Infrastructure Lifecycle Corrosion Management Using AI Analytics and Digital Twins

Corrosion in infrastructure creates high-risk scenarios, and mitigation strategies are expensive, with significant annual costs globally. This paper advances the discourse of corrosion monitoring and tracking in infrastructure, emphasizing the importance of data analytics, AI, and Digital Twins (DT) for managing the infrastructure lifecycle while reducing risk and costs associated with corrosion. The non-parametric analysis of corrosion data is demonstrated to provide insights into spatial and temporal variations, helping in predictive modeling and decision-making. Strategic sampling and analysis of corrosion data help in making evidence-based maintenance decisions, reducing costs, and improving safety. AI analytics enhances the functionality of corrosion databases and Digital Twins, enabling predictive analytics and real-time simulations for better decision-making. Recommendations are provided for the implementation of AI in engineering applications, including data quantity and training resources, but offer significant potential for improved corrosion management.