Soft Computing and Machine Learning in Dam Engineering
“Soft Computing and Machine Learning in Dam Engineering” is a comprehensive, edited Special Issue that explores the latest advances in the application of soft computing and machine learning techniques to dam engineering. This reprint covers a range of topics, including dam design, construction, moni...
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| Formato: | Online |
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| Idioma: | inglês |
| Publicado em: |
MDPI - Multidisciplinary Digital Publishing Institute
2023
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| Assuntos: | |
| Acesso em linha: | ONIX_20230623_9783036575797_35 |
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| _version_ | 1869519214278082560 |
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| collection | Directory of Open Access Books |
| description | “Soft Computing and Machine Learning in Dam Engineering” is a comprehensive, edited Special Issue that explores the latest advances in the application of soft computing and machine learning techniques to dam engineering. This reprint covers a range of topics, including dam design, construction, monitoring, and maintenance, and provides readers with a deep understanding of the theoretical foundations and practical applications of these techniques.Featuring contributions from leading experts in the field, the reprint presents a collection of 11 papers that offer insights into state-of-the-art approaches in dam engineering. The chapters cover topics such as fuzzy logic, genetic algorithms, artificial neural networks, and support vector machines, and provide practical examples of how these techniques can be applied to solve real-world dam engineering problems.Whether you are a researcher, engineer, or student in the field of dam engineering, “Soft Computing and Machine Learning in Dam Engineering” provides a valuable resource for staying up-to-date with the latest techniques and approaches in the field. |
| format | Online |
| id | doab-20.500.12854ir-100803 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1008032024-04-09T23:16:03Z Soft Computing and Machine Learning in Dam Engineering Hariri-Ardebili, M. Amin Salazar, Fernando Pourkamali-Anaraki, Farhad Mazzà, Guido Mata, Juan dams Polynomial Chaos Expansion random fields random forest vibration analysis gravity dams safety assessment probabilistic analysis parameter uncertainty sample optimization variance-based sensitivity analysis sensitivity analysis polynomial chaos expansion uncertainty deep neural networks rockfill dams anomaly detection machine learning support vector machines one-class classification concrete dam machine learning methods structural behaviour model validation ice loads concrete dams back-calculation dam safety monitoring arch dams seismic safety endurance time analysis non-linear seismic analysis concrete damage model tensile and compressive damage design variable finite element feasibility design surrogate AutoML roller compacted concrete (RCC) risk-informed design Cascadia subduction zone (CSZ) non-linear structural analysis multilayer perceptron neural network model structural health monitoring threshold definition moving average of the residuals moving standard deviation of the residuals DBSCAN n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology “Soft Computing and Machine Learning in Dam Engineering” is a comprehensive, edited Special Issue that explores the latest advances in the application of soft computing and machine learning techniques to dam engineering. This reprint covers a range of topics, including dam design, construction, monitoring, and maintenance, and provides readers with a deep understanding of the theoretical foundations and practical applications of these techniques.Featuring contributions from leading experts in the field, the reprint presents a collection of 11 papers that offer insights into state-of-the-art approaches in dam engineering. The chapters cover topics such as fuzzy logic, genetic algorithms, artificial neural networks, and support vector machines, and provide practical examples of how these techniques can be applied to solve real-world dam engineering problems.Whether you are a researcher, engineer, or student in the field of dam engineering, “Soft Computing and Machine Learning in Dam Engineering” provides a valuable resource for staying up-to-date with the latest techniques and approaches in the field. 2023-06-23T09:43:34Z 2023-06-23T09:43:34Z 2023 book ONIX_20230623_9783036575797_35 9783036575797 9783036575780 https://directory.doabooks.org/handle/20.500.12854/100803 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/7266 https://mdpi.com/books/pdfview/book/7266 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-7578-0 10.3390/books978-3-0365-7578-0 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036575797 9783036575780 260 Basel open access |
| spellingShingle | dams Polynomial Chaos Expansion random fields random forest vibration analysis gravity dams safety assessment probabilistic analysis parameter uncertainty sample optimization variance-based sensitivity analysis sensitivity analysis polynomial chaos expansion uncertainty deep neural networks rockfill dams anomaly detection machine learning support vector machines one-class classification concrete dam machine learning methods structural behaviour model validation ice loads concrete dams back-calculation dam safety monitoring arch dams seismic safety endurance time analysis non-linear seismic analysis concrete damage model tensile and compressive damage design variable finite element feasibility design surrogate AutoML roller compacted concrete (RCC) risk-informed design Cascadia subduction zone (CSZ) non-linear structural analysis multilayer perceptron neural network model structural health monitoring threshold definition moving average of the residuals moving standard deviation of the residuals DBSCAN n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Soft Computing and Machine Learning in Dam Engineering |
| title | Soft Computing and Machine Learning in Dam Engineering |
| title_full | Soft Computing and Machine Learning in Dam Engineering |
| title_fullStr | Soft Computing and Machine Learning in Dam Engineering |
| title_full_unstemmed | Soft Computing and Machine Learning in Dam Engineering |
| title_short | Soft Computing and Machine Learning in Dam Engineering |
| title_sort | soft computing and machine learning in dam engineering |
| topic | dams Polynomial Chaos Expansion random fields random forest vibration analysis gravity dams safety assessment probabilistic analysis parameter uncertainty sample optimization variance-based sensitivity analysis sensitivity analysis polynomial chaos expansion uncertainty deep neural networks rockfill dams anomaly detection machine learning support vector machines one-class classification concrete dam machine learning methods structural behaviour model validation ice loads concrete dams back-calculation dam safety monitoring arch dams seismic safety endurance time analysis non-linear seismic analysis concrete damage model tensile and compressive damage design variable finite element feasibility design surrogate AutoML roller compacted concrete (RCC) risk-informed design Cascadia subduction zone (CSZ) non-linear structural analysis multilayer perceptron neural network model structural health monitoring threshold definition moving average of the residuals moving standard deviation of the residuals DBSCAN n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | dams Polynomial Chaos Expansion random fields random forest vibration analysis gravity dams safety assessment probabilistic analysis parameter uncertainty sample optimization variance-based sensitivity analysis sensitivity analysis polynomial chaos expansion uncertainty deep neural networks rockfill dams anomaly detection machine learning support vector machines one-class classification concrete dam machine learning methods structural behaviour model validation ice loads concrete dams back-calculation dam safety monitoring arch dams seismic safety endurance time analysis non-linear seismic analysis concrete damage model tensile and compressive damage design variable finite element feasibility design surrogate AutoML roller compacted concrete (RCC) risk-informed design Cascadia subduction zone (CSZ) non-linear structural analysis multilayer perceptron neural network model structural health monitoring threshold definition moving average of the residuals moving standard deviation of the residuals DBSCAN n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | ONIX_20230623_9783036575797_35 |