City Research Online

Abstract

Corrosion poses significant challenges to steel structures, making early detection essential to avoid high maintenance costs and potential disasters. Automated detection and extent classification enable precise damage assessment, aiding targeted repair strategies and efficient resource allocation. Traditionally, human surveyors have conducted inspections, but these manual methods are labour-intensive and time-consuming and may yield inconsistent results. To overcome the conventional limitations, this study proposes applying image processing and a Convolutional Neural Network (CNN) to corrosion detection, using a database of 9,920 images captured with regular, portable cell phone cameras. The method uses 15 well-known pre-trained models (AlexNet, DenseNet-121, EfficientNet-B0, GoogleNet, Inception-v3, MobileNet-v1, NASNet, ResNet-18, ResNet-50, ResNet-101, ShuffleNet-v1, SqueezeNet, VGG16, VGG19, and Xception). CNN models are used to detect corrosion at 10 levels, based on the percentage extent of corrosion, with levels ranging from 0% to 10% (level 1) and from 90% to 100% (level 10). After comparing 15 pre-trained models, Xception was chosen as the best, achieving a mean accuracy of 0.72, an accuracy of 0.93, and F1 and recall values of 0.91 and 0.94, respectively. This automated approach can aid early detection of corrosion, enhance maintenance prioritization, and reduce inspection costs for steel structures.

Publication Type:	Article
Additional Information:	© the authors. This AAM is provided for your own personal use only. It may not be used for resale, reprinting, systematic distribution, emailing, or for any other commercial purpose without the permission of the publisher.
Publisher Keywords:	Artificial intelligence, Corrosion, Field testing & monitoring, Maintenance & inspection, Steel structures
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TA Engineering (General). Civil engineering (General) T Technology > TH Building construction T Technology > TJ Mechanical engineering and machinery
Departments:	School of Science & Technology School of Science & Technology > Department of Engineering
SWORD Depositor:	Symplectic Administrator

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