The Application of Machine Learning in Geotechnical Engineering
The purpose of the following reprint is to update readers on the latest applications of machine learning methods in the field of geotechnical engineering, from planning and design to construction. Because the objects of geotechnical engineering are natural geological bodies, whose mechanical propert...
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| Formatua: | Online |
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| Hizkuntza: | ingelesa |
| Argitaratua: |
MDPI - Multidisciplinary Digital Publishing Institute
2025
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| Sarrera elektronikoa: | ONIX_20250220_9783725822478_182 |
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| _version_ | 1869516121578668032 |
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| collection | Directory of Open Access Books |
| description | The purpose of the following reprint is to update readers on the latest applications of machine learning methods in the field of geotechnical engineering, from planning and design to construction. Because the objects of geotechnical engineering are natural geological bodies, whose mechanical properties and internal structure are very complex, most geotechnical engineering problems involve the coupling of multiple fields and multiple phases. Therefore, traditional methods (e.g., theoretical methods, numerical methods, and experimental methods) cannot solve geotechnical engineering problems well. The development of artificial intelligence has supported better solutions to geotechnical engineering problems, and machine learning methods have been applied widely, currently representing a hot research topic. As a part of this reprint, leading experts in the field share their insights, research findings, and visions for the future. Together, we embark on a journey to unlock the full potential of machine learning method applications in the field of geotechnical engineering. |
| format | Online |
| id | doab-20.500.12854ir-152818 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1528182025-02-20T13:07:19Z The Application of Machine Learning in Geotechnical Engineering Gao, Wei Artificial neural networks Deep learning Swarm intelligence Evolutionary algorithms Geotechnical engineering Slope engineering Underground engineering Foundation engineering Geomechanics thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::R Earth Sciences, Geography, Environment, Planning The purpose of the following reprint is to update readers on the latest applications of machine learning methods in the field of geotechnical engineering, from planning and design to construction. Because the objects of geotechnical engineering are natural geological bodies, whose mechanical properties and internal structure are very complex, most geotechnical engineering problems involve the coupling of multiple fields and multiple phases. Therefore, traditional methods (e.g., theoretical methods, numerical methods, and experimental methods) cannot solve geotechnical engineering problems well. The development of artificial intelligence has supported better solutions to geotechnical engineering problems, and machine learning methods have been applied widely, currently representing a hot research topic. As a part of this reprint, leading experts in the field share their insights, research findings, and visions for the future. Together, we embark on a journey to unlock the full potential of machine learning method applications in the field of geotechnical engineering. 2025-02-20T13:07:17Z 2025-02-20T13:07:17Z 2024 book ONIX_20250220_9783725822478_182 9783725822478 9783725822485 https://directory.doabooks.org/handle/20.500.12854/152818 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/10026 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-2248-5 10.3390/books978-3-7258-2248-5 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725822478 9783725822485 364 Basel open access |
| spellingShingle | Artificial neural networks Deep learning Swarm intelligence Evolutionary algorithms Geotechnical engineering Slope engineering Underground engineering Foundation engineering Geomechanics thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::R Earth Sciences, Geography, Environment, Planning The Application of Machine Learning in Geotechnical Engineering |
| title | The Application of Machine Learning in Geotechnical Engineering |
| title_full | The Application of Machine Learning in Geotechnical Engineering |
| title_fullStr | The Application of Machine Learning in Geotechnical Engineering |
| title_full_unstemmed | The Application of Machine Learning in Geotechnical Engineering |
| title_short | The Application of Machine Learning in Geotechnical Engineering |
| title_sort | application of machine learning in geotechnical engineering |
| topic | Artificial neural networks Deep learning Swarm intelligence Evolutionary algorithms Geotechnical engineering Slope engineering Underground engineering Foundation engineering Geomechanics thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::R Earth Sciences, Geography, Environment, Planning |
| topic_facet | Artificial neural networks Deep learning Swarm intelligence Evolutionary algorithms Geotechnical engineering Slope engineering Underground engineering Foundation engineering Geomechanics thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::R Earth Sciences, Geography, Environment, Planning |
| url | ONIX_20250220_9783725822478_182 |