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A Primer on Machine Learning Applications in Civil Engineering
Machine learning has undergone rapid growth in diversification and practicality, and the repertoire of techniques has evolved and expanded. The aim of this book is to provide a broad overview of the available machine-learning techniques that can be utilized for solving civil engineering problems. Th...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Milton :
CRC Press LLC,
2019.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Deka, Paresh Chandra. | |
| 245 | 1 | 2 | |a A Primer on Machine Learning Applications in Civil Engineering |
| 260 | |a Milton : |b CRC Press LLC, |c 2019. | ||
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| 520 | |a Machine learning has undergone rapid growth in diversification and practicality, and the repertoire of techniques has evolved and expanded. The aim of this book is to provide a broad overview of the available machine-learning techniques that can be utilized for solving civil engineering problems. The fundamentals of both theoretical and practical aspects are discussed in the domains of water resources/hydrological modeling, geotechnical engineering, construction engineering and management, and coastal/marine engineering. Complex civil engineering problems such as drought forecasting, river flow forecasting, modeling evaporation, estimation of dew point temperature, modeling compressive strength of concrete, ground water level forecasting, and significant wave height forecasting are also included. Features Exclusive information on machine learning and data analytics applications with respect to civil engineering Includes many machine learning techniques in numerous civil engineering disciplines Provides ideas on how and where to apply machine learning techniques for problem solving Covers water resources and hydrological modeling, geotechnical engineering, construction engineering and management, coastal and marine engineering, and geographical information systems Includes MATLAB exercises | ||
| 545 | 0 | |a Paresh Chandra Deka earned a bachelor's in civil engineering at the National Institute of Technology, Silchar, Assam, India, and a PhD at the Indian Institute of Technology, Guwahati, specializing in hydrological modeling. Dr. Deka served on the faculty at the School of Postgraduate Studies at Arbaminch University, Ethiopia from 2005 to 2008 and as visiting faculty in 2012 at the Asian Institute of Technology, Bangkok, Thailand. He has supervised 10 PhD scholars as well as 5 current PhD scholars. He has supervised 40 master's theses as well as 4 current master's students. His research area is soft computing applications in water resources engineering and management. Dr. Deka has published 4 books, 5 book chapters, and more than 40 international journal papers. He is a visiting faculty member doing short-term research interaction at Purdue University, Indiana. With more than 28 years of teaching experience, he is currently a professor in the Department of Applied Mechanics and Hydraulics at the National Institute of Technology, Surathkal, Karnataka, India. | |
| 504 | |a Includes bibliographical references. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Civil engineering. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 7 | |a COMPUTERS |x Machine Theory. |2 bisacsh | |
| 650 | 7 | |a MATHEMATICS |x Arithmetic. |2 bisacsh | |
| 650 | 7 | |a TECHNOLOGY |x Engineering |x Civil. |2 bisacsh | |
| 650 | 7 | |a Civil engineering |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
| 758 | |i has work: |a A Primer on Machine Learning Applications in Civil Engineering (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGmBtwRWhMRqvJ8mMMmq33 |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Deka, Paresh Chandra. |t A Primer on Machine Learning Applications in Civil Engineering. |d Milton : CRC Press LLC, ©2019 |z 9781138323391 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=5967199 |z Texto completo |
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