Abstract
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.
Highlights
The Employed Image Processing and Computational Intelligence MethodsSince the surface of a concrete structure contains a diverse form of texture (e.g., intact surface, cracks, bugholes, and stains), texture information of an image region needs to be analyzed to support the surface void detection process
Ese requirements often involve the delivery of highquality concrete surface with minimum presence of surface voids or bugholes [3]
A high density of surface voids can result in several harmful effects on the performance of concrete structures: (i) Bugholes obviously reduce the aesthetics of concrete structures (ii) ese voids reduce the protective depth of concrete structures and make the reinforcements inside them more vulnerable to corrosion [6]
Summary
Since the surface of a concrete structure contains a diverse form of texture (e.g., intact surface, cracks, bugholes, and stains), texture information of an image region needs to be analyzed to support the surface void detection process. Introduced by Vapnik [70], the SVM have gained popularity in the research community via various works which reported their successful implementations [71,72,73] It is because this machine learning method features significant advantages including resilience to noisy data via a framework of maximum margin construction and capability of handling nonlinearly separable data by means of kernel tricks. It is noted that a numerical feature xk is texture information extracted from an image sample using the Gabor filter and the GLRL. SV represents the number of support vectors (the number of αk > 0 )
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
