Identification of Civil Infrastructure Damage Using Ensemble Transfer Learning Model. (20th October 2021)

• Record Type: Journal Article
• Authors: Shamila Ebenezer, A.; Deepa Kanmani, S.; Sheela, V.; Ramalakshmi, K.; Chandran, V.; Sumithra, M. G.; Elakkiya, B.; Murugesan, Bharani
• Other Names: Gupta B. B. Academic Editor.
• Is Part Of: Advances in civil engineering. Volume 2021(2021)
• Publication Date: 2021-10-20
• Subjects: Civil engineering -- Periodicals; Civil engineering; Periodicals; 624
• DOI: 10.1155/2021/5589688
• Languages: English
• ISSNs: 1687-8086

Abstract

Abstract : This article uses cutting-edge deep learning technology to identify structural damage from images for a civil engineering application. The public infrastructures of the country are generally inspected physically by a visual evaluation by qualified inspectors. However, manual inspections are pretty time-consuming and often require too much labor. The number of experts capable of evaluating such structural damage is inadequate. As a result, computer vision-based techniques for automatic damage detection have been developed. This paper's civil infrastructure damages are classified into four damages of roads common in Indian highways and the concrete deterioration in the bridges. The convolutional neural network has become a standard tool for organizing and recognizing images. In this paper, an ensemble of three CNN models is proposed, and two are transfer learning-based models. The proposed ensemble transfer learning model provided a validation accuracy of 87.1%.