Artificial Intelligence (AI) is now playing a crucial role not only in everyday life, evidenced by the booming application of Large Language Models (LLMs) such as the Generative Pretrained Transformer (GPT), but also in its potential to transform traditional industries like civil engineering. This work examines the application of novel AI tools to enable Digital Twins (DT) for engineering structures, providing a comprehensive solution for the life-cycle management. A comprehensive state-of-the-art review is conducted to explore existing advancements in sensing, inspection, and simulation that are fundamental to the development of digital twins. Building on this knowledge, a framework is proposed to define DT for Engineering (DT4ENG) based on their emphasis and data flow, including forward DT, backward DT, and DT-informed decision making. Following this, a case study on floating offshore wind turbine (FOWT) structures demonstrates the application of DT4ENG in a specific domain, with findings that have broader implications for the life-cycle management of engineering structures. The present study reveals that the AI enables digital twins to effectively identify potential structural issues, predict deterioration, and suggest timely maintenance interventions. This approach enhances the accuracy of structural health assessments, optimises resource allocation, and minimises downtime. By translating the capabilities of digital twins into actionable strategies, the research highlights their potential to significantly improve the life-cycle management of engineering infrastructure. In general, these advancements promise a new era of intelligent maintenance strategies, offering increased safety, extended service life, and cost-effectiveness. The proposed DT4ENG is set to become a standard in the traditional industry, driving a shift towards more sustainable, resilient, adaptive, and intelligent structures.