Mars Applications in Geotechnical Engineering Systems: Multi-dimension With Big Data - Rilegato

Zhang, Wengang

 
9789811374210: Mars Applications in Geotechnical Engineering Systems: Multi-dimension With Big Data
Rilegato
ISBN 10: 981137421X ISBN 13: 9789811374210
Casa editrice: Springer Nature, 2019

Sinossi

This book presents the application of a comparatively simple nonparametric regression algorithm, known as the multivariate adaptive regression splines (MARS) surrogate model, which can be used to approximate the relationship between the inputs and outputs, and express that relationship mathematically. The book first describes the MARS algorithm, then highlights a number of geotechnical applications with multivariate big data sets to explore the approach’s generalization capabilities and accuracy. As such, it offers a valuable resource for all geotechnical researchers, engineers, and general readers interested in big data analysis. 

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Informazioni sull?autore

Dr. Wengang Zhang is a Professor at the School of Civil Engineering, and the founder and Director of the Green Eco-geotechnique Research Center, Chongqing University, China. He obtained his BSc and MSc degrees at Hohai University, China, and his Ph.D. degree at Nanyang Technological University, Singapore. He worked with Prof. Anthony Goh at NTU as a Project Officer, Research Student, Research Associate, and Research Fellow from 2009 to early 2016. He joined Chongqing University as a “Hundred Young Talent Researcher” in May 2016, and in 2017 he was awarded the “1000 Plan Professorship for Young Talents”. His research interests include probabilistic assessment of underground cavern excavations, numerical modeling of deep braced excavation and reliability analysis, big data and machine learning methods in geotechnical engineering. He is currently a member of the International Society for Soil Mechanics and Geotechnical Engineering (ISSMGE) Technical Committees TC304 Reliability and TC309 Machine Learning. Dr. Zhang is the leading Guest Editor of Geoscience Frontier’s special issue Reliability of Geotechnical Infrastructures. Prof. Zhang’s publications include “Multivariate adaptive regression splines for analysis of geotechnical engineering systems”, “Multivariate adaptive regression splines and neural network models for prediction of pile drivability”, “Assessment of soil liquefaction based on capacity energy concept and multivariate adaptive regression splines” and “An improvement to MLR model for predicting liquefaction-induced lateral spread using multivariate adaptive regression splines”, which have received considerable attention from geotechnical academics and practitioners, as well as readers from interdisciplinary researchers.

Dalla quarta di copertina

This book presents the application of a comparatively simple nonparametric regression algorithm, known as the multivariate adaptive regression splines (MARS) surrogate model, which can be used to approximate the relationship between the inputs and outputs, and express that relationship mathematically. The book first describes the MARS algorithm, then highlights a number of geotechnical applications with multivariate big data sets to explore the approach s generalization capabilities and accuracy. As such, it offers a valuable resource for all geotechnical researchers, engineers, and general readers interested in big data analysis. 

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Editore
Springer Nature
Data di pubblicazione
2019
Lingua
Inglese
ISBN 10 
981137421X
ISBN 13 
9789811374210
Rilegatura
Copertina rigida
Numero edizione
1
Numero di pagine
264
Contatto del produttore
non disponibile
Persona responsabile
CMN Printpool Inh. Mathis Neumann
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Hamburg
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Altre edizioni note dello stesso titolo

9789811374241: MARS Applications in Geotechnical Engineering Systems: Multi-Dimension with Big Data

Edizione in evidenza

ISBN 10:  9811374244 ISBN 13:  9789811374241
Casa editrice: Springer, 2020
Brossura