Intro
About This Book
Contents
About the Authors
1 An Overview of Field Classifications to Evaluate Tunnel Boring Machine Performance
1.1 Introduction
1.2 Tunnel Boring Machine
1.2.1 Brief History of TBM
1.2.2 Types and Basic Principles of TBM
1.2.3 TBM Performance Parameters
1.2.4 Factors Influencing TBM Performance
1.3 TBM Prediction Field Classifications
1.4 TBM Performance Prediction Using Field Approach
1.5 RMCs Used in TBM Performance Prediction
1.6 Discussion and Conclusion
References

2 Empirical, Statistical, and Intelligent Techniques for TBM Performance Prediction
2.1 Introduction
2.2 Theoretical Models
2.2.1 Cutter Load Approach
2.2.2 Specific Energy Approach
2.3 Empirical Models
2.4 Statistical Approach
2.5 Computational-Based Techniques
2.6 Discussion and Conclusion
References
3 Developing Statistical Models for Solving Tunnel Boring Machine Performance Problem
3.1 Introduction
3.2 Regression-Based Models
3.2.1 Linear Multiple Regression (LMR)
3.2.2 Non-linear Multiple Regression (NLMR)
3.3 Case Study

3.4 Data Measurement and Input Variables
3.4.1 Rock Material Properties
3.4.2 Rock Mass Properties
3.4.3 Machine Characteristics
3.4.4 Input Variables
3.5 Regression-Based Models
3.5.1 Simple Regression
3.5.2 Multiple Regression
3.6 Discussion and Conclusion
References
4 A Comparative Study of Artificial Intelligence Techniques to Estimate TBM Performance in Various Weathering Zones
4.1 Introduction
4.2 Methodology
4.2.1 Artificial Neural Network (ANN)
4.2.2 Group Method of Data Handling (GMDH)
4.3 Tunnel Site and Data Collection

4.4 GMDH Model Development
4.5 Model Assessment and Discussion
4.6 Conclusions
References