Evaluation of Machine Learning Methods for Image Classification: A Case Study of Facility Surface Damage
Pages 1 - 10
Abstract
Common reinforced concrete (RC) damage includes exposed rebars, spalling, and efflorescence, which not only affect the aesthetics of facilities but also cause structural degradation over time, setting the stage for further severe RC degradation that would reduce the strength and durability of the structure. Damage to RC facilities occurs because of their natural deterioration. Machine learning can be employed to effectively identify various damage areas, and the findings can serve as a reference to management units in the task of ensuring the structural safety of facilities. In this study, a damage image was used to evaluate image classification capabilities achievable through maximum likelihood and random forest supervised machine learning methods. With these methods, accuracies of 98.6% and 96% were achieved for RC damage classification, respectively. The results of this study demonstrate that the use of machine learning can yield favorable results for damage image classification.
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Published In
Dec 2021
170 pages
ISBN:978-3-030-98977-4
DOI:10.1007/978-3-030-98978-1
© Springer Nature Switzerland AG 2022.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 01 December 2021
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