Concrete Spalling Severity Classification Using Image Texture Analysis and a Novel Jellyfish Search Optimized Machine Learning Approach

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.

Review of Image-Processing-Based Technology for Structural Health Monitoring of Civil Infrastructures

“…Texture analysis involves a sophisticated exploration of spatial variations in pixel intensities across structural images where various authors have used it to detect damage including bridges and concrete structures [110][111][112][113][114][115][116]. This nuanced approach employs a repertoire of computational methods to delve into the intricate details of surface textures.…”

Computer Vision-Based Bridge Inspection and Monitoring: A Review

“…However, the method was unable to detect minor spalling. To solve this problem, Hoang et al [ 86 ] developed a method for detecting minor spalling of concrete surfaces based on image texture analysis and a novel jellyfish search optimizer. Nguyen et al [ 87 ] enhanced the prediction performance of the extreme gradient boosting machine (XGBoost) using a meta-heuristic Aquila optimizer metaheuristic, and employed XGBoost and a deep convolutional neural network (DCNN) to categorize concrete spalling into shallow spall and deep spall.…”

Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems

“…Hoang et al 94 implemented a support vector machine classifier that was optimized using JSO for the automatic classification of the severity of concrete spalling. It partitions input data into two classes, shallow spalling and deep spalling.…”

“…Optimizing CNN hyper-parameters JSO Chou et al 90 Predicting peak friction angle of fiber-reinforced soil (FRS) JSO-WFLSSVR Chou et al 93 Classification of concrete as shallow or deep spalling JSO Hoang et al 94 Clustering renewable energy based microgrid JSO Shubham et al 99 Predicting optimal switching angle in voltage control JSO Siddiqui et al 95 Predicting performance of STEACS RVFL-JSO Almodfer et al 89 Benchmark function optimization and data clustering LA-JSO Barshandeh et al 91 Classifying imbalanced and balanced datasets JSO Desuky et al 92 Integrated interval forecasting for solar radiation MOJS Wang and Gao 97 Classifying human brain functions JSO Zhao 98 Forecasting income of rural residents FOGJSO Lei et al 61…”