Development of an Optimization Platform for Geotechnical Engineering / Yantu Gongcheng Youhua Pingtai Kaifa Ji Yingyong - Rilegato

Yin, Zhenyu; Jin, Yinfu

Rilegato

ISBN 10: 9811334072 ISBN 13: 9789811334078

Casa editrice: Springer Nature, 2019

Riassunto

This book presents the development of an optimization platform for geotechnical engineering, which is one of the key components in smart geotechnics. The book discusses the fundamentals of the optimization algorithm with constitutive models of soils. Helping readers easily understand the optimization algorithm applied in geotechnical engineering, this book first introduces the methodology of the optimization-based parameter identification, and then elaborates the principle of three newly developed efficient optimization algorithms, followed by the ideas of a variety of laboratory tests and formulations of constitutive models. Moving on to the application of optimization methods in geotechnical engineering, this book presents an optimization-based parameter identification platform with a practical and concise interface based on the above theories. The book is intended for undergraduate and graduate-level teaching in soil mechanics and geotechnical engineering and other related engineering specialties. It is also of use to industry practitioners, due to the inclusion of real-world applications, opening the door to advanced courses on both modeling and algorithm development within the industrial engineering and operations research fields.

Su questo libro

Informazioni sull?autore

Zhen-Yu Yin is an Associate Professor of Geotechnical Engineering at The Hong Kong Polytechnic University since 2018. Dr. Yin received his BSc in Civil Engineering from Zhejiang University in 1997, followed by a 5 years engineering consultancy at the Zhejiang Jiahua Architecture Design Institute. Then, he obtained his MSc and Ph.D. in Geotechnical Engineering at Ecole Centrale de Nantes (France) in 2003 and 2006 respectively. Then he has been working as postdoctoral researcher at Helsinki University of Technology (Finland), University of Strathclyde (Glasgow, UK), Ecole Centrale de Nantes and the University of Massachusetts (Umass-Amherst, USA). In 2010 he joined Shanghai Jiao Tong University as Special Researcher and received the honor of the “Professor of Exceptional Rank of Shanghai Dong-Fang(the Eastern) Scholar". In 2013, he joined Ecole Centrale de Nantes as an Associate Professor before moving to Hong Kong. Dr. Yin has published over 100 articles in peer-reviewed journals. He has been a member of the granular materials committee of American Society of Civil Engineers since 2012.

Yin-Fu Jin received his bachelor's degree in civil engineering from the Northwest A&F University in 2011 and received his Ph.D. degree in geotechnical engineering from Ecole Centrale de Nantes at France in 2016. He is mainly interested in soil mechanics and parameter identification. He has published more than 10 peer-reviewed papers in international journals such as Acta Geotechnica, International Journal for Numerical and Analytical Methods in Geomechanics, Ocean Engineering and Engineering Geology.

Dalla quarta di copertina

This book presents the development of optimization platform for geotechnical engineering which is one of the key components in smart geotechnics. The book discusses the fundamentals of the optimization algorithm with constitutive models of soils. Helping readers easily understand the optimization algorithm applied in geotechnical engineering, this book first introduces the methodology of the optimization-based parameter identification, and then elaborates the principle of three newly developed efficient optimization algorithms, followed by the ideas of a variety of laboratory tests and formulations of constitutive models. Moving on to the application of optimization methods in geotechnical engineering, this book presents an optimization-based parameter identification platform with a practical and concise interface based on the above theories. The book is intended for undergraduate and graduate-level teaching in soil mechanics and geotechnical engineering and other related engineering specialties. It is also of use to industry practitioners, due to the inclusion of real-world applications, opening the door to advanced courses on both modeling and algorithm development within the industrial engineering and operations research fields.

Dettagli dell'edizione

• Editore: Springer Nature
• Data di pubblicazione: 2019
• Lingua: Inglese
• ISBN 10: 9811334072
• ISBN 13: 9789811334078
• Rilegatura: Copertina rigida
• Numero edizione: 1
• Numero di pagine: 396
• Contatto del produttore: non disponibile
• Persona responsabile: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Germania