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研究生： 吳宏興
Doddy - Prayogo
論文名稱： A Novel Genetic Algorithm-Based Evolutionary Support Vector Machine for Optimizing High-Performance Concrete
A Novel Genetic Algorithm-Based Evolutionary Support Vector Machine for Optimizing High-Performance Concrete
指導教授： 鄭明淵
Min-Yuan Cheng
口試委員: 楊亦東
I-Tung Yang
郭斯傑
Sy-Jye Guo
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： 2012
畢業學年度： 100
語文別： 英文
論文頁數： 111
外文關鍵詞： High-Performance Concrete, Genetic Algorithm
相關次數： 點閱：197  下載：0 
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    • Finding the effective method for optimizing the high-performance concrete mixture could deliver significant benefits to construction industry. However, traditional proportioning methods were not sufficient due to the expensive cost, limitation of use, and incompetence to deal with nonlinear relationship among components and concrete properties. Consequently, this research introduces a novel GA-based Evolutionary SVM (GA-ESIM) which combines together K-means Chaos Genetic Algorithm (KCGA) with Evolutionary SVM Inference Model (ESIM). This model benefits from both complex input-output mapping in ESIM and global solution with faster convergence characteristics in KCGA. Total 1,030 records from concrete strength experiments are provided to demonstrate the GA-ESIM application. According to the results, the new developed model successfully produces the optimum mixture with minimal prediction error. Furthermore, Graphic User Interface is utilized to help the users in performing optimization tasks.

    ABSTRACT i ACKNOWLEGDEMENT ii ABBREVIATIONS AND SYMBOLS vii LIST OF FIGURES x LIST OF TABLES xi 1. INTRODUCTION 1 1.1. Research motivation 1 1.2. Research objective 3 1.3. Research scope, assumptions and hypotheses 3 1.3.1. Research scope 3 1.3.2. Research assumptions and hypotheses 4 1.4. Research methodology 4 1.4.1 Problem formulation 7 1.4.2 Literature review 7 1.4.3 Model construction 8 1.4.4 Model validation and application 9 1.5. Study outline 9 2. LITERATURE REVIEW 11 2.1 High-Performance Concrete (HPC) mixture optimization 12 2.1.1 The significance of HPC mixture optimization 12 2.1.2 Research works on HPC mixture optimization 12 2.2 K-means Chaos Genetic Algorithm 14 2.2.1 Genetic Algorithm (GA) 14 2.2.2 Chaos mapping 21 2.2.3 K-means clustering 24 2.2.4 The Integration between chaos mapping with k-mean clustering in GA 26 2.3. Evolutionary Support Vector Machine Inference Model (ESIM) 28 2.3.1 Support Vector Machine (SVM) 28 2.3.2 Fast messy Genetic Algorithm (fmGA) 32 2.3.3 The Combination between SVM with fmGA 36 2.4. Cross validation 38 2.4.1 Basic concepts 38 2.4.2 Common types of cross validation 38 3. GENETIC ALGORITHM-BASED EVOLUTIONARY SUPPORT VECTOR (GA-ESIM) 42 3.1 Model architecture 42 3.2 Model adaptation process 43 4. CASE STUDY 48 4.1. Input data 48 4.2. Cross validation to verify ESIM training 50 4.3. ESIM Prediction model 50 4.4. The Optimization Process of GA-ESIM 54 5. INTEGRATING GA-ESIM WITH GRAPHIC USER INTERFACE (GUI) OF MATLAB 61 6. CONCLUSIONS AND RECOMMENDATIONS 64 6.1 Review the research purpose 64 6.2 Research accomplishments 64 6.3 Conclusions 65 6.4 Research Contributions 67 6.5 Future research works 68 REFERENCES 69 APPENDIX A (Matlab code of GA-ESIM) 75 A.1 Matlab code of GA-ESIM 75 A.2 Input HPC data (inputtrain.txt = inputtest.txt) 82

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