Artificial Intelligence-Aided Design (AIAD) for Structures and Engineering: A State-of-the-Art Review and Future Perspectives

Even with the state-of-the-art technology of computer-aided design and topology optimization, the present structural design still faces the challenges of high dimensionality, multi-objectivity, and multi-constraints, making it knowledge/experience-demanding, labor-intensive, and difficult to achieve or simply lack of global optimality. Structural designers are still searching for new ways to cost-effectively to achieve a possible global optimality in a given structure design, in particular, we are looking for decreasing design knowledge/experience-requirements and reducing design labor and time. In recent years, Artificial Intelligence (AI) technology, characterized by the large language model (LLM) of Machine Learning (ML), for instance Deep Learning (DL), has developed rapidly, fostering the integration of AI technology in structural engineering design and giving rise to the concept and notion of Artificial Intelligence-Aided Design (AIAD). The emergence of AIAD has greatly alleviated the challenges faced by structural design, showing great promise in extrapolative and innovative design concept generation, enhancing efficiency while simplifying the workflow, reducing the design cycle time and cost, and achieving a truly global optimal design. In this article, we present a state-of-the-art overview of applying AIAD to enhance structural design, summarizing the current applications of AIAD in related fields: marine and naval architecture structures, aerospace structures, automotive structures, civil infrastructure structures, topological optimization structure designs, and composite micro-structure design. In addition to discussing of the AIAD application to structural design, the article discusses its current challenges, current development focus, and future perspectives.