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作者姓名(中文):楊立廷
作者姓名(英文):Mlamuli Bafanatoti Manyatsi
論文名稱(中文):Investigating the Applicability of Underground Excavation Indices to the Prediction of Tunneling-Induced Surface Settlement Using Artificial Intelligence
論文名稱(外文):Investigating the Applicability of Underground Excavation Indices to the Prediction of Tunneling-Induced Surface Settlement Using Artificial Intelligence
指導教授姓名(中文):陳堯中
指導教授姓名(英文):Yao-Chung Chen
口試委員姓名(中文):陳立憲
呂守陞
徐國偉
口試委員姓名(英文):Li-Hsien Chen
Sou-Sen Leu
Guo-Wei Xu
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:營建工程系
學號:M10605802
出版年(民國):108
畢業學年度:107
學期:2
語文別:英文
論文頁數:168
中文關鍵詞:TunnelingIndicesArtificial IntelligenceSettlement
外文關鍵詞:TunnelingIndicesArtificial IntelligenceSettlement
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The issue of settlement poses a big threat to underground excavation, especially in urban areas where the smallest of ground movements has the potential to lead to a catastrophic disaster. It is therefore crucial to understand the factors that play a role in causing surface settlement during the excavation process. Numerous approaches including empirical, numerical and analytical have already been established to map the relationships that exist between tunneling and ground deformation. It has also been established in previous studies that surface settlement is not caused by one factor or the other but is more of a combination of factors. Artificial Intelligence methods (AI) have the ability to relate several parameters to surface settlement quickly and efficiently given the proper skills and understanding. In this study, dimensional analysis is used to normalize the factors that affect surface settlement and then the influence of these normalized excavation factors on the prediction of surface settlement is investigated using AI. Three sets of analyses are run on RapidMiner Studio v9.2 using four different algorithms, decision trees, artificial neural networks, random forests and support vector machines. The first set of analyses were done with no indices, the second included the thrust index (Th/(D^2 q_u )) and the last set included the depth index (H/D). The results of the predictions are then compared to the field measurements to see if including these indices in the analysis benefits the prediction or not. The study shows that the thrust index does in fact improve the prediction accuracy while the influence of the depth index in inconclusive.
The issue of settlement poses a big threat to underground excavation, especially in urban areas where the smallest of ground movements has the potential to lead to a catastrophic disaster. It is therefore crucial to understand the factors that play a role in causing surface settlement during the excavation process. Numerous approaches including empirical, numerical and analytical have already been established to map the relationships that exist between tunneling and ground deformation. It has also been established in previous studies that surface settlement is not caused by one factor or the other but is more of a combination of factors. Artificial Intelligence methods (AI) have the ability to relate several parameters to surface settlement quickly and efficiently given the proper skills and understanding. In this study, dimensional analysis is used to normalize the factors that affect surface settlement and then the influence of these normalized excavation factors on the prediction of surface settlement is investigated using AI. Three sets of analyses are run on RapidMiner Studio v9.2 using four different algorithms, decision trees, artificial neural networks, random forests and support vector machines. The first set of analyses were done with no indices, the second included the thrust index (Th/(D^2 q_u )) and the last set included the depth index (H/D). The results of the predictions are then compared to the field measurements to see if including these indices in the analysis benefits the prediction or not. The study shows that the thrust index does in fact improve the prediction accuracy while the influence of the depth index in inconclusive.
ABSTRACT I
AKNOWLEDGEMENTS II
LIST OF TABLES VI
LIST OF FIGURES VIII
ABBREVIATIONS & SYMBOLS XI
CHAPTER 1: INTRODUCTION 1
1.1 Motivation and Purpose 1
1.2 Research Method and Scope 2
1.3 Summary and Outline 3
CHAPTER 2: LITERATURE REVIEW 5
2.1 Underground Mechanized Excavation Methods 5
2.1.1 Shield Tunneling 5
2.1.2 Tunnel Boring Machine 7
2.1.3 Pipe Jacking 8
2.1.4 Differences Between Tunneling Methods 10
2.2 Generalized Mechanical Excavation 11
2.2.1 Cutting Mechanism I: Single Cutter Damage at Contact Field 15
2.2.2 Cutting Mechanism II: Double Cutter Optimum Spacing 17
2.2.3 Cutting Mechanism III: Overall Cutter Head Configuration 19
2.3 Generalized Indentation Formula 20
2.3.1 Evolution of Indentation Damage 20
2.3.2 Theoretical Solution to Indentation Damage 21
2.3.3 Effect of Cutter Wearing 23
2.4 Mechanized Underground Excavation Indices 24
2.4.1 Introduction to Dimensional Analysis 28
2.4.2 Relationships Among Quantities 29
2.4.3 Conversion into Dimensionless Parameters 31
2.4.4 Application of Dimensionless Analysis to Mechanical Cutting 33
2.5 Factors Affecting Surface Settlements 34
2.6 Current Assessment of Settlement in Underground Excavation 38
2.6.1 Empirical Methods 39
2.6.2 Numerical Methods 45
2.6.3 Artificial Neural Networks 46
2.6.4 Decision Trees 49
2.6.5 Support Vector Machine 50
2.6.6 Random Forest 52
CHAPTER 3: METHODOLOGY 54
3.1 Development of Mechanized Excavation Indices 54
3.1.1 The Physics of Mechanical Excavation and Settlement 54
3.1.2 Dimensional Analysis and Establishing Indices 55
3.2 Data Preparation 58
3.2.1 Feature Selection 61
3.2.2 Dummy Variable Encoding 63
3.3 Estimation of Jacking Forces in Micro-tunneling Operations 64
3.4 Artificial Intelligence 67
3.4.1 Software 67
3.4.2 Analysis Methodology 68
CHAPTER 4: ESTIMATING MAXIMUM SURFACE SETTLEMENT: CASE STUDY 70
4.1 Project Description 70
4.1.1 Geology 72
4.1.2 Machine Description 73
4.1.3 Settlement Monitoring 75
4.1.4 Data Handling 75
4.2 Artificial Intelligence Methods 79
4.2.1 Artificial Neural Networks 79
4.2.2 Decision Trees 87
4.2.3 Support Vector Machines 92
4.2.4 Random Forest 98
4.3 Evaluation and Comparison 103
4.3.1 Manually Determined Split Ratio 104
4.3.2 Cross Validation 108
4.3.3 Significance and Sensitivity Analysis 111
CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS 116
5.1 Conclusions 116
5.2 Recommendations 117
REFERENCES 119
APPENDICES i
Appendix I: Oral Defense Questions and Revisions i
Appendix II: Spatial Description of Boreholes ii
Appendix III: Detailed Coring Results x
Appendix IV: Pictures of Excavation Machine xxx
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全文檔公開日期:2019/08/20 (本校及校內區域網路)
全文檔公開日期:不公開 (校外網際網路)
全文檔公開日期:不公開 (國家圖書館:臺灣博碩士論文系統)
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