The rise of deep learning: AI and engineering applications under the spotlight of the 2024 Nobel prize

The rise of deep learning has brought about transformative advancements in both scientific research and engineering applications. The 2024 Nobel Prizes, particularly in Physics and Chemistry, highlighted the revolutionary impact of deep learning, with AlphaFold’s breakthrough in protein structure prediction exemplifying its potential. This review explores the historical evolution of deep learning, from its foundational theories in neural networks and connectionism to its modern applications in various fields. Focus is given to its use in geotechnical engineering, particularly in geological disaster prediction, tunnel safety monitoring, and structural design optimization. The integration of deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers has enabled significant progress in analyzing complex, unstructured data, offering innovative solutions to longstanding engineering challenges. The review also examines the opportunities and challenges faced by the field, advocating for interdisciplinary collaboration and open data sharing to further unlock deep learning’s potential in advancing both scientific and engineering disciplines. As deep learning continues to evolve, it promises to drive further innovation, shaping the future of engineering practices and scientific discovery.