Issue |
E3S Web of Conf.
Volume 529, 2024
International Conference on Sustainable Goals in Materials, Energy and Environment (ICSMEE’24)
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Article Number | 04012 | |
Number of page(s) | 10 | |
Section | Advanced Interdisciplinary Approaches | |
DOI | https://doi.org/10.1051/e3sconf/202452904012 | |
Published online | 29 May 2024 |
Digging Deeper: The Role of Big Data Analytics in Geotechnical Investigations
1 Institute of Aeronautical Engineering, Dundigal, Hyderabad
2 Department of Applied Sciences, New Horizon College of Engineering, Bangalore, India
3 Lovely Professional University, Phagwara, India
4 Department of Management Studies, Mangalam College of Engineering, Kottayam 686631, Kerala
5 Lloyd Institute of Management and Technology, Greater Noida, Uttar Pradesh, India-201306
6 Department of computers Techniques engineering, College of technical engineering, The Islamic University, Najaf, Iraq
7 Lloyd Institute of Engineering & Technology, Knowledge Park II, Greater Noida, Uttar Pradesh 201306
* Corresponding author: v.divyavani@iare.ac.in
This review paper explores the transformative role of big data analytics in geotechnical engineering, transferring past conventional methods to a data-driven paradigm that complements decision-making and precision in subsurface investigations. By integrating large statistics analytics with geotechnical engineering, this study demonstrates big improvements in website characterization, danger assessment, and production methodologies. The research underscores the capability of big data to revolutionize geotechnical investigations through improved prediction models, threat management, and sustainable engineering practices, highlighting the critical role of big data in addressing international warming and ozone depletion. Through the examination of numerous case studies and AI-driven methodologies, this paper sheds light at the efficiency gains and environmental benefits attainable in geotechnical engineering.
Key words: Big-data / geotechnical engineering / artificial intelligence / machine learning
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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