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研究生：	田華勳 Tien, Hau-Hsun
論文名稱：	類神經網路應用於曼谷地層深開挖連續壁變形之預測 Prediction of Diaphragm Wall Deflection in Deep Excavation of Bangkok Subsoil Using Artificial Neural Networks
指導教授：	常正之 Charng, Jeng-Jy
學位類別：	碩士 Master
系所名稱：	工學院 - 土木工程學系 Department of Civil Engineering
論文出版年：	2004
畢業學年度：	92
語文別：	英文
論文頁數：	183
中文關鍵詞：	類神經網路、深開挖
外文關鍵詞：	Artificial Neural Networks, Deep Excavation
相關次數：	點閱：156 下載：8
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　　連續壁側向變形是深開挖中的重要現場量測值。運用此監測值評估建造績效以避免對鄰近結構物所帶來的支撐系統破壞或是損害。儘管對許多建造方案和預測方法做了嘗試, 卻沒有一個方法能夠準確地預測由於跟連續壁側向變形有關的複雜大地工程與建造因素所造成的建造績效。本論文主要利用監督式倒傳遞類神經網路來預測泰國曼谷市區中的連續壁側向變形。此外, 我們採用前幾階量測的連續壁側向變形當作類神經網路的輸入值。經過充分地降低那些常常是極度波動且難以估定的土壤參數之重要性。模擬結果顯示類神經網路可以合理地預測連續壁最大側向變形的大小及位置。
　　最後, 我們利用相對重要性分析來詳述如何透過分割彼此相連結之權重去決定不同輸入參數的相對重要性。相對重要性分析的結果顯示等值SPT-N值是預測中最重要的影響參數。

　　Lateral wall deflection is an important field measurement in deep excavation. The monitoring is applied to evaluate construction performance to avoid a supporting system failure or damages incurred to adjacent structures. Despite the numerous case tries of construction projects and several forecasting method, no method accurately forecasts the performance of construction due to complicated geotechnical and construction factors affecting the behavior of the lateral wall deflection. This work predicts the lateral wall deflection in Bangkok metropolitan by using back-propagation supervised neural network. In addition, the knowledge representation adopts measured lateral wall deflection of previous stages as inputs to the network. Doing so substantially reduces the importance of soil parameters, which are often extremely fluctuating and difficult to assess. Simulation results indicate that the artificial neural network can reasonably predict the magnitude, as well as location, of maximum deflection of lateral wall deflection.
　　Finally, relative importance analysis we used details the procedure for partitioning the connection weights to determine the relative importance of the various inputs. The results of relative importance analysis indicated that the equivalent SPT-N value, , is the most important factor for the prediction.

Chapter	Title	                                                Page
		
	Abstract	                                           I
	Acknowledgment	                                         III
	Table of Contents	                                  IV
	List of Tables	                                         VII
	List of Figures	                                        VIII
	List of Symbols	                                          XI

I   	Introduction	
1   General	                                           1
2   Scope and Objective of Study	                   2
2.1    Scope of Study	                                   2
2.2    Objective of Study	                           2
3   Methodology	                                   2
		
	Tables and Figures (Chapter 1)	                           4
		
II	Literature Review	
1   Diaphragm Walls and Construction Sequence            6
1.1    Top-Down Construction Method	                   6
1.2    Bottom-Up Construction Method	                   7
2   Deep Excavation	                                   7
2.1    Influencing Factors in Excavation Behavior	   8
2.2    Lateral Wall Movements	                           9
2.2.1     Effect of Wall Stiffness	                  11
2.2.2     Effect of Depth of Unsupported Excavation	  12
2.2.3     Effect of Support Stiffness	                  13
2.2.4     Effect of Wall Embedment Depth	          13
2.3    Ground Surface Settlement	                  14
2.4    Supporting System	                          15
3   Engineering Properties of Bangkok Subsoil Conditions16
3.1    General	                                  16
3.2    Typical Bangkok Subsoil Profile	          17
3.3    Construction Condition	                          17
3.4    Investigations of Bangkok Braced Excavations	  18
3.5    Projects Analysis	                          19
4   Artificial Neural Network (ANN)	                  22
4.1    Theoretical Background	                          22
4.1.1     Definition	                                  22
4.1.2     The Biological Neuron	                  22
4.1.3     The Artificial Neuron	                  23
4.1.4     Design	                                  23
4.1.5     Layers	                                  23
4.1.6     Inter- Layer Connections	                  24
4.1.7     Intra-Layer Connections	                  25
4.1.8     Learning	                                  26
4.1.9     Learning Laws	                          26
4.1.10    Function	                                  28
4.2    Applications of ANN on Geotechnical Engineering  28
4.2.1     Slope Movements	                          28
4.2.2     Pile Foundation	                          29
4.2.3     Liquefaction Potential	                  32
4.2.4     Material Behavior	                          34
4.2.5     Deep Excavation	                          36
4.2.6     Others	                                  37
4.3    MALAB Program	                                  39
4.3.1     Introduction	                          39
4.3.2     Back-Propagation	                          40
4.3.3     Back-Propagation Algorithm	                  43
4.3.4     Conjugate Gradient Algorithms	          46
		
	Tables and Figures (Chapter 2)	                          51
		
III	Methodology	
1   General	                                          78
2   Data Collection	                                  78
3   Categorization of Data	                          79
3.1    Training Data Set	                          79
3.2    Validation Data Set	                          80
3.3    Test Data Set	                                  80
3.4    Summary	                                  80
4   Preprocessing of Data	                          80
5   Training Processing	                          81
5.1    Learning Algorithm Processes	                  81
5.2    Feed-forward	                                  82
5.2.1     Summation Function	                          82
5.2.2     Transfer Function	                          83
5.3    Error-Feedback	                                  84
5.3.1     Delta Rule	                                  84
5.3.2     Average Sum Squared Error	                  84
5.3.3     Adjust Weights Process	                  85
6   Optimal Neuron Number in Hidden Layer	          87
7   Simulation Processing	                          87
8   Relative Importance Analysis	                  88
		
	Tables and Figures (Chapter 3)	                          90
		
IV	Results and Discussions	
1   Optimal ANN Framework	                          97
1.1    Hidden Layer Size	                          97
1.2    Learning Rate	                                  98
1.3    Establishment and Training Parameters of         98
                 ANN Model	
1.4    Training Results of Various Excavation Stage	  99
2   Comparisons between ANN Prediction and Field        99
              Observation of Lateral Wall Movement	
2.1    First Excavation Stage	                          99
2.2    Second Excavation Stage	                 100
2.3    Third Excavation Stage	                         100
2.4    Summary	                                 101
3   Relative Importance Analysis	                 102
		
	Tables and Figures (Chapter 4)	                         104
		
V	Conclusions and Recommendations	
1   Conclusions	                                 128
2   Recommendations	                                 129
		
	Appendixes A	                                         130
	Appendixes B	                                         154
	References	                                         176
                                    

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