Application of Machine Learning in Slope Stability Assessment
This book focuses on the application of machine learning in slope stability assessment. The contents include: overview of machine learning approaches, the mainstream smart in-situ monitoring techniques, the applications of the main machine learning algorithms, including the supervised learning, unsu...
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| Main Authors: | , , , , |
|---|---|
| Autor Corporativo: | |
| Formato: | Recurso Electrónico livro electrónico |
| Idioma: | inglês |
| Publicado em: |
Singapore :
Springer Nature Singapore : Imprint: Springer,
2023.
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| Edição: | 1st ed. 2023. |
| Assuntos: | |
| Acesso em linha: | https://doi.org/10.1007/978-981-99-2756-2 |
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| 100 | 1 | |a Wengang, Zhang. |e author. |0 (orcid)0000-0001-6051-1388 |1 https://orcid.org/0000-0001-6051-1388 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Application of Machine Learning in Slope Stability Assessment |h [electronic resource] / |c by Zhang Wengang, Liu Hanlong, Wang Lin, Zhu Xing, Zhang Yanmei. |
| 250 | |a 1st ed. 2023. | ||
| 264 | 1 | |a Singapore : |b Springer Nature Singapore : |b Imprint: Springer, |c 2023. | |
| 300 | |a 1 online resource (XIX, 201 p. 104 illus.). |b 103 illus. in color. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 341 | 0 | |b Table of contents navigation |2 onix | |
| 341 | 0 | |b Single logical reading order |2 onix | |
| 341 | 0 | |b Short alternative textual descriptions |2 onix | |
| 341 | 0 | |b Use of color is not sole means of conveying information |2 onix | |
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| 505 | 0 | |a Overview -- Remote Sensing Techniques -- Machine Learning Algorithms -- Real-time Monitoring and Early Warning of Landslide -- Prediction of Slope Stability Using Ensemble Learning Techniques -- Application of LSTM and Prophet Algorithm in Slope Displacement Prediction -- Displacement Prediction of Jiuxianping Landslide using GRU Networks -- Efficient Seismic Stability Analysis of Slopes Subjected to Water Level Changes Using Gradient Boosting Algorithms -- Efficient Reliability Analysis of Slopes in Spatially Variable Soils Using XGBoost -- Efficient Time-variant Reliability Analysis of Bazimen Landslide in the TGRA Using XGBoost and LightGBM -- Future work recommendation. | |
| 520 | |a This book focuses on the application of machine learning in slope stability assessment. The contents include: overview of machine learning approaches, the mainstream smart in-situ monitoring techniques, the applications of the main machine learning algorithms, including the supervised learning, unsupervised learning, semi- supervised learning, reinforcement learning, deep learning, ensemble learning, etc., in slope engineering and landslide prevention, introduction of the smart in-situ monitoring and slope stability assessment based on two well-documented case histories, the prediction of slope stability using ensemble learning techniques, the application of Long Short-Term Memory Neural Network and Prophet Algorithm in Slope Displacement Prediction, displacement prediction of Jiuxianping landslide using gated recurrent unit (GRU) networks, seismic stability analysis of slopes subjected to water level changes using gradient boosting algorithms, efficient reliability analysis of slopes in spatially variable soils using XGBoost, efficient time-variant reliability analysis of Bazimen landslide in the Three Gorges Reservoir Area using XGBoost and LightGBM algorithms, as well as the future work recommendation.The authors also provided their own thoughts learnt from these applications as well as work ongoing and future recommendations. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Civil engineering. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Engineering geology. | |
| 650 | 1 | 4 | |a Civil Engineering. |
| 650 | 2 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Geoengineering. |
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| 700 | 1 | |a Lin, Wang. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 700 | 1 | |a Xing, Zhu. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 700 | 1 | |a Yanmei, Zhang. |e author. |0 (orcid)0000-0003-1055-1052 |1 https://orcid.org/0000-0003-1055-1052 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
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