Data Analysis in Pavement Engineering: Theory and Methodology offers a complete introduction to the basis of the finite element method, covering fundamental theory and worked examples in the detail required for readers to apply the knowledge to their own engineering problems and understand more advanced applications. This edition sees the significant addition of content addressing coupling problems, including Finite element analysis formulations for coupled problems; Details of algorithms for solving coupled problems; and Examples showing how algorithms can be used to solve for piezoelectricity and poroelasticity problems.
Focusing on the core knowledge, mathematical and analytical tools needed for successful application, this book represents the authoritative resource of choice for graduate-level students, researchers and professional engineers involved in finite element-based engineering analysis.
1. Data Analysis in Pavement Engineering - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Table of Contents - p. iv 6. Preface - p. xv 7. Chapter 1 Pavement performance data - p. 1 8. Chapter 2 Fundamentals of statistics - p. 29 9. Chapter 3 Design of experiments - p. 67 10. Chapter 4 Regression - p. 107 11. Chapter 5 Logistic regression - p. 141 12. Chapter 6 Count data models - p. 153 13. Chapter 7 Survival analysis - p. 165 14. Chapter 8 Time series - p. 181 15. Chapter 9 Stochastic process - p. 197 16. Chapter 10 Decision trees and ensemble learning - p. 211 17. Chapter 11 Neural networks - p. 223 18. Chapter 12 Support vector machine and k-nearest neighbors - p. 247 19. Chapter 13 Principal component analysis - p. 263 20. Chapter 14 Factor analysis - p. 275 21. Chapter 15 Cluster analysis - p. 289 22. Chapter 16 Discriminant analysis - p. 303 23. Chapter 17 Structural equation model - p. 315 24. Chapter 18 Markov chain Monte Carlo - p. 337 25. Index - p. 347
Data Analysis in Pavement Engineering: Theory and Methodology offers a complete introduction to the basis of the finite element method, covering fundamental theory and worked examples in the detail required for readers to apply the knowledge to their own engineering problems and understand more advanced applications. This edition sees the significant addition of content addressing coupling problems, including Finite element analysis formulations for coupled problems; Details of algorithms for solving coupled problems; and Examples showing how algorithms can be used to solve for piezoelectricity and poroelasticity problems.
Focusing on the core knowledge, mathematical and analytical tools needed for successful application, this book represents the authoritative resource of choice for graduate-level students, researchers and professional engineers involved in finite element-based engineering analysis.
1. Data Analysis in Pavement Engineering - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Table of Contents - p. iv 6. Preface - p. xv 7. Chapter 1 Pavement performance data - p. 1 8. Chapter 2 Fundamentals of statistics - p. 29 9. Chapter 3 Design of experiments - p. 67 10. Chapter 4 Regression - p. 107 11. Chapter 5 Logistic regression - p. 141 12. Chapter 6 Count data models - p. 153 13. Chapter 7 Survival analysis - p. 165 14. Chapter 8 Time series - p. 181 15. Chapter 9 Stochastic process - p. 197 16. Chapter 10 Decision trees and ensemble learning - p. 211 17. Chapter 11 Neural networks - p. 223 18. Chapter 12 Support vector machine and k-nearest neighbors - p. 247 19. Chapter 13 Principal component analysis - p. 263 20. Chapter 14 Factor analysis - p. 275 21. Chapter 15 Cluster analysis - p. 289 22. Chapter 16 Discriminant analysis - p. 303 23. Chapter 17 Structural equation model - p. 315 24. Chapter 18 Markov chain Monte Carlo - p. 337 25. Index - p. 347
