The Future of Geotechnical Engineering Through Deep Learning: A Concise Literature Review

Deep Learning (DL) has rapidly become a transformative force across various industries, and geotechnical engineering is no exception. The ability of DL models to autonomously learn and identify intricate patterns in vast datasets has made them invaluable in addressing the complexities inherent in geotechnical problems. These advanced computational models have the potential to revolutionize how engineers analyze subsurface conditions, predict geological phenomena, and design infrastructure, making them an essential tool in the evolving landscape of geotechnical research and practice. This review paper presents a thorough exploration of DL techniques specifically tailored to the needs of geotechnical engineering. The paper begins by providing an in-depth analysis of the foundational principles of deep learning, followed by an examination of various DL architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), and their applicability to geotechnical challenges. The discussion includes the integration of these methods into traditional geotechnical practices such as soil characterization, rock mechanics, foundation design, and slope stability analysis. Furthermore, this review highlights the advantages of utilizing DL for modeling complex geotechnical systems, particularly in the context of predictive modeling and forecasting. It showcases examples where DL has been employed to improve the accuracy of site-specific predictions, enhance decision-making processes, and optimize resource allocation in engineering projects. Alongside these advancements, the paper also delves into the obstacles and limitations encountered when implementing DL in geotechnical applications, including the need for high-quality data, interpretability of results, and computational resource requirements. The paper concludes by identifying emerging opportunities for future research and technological advancements in this domain. A particular emphasis is placed on the integration of artificial intelligence (AI) with geotechnical engineering, exploring the potential synergy between DL and other AI techniques such as machine learning and evolutionary algorithms. As the field of DL continues to evolve, the paper suggests avenues for continued exploration, particularly in improving the robustness of models, enhancing their interpretability, and scaling them for large-scale, real-world geotechnical projects.