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MARS Applications in Geotechnical Engineering Systems

Multi-Dimension with Big Data

Authors: Zhang, Wengang

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  • Presents the nonparametric regression algorithm known as multivariate adaptive regression splines (MARS) and its applications 
  • Introduces simple algorithms that are easy to interpret and deliver good computational efficiency 
  • Provides numerous examples and highlights geotechnical applications of big data to facilitate reader comprehension 
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eBook 96,29 €
price for Spain (gross)
  • ISBN 978-981-13-7422-7
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  • Immediate eBook download after purchase
Hardcover 124,79 €
price for Spain (gross)
  • ISBN 978-981-13-7421-0
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  • Immediate ebook access, if available*, with your print order
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About this book

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. 

About the authors

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.

Table of contents (14 chapters)

Table of contents (14 chapters)

Buy this book

eBook 96,29 €
price for Spain (gross)
  • ISBN 978-981-13-7422-7
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 124,79 €
price for Spain (gross)
  • ISBN 978-981-13-7421-0
  • Free shipping for individuals worldwide
  • Immediate ebook access, if available*, with your print order
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
MARS Applications in Geotechnical Engineering Systems
Book Subtitle
Multi-Dimension with Big Data
Authors
Copyright
2020
Publisher
Springer Singapore
Copyright Holder
Science Press and Springer Nature Singapore Pte Ltd.
eBook ISBN
978-981-13-7422-7
DOI
10.1007/978-981-13-7422-7
Hardcover ISBN
978-981-13-7421-0
Edition Number
1
Number of Pages
XXI, 240
Number of Illustrations
35 b/w illustrations, 64 illustrations in colour
Additional Information
Jointly published with Science Press, Beijing, China
Topics

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