A Novel Approach for Automatic Detection of Concrete Surface Voids Using Image Texture Analysis and History-Based Adaptive Differential Evolution Optimized Support Vector Machine
Joint Authors
Nguyen, Quoc-Lam
Hoang, Nhat-Duc
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-28
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
To inspect the quality of concrete structures, surface voids or bugholes existing on a concrete surface after the casting process needs to be detected.
To improve the productivity of the inspection work, this study develops a hybrid intelligence approach that combines image texture analysis, machine learning, and metaheuristic optimization.
Image texture computations employ the Gabor filter and gray-level run lengths to characterize the condition of a concrete surface.
Based on features of image texture, Support Vector Machines (SVM) establish a decision boundary that separates collected image samples into two categories of no surface void (negative class) and surface void (positive class).
Furthermore, to assist the SVM model training phase, the state-of-the-art history-based adaptive differential evolution with linear population size reduction (L-SHADE) is utilized.
The hybrid intelligence approach, named as L-SHADE-SVM-SVD, has been developed and complied in Visual C#.NET framework.
Experiments with 1000 image samples show that the L-SHADE-SVM-SVD can obtain a high prediction accuracy of roughly 93%.
Therefore, the newly developed model can be a promising alternative for construction inspectors in concrete quality assessment.
American Psychological Association (APA)
Hoang, Nhat-Duc& Nguyen, Quoc-Lam. 2020. A Novel Approach for Automatic Detection of Concrete Surface Voids Using Image Texture Analysis and History-Based Adaptive Differential Evolution Optimized Support Vector Machine. Advances in Civil Engineering،Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1121552
Modern Language Association (MLA)
Hoang, Nhat-Duc& Nguyen, Quoc-Lam. A Novel Approach for Automatic Detection of Concrete Surface Voids Using Image Texture Analysis and History-Based Adaptive Differential Evolution Optimized Support Vector Machine. Advances in Civil Engineering No. 2020 (2020), pp.1-15.
https://search.emarefa.net/detail/BIM-1121552
American Medical Association (AMA)
Hoang, Nhat-Duc& Nguyen, Quoc-Lam. A Novel Approach for Automatic Detection of Concrete Surface Voids Using Image Texture Analysis and History-Based Adaptive Differential Evolution Optimized Support Vector Machine. Advances in Civil Engineering. 2020. Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1121552
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1121552