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MARS Applications in Geotechnical Engineering Systems

Multi-Dimension with Big Data

  • Book
  • © 2020

Overview

Authors:
  1. Wengang Zhang

    1. Chongqing University, Chongqing, China

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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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Table of contents (14 chapters)

  1. Front Matter

    Pages i-xxi
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  2. Introduction

    • Wengang Zhang
    Pages 1-5

  3. A Review of Surrogate Models

    • Wengang Zhang
    Pages 7-17

  4. Simple MARS Modeling Examples

    • Wengang Zhang
    Pages 19-25

  5. MARS Use in Prediction of Collapse Potential for Compacted Soils

    • Wengang Zhang
    Pages 27-46

  6. MARS Use in Prediction of Diaphragm Wall Deflections in Soft Clays

    • Wengang Zhang
    Pages 47-83

  7. MARS Use for Inverse Analysis of Soil and Wall Properties in Braced Excavation

    • Wengang Zhang
    Pages 85-102

  8. MARS Use for Estimations of Lateral Wall Deflection Profiles

    • Wengang Zhang
    Pages 103-120

  9. MARS Use for Determination of EPB Tunnel-Related Maximum Surface Settlement

    • Wengang Zhang
    Pages 121-136

  10. MARS Use in HP-Pile Drivability Assessment

    • Wengang Zhang
    Pages 137-154

  11. MARS Use in Estimation of Liquefaction-Induced Lateral Spreading

    • Wengang Zhang
    Pages 155-171

  12. MARS Use in Assessment of Soil Liquefaction Based on Capacity Energy Concept

    • Wengang Zhang
    Pages 173-186

  13. MARS_LR Use in Assessment of Soil Liquefaction

    • Wengang Zhang
    Pages 187-198

  14. MARS Use in Evaluating Entry-Type Excavation Stability

    • Wengang Zhang
    Pages 199-207

  15. Conclusions and Recommendations

    • Wengang Zhang
    Pages 209-211

  16. Back Matter

    Pages 213-240
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Keywords

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. 

Authors and Affiliations

  • Chongqing University, Chongqing, China

    Wengang Zhang

About the author

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 TC309Machine 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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