Deep learning in corrosion assessment and control: a critical review of techniques and challenges
Monika Rajendran, S. Deivalakshmi
Abstract
Abstract Deep learning (DL) techniques are advancing quickly, which has increased interest in leveraging them to analyze intricate corrosion patterns and forecast corrosion behavior. This offers a huge challenge across multiple industries, resulting in large financial losses and safety risks. It results in the creation of solutions that are more accurate, effective, and proactive to tackle corrosion-related challenges, thereby enhancing the safety, reliability, and sustainability of vital infrastructure systems. This review commences by utilizing deep learning algorithms and its applications in corrosion assessment across various sectors, encompassing corrosion detection, prediction, classification, and material degradation analysis. The goal is to offer insights in current status and future prospects of corrosion management.