Predicting Tensile Loads in Reinforcement for Geosynthetic-Reinforced Soil Structures by Evolutionary Metaheuristic Intelligence Model

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研究生：	朱文漢 JUSIEANDRA - PRIBADI PAMPANG
論文名稱：	Predicting Tensile Loads in Reinforcement for Geosynthetic-Reinforced Soil Structures by Evolutionary Metaheuristic Intelligence Model Predicting Tensile Loads in Reinforcement for Geosynthetic-Reinforced Soil Structures by Evolutionary Metaheuristic Intelligence Model
指導教授：	周瑞生 Jui-Sheng Chou
口試委員:	楊立人 Li-Ren Yang 葛宇甯 Yu-Ning Ge 周建成 Chien-Cheng Chou 楊國鑫 Kuo-Hsin Yang
學位類別：	碩士 Master
系所名稱：	工程學院 - 營建工程系 Department of Civil and Construction Engineering
論文出版年：	2014
畢業學年度：	102
語文別：	英文
論文頁數：	181
中文關鍵詞：	Reinforcement loads 、geosynthetic reinforced soil structures 、machine learning 、optimization 、evolutionary metaheuristic intelligence.
外文關鍵詞：	Reinforcement loads, geosynthetic reinforced soil structures, machine learning, optimization, evolutionary metaheuristic intelligence.
相關次數：	點閱：251 下載：3
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上一筆

Accurately estimating reinforcement tensile loads is crucial for evaluating the internal stabilities of geosynthetic-reinforced soil (GRS) structures. This study developed an evolutionary metaheuristic intelligence model for efficiently and accurately estimating reinforcement loads. The proposed model improves the prediction capability of the firefly algorithm (FA) by integrating intelligent components, namely chaotic maps, adaptive inertia weight, and Lévy flight. The enhanced FA is then used to optimize the hyperparameters for support vector regression (SVR) model. The proposed model was validated using a database of 15 wall case studies (totally 94 data points) via cross-validation algorithm and hypothesis test. The method was then compared with conventional prediction methods in terms of accuracy in predicting reinforcement tensile loads in GRS structures. The validation results showed that the proposed model had superior accuracy and mean absolute percentage errors below 7%. Moreover, comparison with the baseline models and prior literature indicated that the evolutionary metaheuristic intelligence model had a significant improvement of 26.15% to 96.90% in root mean square errors. This study validates the effectiveness of the proposed model of reinforcement tensile loads and its feasibility for facilitating early designs of construction structures.

TABLE OF CONTENTS
ABSTRACT	i
ACKNOWLEDGEMENTS	ii
LIST OF FIGURES	vii
LIST OF TABLES	viii
ABBREVIATIONS AND SYMBOLS	ix
CHAPTER 1 INTRODUCTION	1
1.1. Research background	1
1.2. Research objectives	5
1.3. Research process	6
CHAPTER 2 LITERATURE REVIEW	7
2.1. Current practice in prediction of reinforcement tension loads	7
2.2. Data mining and artificial intelligence-based approaches	8
2.3. Hybrid computational model	9
CHAPTER 3 METHODOLOGY	11
3.1. Regression-based models	11
3.1.1. Classification and regression tree	11
3.1.2. Generalized linear regression	12
3.1.3. Support vector regression	12
3.1.4. Ensemble regression models	16
3.2. Evolutionary metaheuristic regression model	17
3.2.1. Firefly algorithm	17
3.2.2. Smart components	19
3.2.2.1. Chaotic maps	19
3.2.2.2. Adaptive inertia weight	19
3.2.2.3. Lévy flights	20
3.2.2.4. Combination of smart components with firefly algorithm	21
3.2.2.5. Benchmark functions	23
3.2.3. Smart firefly algorithm in optimization of support vector regression	28
3.3. Prediction performance and evaluation methods	31
3.3.1. Cross fold validation	31
3.3.2. Performance measures	31
3.3.3. Hypothesis testing	33
CHAPTER 4 DATA ANALYSIS AND RESULTS	34
4.1. Data description and pre-processing	34
4.2. Model construction	34
4.3. Comparisons of models and conventional methods	40
4.3.1. Analytical results	40
4.3.2. Discussion	44
CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS	49
5.1. Conclusions	49
5.2. Future research and recommendations	50
REFERENCES	51
APPENDIX A. Analytical results of benchmark functions	64
APPENDIX B. Original dataset from literature.	73
APPENDIX C. Performance measure via cross-fold for dataset using baseline AI models and ensemble models – validation dataset.	75
APPENDIX D. Performance measure via cross-fold for original data using SFA-SVR model.	76
APPENDIX E. Performance measure via cross-fold for dataset with 8 new sample data using baseline AI models and ensemble models – test dataset.	77
APPENDIX F. Performance measure via cross-fold for dataset with 8 new sample data using SFA-SVR.	78
APPENDIX G. Typical modeling in IBM SPSS Modeler (Clementine 12.0) via cross-fold.	79
APPENDIX H. The generation of fireflies in 10-cross fold of SFA-SVR for 8 new datapoints.	80
APPENDIX I. Interface of SFA-SVR algorithm analysis process in MATLAB.	81
APPENDIX J. MATLAB Code.	82
APPENDIX K. Tutorial PPT	99

LIST OF FIGURES
Figure 1 1. Design model of GRS structure.	2
Figure 3 1. Architecture of a regression machine constructed by support vector algorithm.	14
Figure 3 2. The contour of bukin function No. 6.	26
Figure 3 3. Framework of the SFA-SVR model.	30
Figure 3 4. Ten-fold cross-validation method.	32
Figure 4 1. Flowchart of modeling stream for baseline models.	37
Figure 4 2. The SFA-SVR schemes and partial algorithms.	38
Figure 4 3. Performance comparison of baseline and ensemble models.	43
Figure 4 4. Cross section of GRS test wall.	45
Figure 4 5. Performance comparison of the proposed models and prior prediction methods in predicting Tmax at reinforcement layer 3 based on 8 new data samples.	48

LIST OF TABLES
Table 3 1. Numerical benchmark functions	23
Table 3 2. Hypothesis testing results of optimum for the SFA model.	28
Table 4 1. Sources of datasets reported in the literature.	35
Table 4 2. Statistical attributes of GRS dataset.	36
Table 4 3. Default parameter settings in baseline models.	36
Table 4 4. Parameters settings for SFA-SVR algorithm.	40
Table 4 5. Summary of cross-fold modeling performance for baseline, ensemble and proposed models.	41
Table 4 6. Optimal values of C and σ for ten folds.	43
Table 4 7. The 8 new sample datasets from GRS test wall.	44
Table 4 8. Hypothesis testing for prediction models improvement in 8 new data points.	47

                                

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