Deep Learning in Geotechnical Engineering: A Comprehensive Review of Methods and Outcomes
Keywords:
Geotechnical Engineering, Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Geotechnical Analysis, Geotechnical ModelingAbstract
Deep Learning (DL) has emerged as a strong method that is being used in a variety of industries, one of which is geotechnical engineering, owing to its capacity to uncover complex patterns from enormous datasets. This is one of the reasons why DL is being used. Among other reasons, this is one of the justifications for hiring DL. This review paper presents not only a thorough discussion and analysis of the use of DL techniques in geotechnical engineering but also a thorough overview of those methods. In this essay, we study the many different approaches that may be taken and the results that can be achieved by making use of DL. Furthermore, we highlight the applicability of these methodologies and discoveries in geotechnical research, modeling, and forecasting. This article also addresses the problems, opportunities, and prospective study pathways that lie ahead for this rapidly developing area of investigation. Specifically, it focuses on the topic of artificial intelligence.
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