MARS Applications in Geotechnical Engineering Systems (eBook)

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. 

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.

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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.