation and 10-fold cross validation techniques were applied for validation on this dataset. As a result of the test procedures, 93% classification success was achieved for 10-fold CV. The results obtained with the test procedures prove the success of the proposed method in automatic crack classification.
DenseNet201; Relief; Machine Learning; Concrete Crack; Classification
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Suat Gokhan Ozkaya and Mehmet Baygin. Advanced machine learning approach for automatic crack detection and classification in concrete surfaces. World Journal of Advanced Research and Reviews, 2023, 18(3), 1367-1379 . Article DOI: https://doi.org/10.30574/wjarr.2023.18.3.1282