Multiclass damage detection in concrete structures using a transfer learning‐based generative adversarial networks. Issue 11 (19th August 2022) - Dunphy, Kyle; Sadhu, Ayan; Wang, Jinfei
Summary: A large amount of the world's existing infrastructure is reaching the end of its service life, requiring intervention in the form of structural rehabilitation or replacement. A critical aspect of such asset management is the condition assessment of these structures to evaluate their existing health and dictate the scheduling and extent of required rehabilitation. It has been demonstrated that human‐based manual inspections face logistical constraints and are expensive, time extensive, and subjective, depending on the knowledge of the inspection. Recently, autonomous vision‐based techniques have been proposed as an alternative, more accurate method for the inspection of deteriorating structures. Convolutional neural networks (CNNs) have demonstrated state‐of‐the‐art accuracy with respect to damage classification for concrete structures and are often implemented to process images taken from vision‐based sensors such as cameras, smartphones, and drones. However, these archetypes require a large database of annotated images to train the network to an accurate level, which is not readily available for real‐life structures. Moreover, CNNs are limited to the extent by which they are trained; they are often only trained for binary damage classification of a singular material model. This paper addresses these challenges of CNNs through the application of a generative adversarial network (GANs) for multiclass damage detection of concrete structures. The proposed GAN is trained using the SDNET2018 dataset to detect cracking, spalling, pitting, and construction joints in concrete surfaces. Moreover, transfer learning is implemented to transfer the learned features of the GAN to a CNN architecture to allow for accurate image classification. It is concluded that, for a 0%–30% reduction in the amount of labeled data used, the proposed GAN method has comparable accuracy to traditional CNNs.