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 Modeling
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.
G. Ellis, “Creating a learning environment that supports innovation and Deep Learning in geotechnical engineering,” 2012 ASEE Annual Conference & Exposition Proceedings. doi:10.18260/1-2—21109
W. Zhang and K.-K. Phoon, “Editorial for advances and applications of Deep Learning and Soft Computing in Geotechnical Underground Engineering,” Journal of Rock Mechanics and Geotechnical Engineering, vol. 14, no. 3, pp. 671–673, 2022. doi:10.1016/j.jrmge.2022.01.001
Z. Z. Wang, “Deep learning for geotechnical reliability analysis with multiple uncertainties,” Journal of Geotechnical and Geoenvironmental Engineering, vol. 148, no. 4, 2022. doi:10.1061/(asce)gt.1943-5606.0002771
U. Michelucci, “Advanced CNNS and transfer learning,” Advanced Applied Deep Learning, pp. 125–160, 2019. doi:10.1007/978-1-4842-4976-5_4
MAO AND Q. LI, “GENERATIVE ADVERSARIAL NETWORKS (GANS),” GENERATIVE ADVERSARIAL NETWORKS FOR IMAGE GENERATION, PP. 1–7, 2020. DOI:10.1007/978-981-33-6048-8_1
“Geographical Information Systems (GIS),” Environmental Sciences: A Student’s Companion, pp. 314–314, 2009. doi:10.4135/9781446216187.n156
G. Dong, L. Duan, J. Nummenmaa, and P. Zhang, “Feature generation and feature engineering for sequences,” Feature Engineering for Machine Learning and Data Analytics, pp. 145–166, 2018. doi:10.1201/9781315181080-6
Gupta, R., Mane, M., Bhardwaj, S., Nandekar, U., Afaq, A., Dhabliya, D., Pandey, B.K. Use of artificial intelligence for image processing to aid digital forensics: Legislative challenges (2023) Handbook of Research on Thrust Technologies? Effect on Image Processing, pp. 433-447.
Mr. Dharmesh Dhabliya. (2012). Intelligent Banal type INS based Wassily chair (INSW). International Journal of New Practices in Management and Engineering, 1(01), 01 - 08. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/2
Dhabliya, P. D. . (2020). Multispectral Image Analysis Using Feature Extraction with Classification for Agricultural Crop Cultivation Based On 4G Wireless IOT Networks. Research Journal of Computer Systems and Engineering, 1(1), 01–05. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/10
How to Cite
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.