AI in research, education, and practice of structural and earthquake engineering - a reflection on impacts, challenges, and future directions
DOI:
https://doi.org/10.29352/mill0226.39584Abstract
The fields of structural and earthquake engineering are critical in ensuring the safety and resilience of our built environment, particularly as global challenges such as urbanization, climate change, and disaster preparedness become increasingly urgent (Ferreira & Santos, 2024). In recent years, the integration of engineering with artificial intelligence (AI), machine learning (ML), and deep learning (DL) has begun to reshape traditional paradigms. These technologies offer new opportunities to automate processes, enhance predictive accuracy, and optimize designs, paving the way for more efficient, adaptive, and sustainable engineering practices.
Despite its transformative potential, the adoption of AI in structural and earthquake engineering remains relatively underutilized compared to other fields (Tapeh & Naser, 2023; Xie et al., 2020). Traditional mechanics-based methodologies continue to dominate the field, and it remains to be seen how AI-driven approaches will coexist with these established practices, particularly in the professional and educational contexts. Some skepticism surrounding AI’s perceived opacity—often viewed as a "black box" compared to the transparency of experimental, numerical, and analytical methods—further complicates its integration. However, AI’s unparalleled ability to process extensive datasets, execute computationally intensive tasks, and adapt to real-time conditions presents significant opportunities for innovation.
In this editorial, I aim to provide a concise yet comprehensive reflection on AI's current state and transformative potential in structural and earthquake engineering. By looking at its impacts across research, education, and practical implementation, I hope to highlight some opportunities and challenges for AI integration. Furthermore, I will emphasize pressing concerns, such as the importance of fostering interdisciplinary collaboration and addressing the significant environmental footprint of AI technologies—an aspect that, in my view, has not received the attention it deserves.
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