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Titlebook: Civil Engineering for Multi-Hazard Risk Reduction; Select Proceedings o K. S. Sreekeshava,Sreevalsa Kolathayar,N. Vinod Ch Conference proce

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書(shū)目名稱Civil Engineering for Multi-Hazard Risk Reduction
副標(biāo)題Select Proceedings o
編輯K. S. Sreekeshava,Sreevalsa Kolathayar,N. Vinod Ch
視頻videohttp://file.papertrans.cn/227/226880/226880.mp4
概述Addresses developments related to advanced interdisciplinary sustainable approaches.Presents select proceedings of the IACESD 2023.Covers topics like Sustainable and Resilient Communities and Sustaina
叢書(shū)名稱Lecture Notes in Civil Engineering
圖書(shū)封面Titlebook: Civil Engineering for Multi-Hazard Risk Reduction; Select Proceedings o K. S. Sreekeshava,Sreevalsa Kolathayar,N. Vinod Ch Conference proce
描述.This book presents select proceedings of the International Conference on Interdisciplinary Approaches in Civil Engineering for Sustainable Development (IACESD 2023) hosted under the aegis of the Group of Twenty (G20) and Civil 20 (C20) at Jyothy Institute of Technology, Bengaluru, India. The topics covered include sustainable and resilient communities, sustainable construction materials, disaster resilient infrastructure, nano-composites and bio-composites, sustainable geotechnics and earthquake engineering. This book serves as a resource material for researchers and industry professionals interested in disaster risk reduction..
出版日期Conference proceedings 2024
關(guān)鍵詞Sustainable and Resilient Communities; Sustainable Construction Materials; Disaster Resilient Infrastr
版次1
doihttps://doi.org/10.1007/978-981-99-9610-0
isbn_softcover978-981-99-9612-4
isbn_ebook978-981-99-9610-0Series ISSN 2366-2557 Series E-ISSN 2366-2565
issn_series 2366-2557
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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Human Activity Recognition in Construction Industry Using Machine Learning Pose Estimation Technique Green, Blue) video frames. The suggested method in this project achieves real-time performance on the majority of modern mobile phones, desktops/laptops, Python, and even the web, in contrast to current state-of-the-art methodologies, which rely mostly on strong desktop environments for inference.
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SSI on Geodesic Dome Using RSM and Comparison with ANNeloped with four inputs as soil spring values and four outputs as base shear values for zone II, III, IV, and V using the Levenberg–Marquardt algorithm. It is observed from the study that the ANN model can predict the dynamic characteristics and seismic response of the soil-structure system with 98%
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