Abstract
Pitting corrosion can lead to critical failures of infrastructure elements. Therefore, accurate detection of corroded areas is crucial during the phase of structural health monitoring. This study aims at developing a computer vision and data-driven method for automatic detection of pitting corrosion. The proposed method is an integration of the history-based adaptive differential evolution with linear population size reduction (LSHADE), image processing techniques, and the support vector machine (SVM). The implementation of the LSHADE metaheuristic in this research is multifold. This optimization algorithm is employed in the task of multilevel image thresholding to extract regions of interest from the metal surface. Image texture analysis methods of statistical measurements of color channels, gray-level co-occurrence matrix, and local binary pattern are used to compute numerical features subsequently employed by the SVM-based pattern recognition phase. In addition, the LSHADE metaheuristic is also used to optimize the hyperparameters of the machine-learning approach. Experimental results supported by statistical test points out that the newly developed approach can attain a good predictive result with classification accurate rate = 91.80%, precision = 0.91, recall = 0.94, negative predictive value = 0.93, and F1 score = 0.92. Thus, the newly developed method can be a promising tool to be used in a periodic structural health survey.
Highlights
Ageing infrastructure elements with constraints on maintenance budgets are the main concern of infrastructure management agencies around the world. ese facts urge the implementation of smart and cost-effective methods in the field of structure health monitoring [1,2,3,4,5,6,7]
Experimental Results and Discussions e proposed metaheuristic approach used for pitting corrosion detection, named as LSHADE-SVM Classification (SVC)-PCD, has been developed in Visual C#.NET environment (Framework 4.6.2)
To train and test the integrated LSHADE-SVC-PCD model used for pitting corrosion detection, the collected dataset has been divided into two subsets of training and testing datasets. e training dataset, accounting for 90% of the original dataset, is used for model construction and the rest of the dataset is reserved for testing the model generalization
Summary
Ageing infrastructure elements with constraints on maintenance budgets are the main concern of infrastructure management agencies around the world. ese facts urge the implementation of smart and cost-effective methods in the field of structure health monitoring [1,2,3,4,5,6,7]. Ese facts urge the implementation of smart and cost-effective methods in the field of structure health monitoring [1,2,3,4,5,6,7]. Typical objectives of structure health monitoring include the correct recognition of the presence, the location, and the type of the structural defects. It is reported that corrosion is a dominant form of defects with 42% of frequency of failure mechanisms in engineering structures [10]. Pitting corrosion, recognized by isolated corroded damage units within the metal surface, is a severe type of structural defect [3, 13]. Is defect appears on the surface of various civil engineering structures including bridges, high-rise buildings, pipelines, and storage tanks (see Figure 1). Pitting corrosion is generally more harmful than uniform corrosion since it is hard to detect and predict, as well as design against its damages [15]. is localized corrosion damages may have diverse shapes (often hemispherical or Mathematical Problems in Engineering (a)
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