Artificial intelligence-assisted civil engineering: Digital twins for the wind energy infrastructure

In the landscape of contemporary Civil Engineering, Artificial Intelligence (AI) stands as a critical pillar of innovation, fundamentally transforming the operation and maintenance (O&M) of infrastructures systems. The present study focuses on the integration of AI to boost digital twins in addressing the complexities of life-cycle management of infrastructure assets. AIboosted digital twins embody a synthesis of real-time data acquisition, advanced analytics, and predictive modelling, marking a significant departure from traditional O&M approaches that are often reactive and less informed. The methodology employed encapsulates the convergence of data and model twin-driven insights and computational intelligence, using environmental conditions to feed sophisticated probabilistic models and multi-physics simulations. This research specifically investigates the application of these technologies in the context of a case study on floating offshore wind turbines (FOWTs), yet the primary focus is the expansive role of AI-boosted digital twins across Civil Engineering domains. Significant findings from the study reveal the capability of AI-boosted digital twins to identify potential failure modes in structural components, predict the evolution of deterioration, and recommend timely O&M interventions in terms of different actions. In general, the present approach not only enhances the predictive accuracy of structural health assessments, but also optimizes resource allocation and minimizes downtime. By distilling the essence of these digital twins into actionable insights, the research underscores their potential to revolutionize infrastructure management. The implications are vast, heralding a new era of intelligent O&M strategies that promise increased safety, extended service life and cost-effectiveness. The integration of AI-boosted digital twins is posited to become an industry standard, advocating for a shift towards more resilient, adaptive, and intelligent Civil Engineering practices.