Abstract
During the phase of building survey, spalling and its severity should be detected as earlier as possible to provide timely information on structural heath to building maintenance agency. Correct detection of spall severity can significantly help decision makers develop effective maintenance schedule and prioritize their financial resources better. This study aims at developing a computer vision-based method for automatic classification of concrete spalling severity. Based on input image of concrete surface, the method is capable of distinguishing between a minor spalling in which the depth of the broken-off material is less than the concrete cover layer and a deep spalling in which the reinforcing steel bars have been revealed. To characterize concrete surface condition, image texture descriptors of statistical measurement of color channels, gray-level run length, and center-symmetric local binary pattern are used. Based on these texture-based features, the support vector machine classifier optimized by the jellyfish search metaheuristic is put forward to construct a decision boundary that partitions the input data into two classes of shallow spalling and deep spalling. A dataset consisting of 300 image samples has been collected to train and verify the proposed computer vision method. Experimental results supported by the Wilcoxon signed-rank test point out that the newly developed method is highly suitable for concrete spall severity classification with accuracy rate = 93.33%, F1 score = 0.93, and area under the receiver operating characteristic curve = 0.97.
Highlights
Spalling is a notable defect widely encountered in surface of reinforced concrete structures. e appearance of spalling significantly deteriorates the integrity and durability of reinforced concrete elements. is defect can be caused by severe servicing environment and loads
The Computer Vision-Based Jellyfish Search Optimized Support Vector Classification (JSO-Support Vector Machine Classification (SVC)) for Concrete Spalling Severity Classification is section of the article aims at describing the overall structure of the proposed computer vision-based approach used for automatic classification of concrete spalling severity. e overall structure of the newly developed approach consists of three modules: (i) image texture computation, (ii) JS-based model optimization, and (iii) SVC-based spalling severity categorization based on input image samples
To reliably evaluate the predictive performance of the proposed JSO-SVC, this study has repeated the model training and testing processes with 20 independent runs. e statistical measurements obtained from these 20 independent runs are employed to quantify the model predictive capability in the task of concrete spalling severity recognition. is repeated model evaluation aims at reducing the variation caused by the randomness in the data separation process
Summary
Received 17 May 2021; Revised 13 September 2021; Accepted 7 November 2021; Published 10 December 2021. Is study aims at developing a computer vision-based method for automatic classification of concrete spalling severity. To characterize concrete surface condition, image texture descriptors of statistical measurement of color channels, gray-level run length, and center-symmetric local binary pattern are used. Based on these texture-based features, the support vector machine classifier optimized by the jellyfish search metaheuristic is put forward to construct a decision boundary that partitions the input data into two classes of shallow spalling and deep spalling. Experimental results supported by the Wilcoxon signed-rank test point out that the newly developed method is highly suitable for concrete spall severity classification with accuracy rate 93.33%, F1 score 0.93, and area under the receiver operating characteristic curve 0.97
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