多元自适应回归样条方法在多维大数据岩土工程中的应用 仉文岗 著 大中专 文轩网

1 Introduction
1.1 Background
1.2 Objectives and Scope of This Book
1.3 Outline of This Book
References
2 A Review of Surrogate Models
2.1 Several Commonly Adopted Approaches
2.1.1 Response Surface Methods
2.1.2 Regression Analysis
2.1.3 Artificial Neural Networks
2.2 MARS Methodology
2.2.1 A Brief Introduction of MARS
2.2.2 MARS Algorithm
2.2.3 MARS Applications
2.3 Summary
References
3 Simple MARS Modeling Examples
3.1 Simple Function Approximation
3.2 Two-Dimensional Approximation
3.3 Function Approximation with Noise
3.4 Summary
Reference
4 MARS Use in Prediction of Collapse Potential for Compacted Softs
4.1 Background
4.2 The Database
4.3 The Developed MARS Models and Modeling Results
4.4 Model Interpretability
4.5 Parameter Relative Importance
4.6 Summary
References
5 MARS Use in Prediction of Diaphragm Wall Deflections in Soft Clays
5.1 Backgmund
5.2 The Database
5.3 The Developed MARS Models and Modeling Results
5.4 Model Interpretability
5.5 Parameter Relative Importance
5.6 Assessment of the Proposed MARS Model
5.7 Summary
References
6 MARS Use for Inverse Analysis of Soil and Wall Properties in Braced Excavation
6.1 Background
6.2 The Databases
6.3 The Developed MARS Eso/Cu Model and Parametric Sensitivity Analysis
6.4 Excavation History Validations on Eso/Cu Model
6.5 The Developed MARS In(S) Model and Parametric Sensitivity Analysis
6.6 Excavation History Validations on In(S) Model
6.7 Summary
References
7 MARS Use for Estimations of Lateral Wall Deflection Profiles
7.1 Background
7.2 The Databases
7.3 The Developed MARS Model and Modeling Results
7.4 Parametric Sensitivity Analysis
7.5 Excavation Case History Validations
7.6 Summary
References
8 MARS Use for Determination of EPB Tunnel-Related Maximum Surface Settlement
8.1 Background
8.2 Ground Conditions
8.3 Data Sets and the Influential Parameters
8.3.1 Tunnel Geometry
8.3.2 Geological Conditions
8.3.3 EPB Operation Factors
8.4 The Developed MARS Model and Modeling Results
8.5 Parametric Sensitivity Analyses
8.6 Summary
References
9 MARS Use in HP-Pile Drivability Assessment
9.1 Background
9.2 The Database and Performance Measures
9.3 The Developed MARS Models and Modeling Results
9.3.1 Pile Category Ql
9.3.2 Pile Categories Q2-Q5
9.4 Combined Data set Q1-Q5
9.4.1 Model Interpretability
9.4.2 Parameter Relative Importance
9.5 Summary
References
10 MARS Use in Estimation of Liquefaction-Induced Lateral Spreading
10.1 Background
10.2 The Database
10.3 The Developed MARS Model
10.4 Parametric Sensitivity Analysis and Model Interpretability
10.5 Design Charts
10.6 Discussions and Summary
References
11 MARS Use in Assessment of Soil Liquefaction Based on Capacity Energy Concept
11.1 Background
11.2 The Database
11.3 MARS Modeling Results
11.4 Parameter Relative Importance
11.5 The Developed MARS Model
11.6 Parametric Sensitivity Analysis
11.7 Validation from Case Histories
11.8 Performance Comparisons
11.9 Summary
References
12 MARS_LR Use in Assessment of Soil Liquefaction
12.1 Background
12.2 Modeling Accuracy
12.3 The Databases
12.3.1 Database 1
12.3.2 Database 2
12.3.3 Database 3
12.4 The Developed MARS_LR Models and Modeling Results . . .
12.4.1 MARS_LR Model 1
12.4.2 MARS_LR Model 2
12.4.3 MARS_LR Model 3
12.5 Summary
References
13 MARS Use in Evaluating Entry-Type Excavation Stability
13.1 Background
13.2 Critical Span Graph and the Database
13.3 The Developed MARS_LR Model and Modeling Results
13.4 Model Interpretability
13.5 Summary
References
14 Conclusions and Recommendations
14.1 Summary and Conclusions
14.1.1 Main Advantages of MARS
14.1.2 Applications of MARS Algorithm in Big Data Geotechnical Problems
14.1.3 MARS_LR for Pattern Recognition/Classification
14.2 Recommendations for Future Work
References
Appendix A: MATLAB BP Algorithm Adopted for the BPNN Models
Appendix B: MARS Algorithms Adopted for the Built Models Using MATLAB