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Titlebook: Engineering Geology for a Habitable Earth: IAEG XIV Congress 2023 Proceedings, Chengdu, China; Volume 4: Technologi Sijing Wang,Runqiu Huan

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书目名称Engineering Geology for a Habitable Earth: IAEG XIV Congress 2023 Proceedings, Chengdu, China
副标题Volume 4: Technologi
编辑Sijing Wang,Runqiu Huang,Vassilis P. Marinos
视频video
概述Presents a full account of IAEG XIV Congress 2023.Includes contributions of numerous worldwide renowned scientists.Gives a thorough overview on engineering geology for society and different regions
丛书名称Environmental Science and Engineering
图书封面Titlebook: Engineering Geology for a Habitable Earth: IAEG XIV Congress 2023 Proceedings, Chengdu, China; Volume 4: Technologi Sijing Wang,Runqiu Huan
描述.This book collects the selected papers of the XIV Congress of the International Association for Engineering Geology and the Environment held in Chengdu, Sichuan, China from September 21st - 27th, 2023, with the theme of Engineering Geology for a Habitable Earth. The meeting proceedings analyses the dynamic role of engineering geology in our changing world...The congress is expected to enhance the inter-disciplinary research development of international engineering geology and the environment, and contribute to the advancement of major projects, ecological progress, and habitable earth with in-depth discussion in the area of engineering geology and global climate change, geological hazard assessment and prevention, geotechnical properties of rock and soil mass, engineering geology and the environmental issues concerning marine, transportation, urban and ecological environment protection, engineering geology and resilience engineering construction, intelligent engineering geology, and new theories, methods, and techniques in engineering geology..
出版日期Conference proceedings 2024
关键词In-situ Geo-technology; Monitoring Technologies; Multiscale Geotechnical Numerical Modelling; Major Eng
版次1
doihttps://doi.org/10.1007/978-981-99-9069-6
isbn_softcover978-981-99-9071-9
isbn_ebook978-981-99-9069-6Series ISSN 1863-5520 Series E-ISSN 1863-5539
issn_series 1863-5520
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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Genetics of micropropagated woody plantsDeep learning-based semantic segmentation algorithms often require a considerable amount of pixel-level labeled training data. In the study of landslide segmentation research, too high cost of labeling is a barrier to develop deep learning methods. Although the change detection method does not requi
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MICROPROPAGATION OF THE GRAPEVINE, spp.)ry simulations. FO-DAS transforms FO cables into dense seismoacoustic arrays, but previous work has shown effectiveness depends on factors like backfill type and quality. We installed a tight-buffered FO cable in a laboratory test apparatus and varied the surrounding backfill material between air, w
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Patterns of Sectoral and Spatial Change, level. The drawdown of groundwater level caused by pumping will increase the effective stress of the soil, which leads to compression. Nantong, a city in China, is a typical coastal city with thick water-rich sand strata. Groundwater pumping is required before implementation of an excavation. Exces
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Microregionalism in the Zambezi Basin, And then the geological information was interpreted and translated so that the geological mapping of the experimental areas were completed by optimizing the digital image processing method and interpretive inversion system. In the end, a set of human–machine cooperative working mode was set up and
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Microscale Diagnostic Techniquesetaining walls. However, most of current theoretical methods can only consider limited factors due to no proper approach for considering more complex condition. In this study, by means of machine learning, 12 related factors affecting the reinforcement loads were chosen, and these factors are closel
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https://doi.org/10.1007/978-3-030-10662-1dely applied in the surveying the landslides due to its convenience and low cost. As the key step for geophysical interpretation, we applied the DL technique to generate the new generation inversion method due to its strong adaptivity and mapping abilities on the big amount data. In this study, cons
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https://doi.org/10.1007/978-1-4615-5211-6ld geological conditions. This paper compared the performances of four machine learning algorithms, i.e. artificial neural network, support vector machine, decision tree, and random forest in predicting the instabilities of rock slopes along a highway in Anhui Province, China. Eight evaluation indic
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