Evolutionary Fuzzy Decision Model for Construction Management using Weighted Support Vector Machine

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研究生：	Andreas Franskie Van Roy Andreas - Franskie Van Roy
論文名稱：	Evolutionary Fuzzy Decision Model for Construction Management using Weighted Support Vector Machine Evolutionary Fuzzy Decision Model for Construction Management using Weighted Support Vector Machine
指導教授：	鄭明淵 Min-Yuan Cheng
口試委員:	周瑞生 Jui-Sheng Chou 曾惠斌 Hui-Ping Tserng 王維志 Wei-Chih Wang 鄭道明 Tao-Ming Cheng
學位類別：	博士 Doctor
系所名稱：	工程學院 - 營建工程系 Department of Civil and Construction Engineering
論文出版年：	2010
畢業學年度：	98
語文別：	英文
論文頁數：	139
外文關鍵詞：	Fast messy genetic algorithms, weighted Support vector machine, Fuzzy logic, Construction management
相關次數：	點閱：253 下載：13
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Construction projects are, by their very nature, challenging; and project decision makers must work successfully within an environment that is frequently complex and fraught with uncertainty. As many decisions must be made intuitively based on limited information, successful decision making depends heavily on two factors, including the experience of the expert(s) involved and the quality of knowledge accumulated from previous experience. Knowledge, however, is subject to various factors that cause its value and accuracy to deteriorate. Research has demonstrated that artificial intelligence has the potential to overcome these factors.
The Evolutionary Fuzzy Support Vector Machine Inference Model (EFSIM), an artificial intelligence hybrid system that fuses together fuzzy logic (FL), weighted support vector machines (SVMs) and a fast messy genetic algorithm (fmGA), represents an alternative approach to retaining and utilizing experiential knowledge. In the EFSIM, FL handles imprecision in the environment and approximate reasoning; weighted SVMs act as a supervised learning tool to handle fuzzy input-output mapping focused on data characteristics; and fmGA is used as an optimization tool to search simultaneously for fittest membership functions, defuzzification parameter and weighted SVMs parameters (herein C,  and ).
The Evolutionary Fuzzy Support Vector Machine Inference System (EFSIS), in effect an automated EFSIM adaptation process, used one artificial and four real construction management problems to demonstrate the EFSIM as an effective and potential tool for solving various problems in construction management.

TABLE OF CONTENTS
ABSTRACT	i
ACKNOWLEDGEMENTS	iii
TABLE OF CONTENTS	iv
ABBREVIATIONS AND SYMBOLS	vii
LIST OF FIGURES	xiv
LIST OF TABLES	xvi

1. 	INTRODUCTION	1
1.1 Research Motivation	1
1.2 Research Objectives	5
1.3 Scope Definition	6
1.3.1 Boundary Identification	6
1.3.2 Research Hypotheses and Assumptions	7
1.4 Research Methodology	8
1.4.1 Problem Formulation	10
1.4.2 Literature Review	11
1.4.3 Model Construction	11
1.4.4 System Development	12
1.4.5 Assessment	13
1.5 Study Outline	13

2. 	FUZZY LOGIC (FL), WEIGHTED SUPPORT VECTOR MACHINES (WEIGTED SVMs), FAST MESSY GENETIC ALGORITHMS (fmGA), AND OBJECT ORIENTED SYSTEM DEVELOPMENT (OOSD)	15
2.1 Fuzzy Logic	16
2.1.1 Basic Concept	16
2.1.2 Application in Construction Management	19
2.1.2 Advantages and Disadvantages	20
2.2 Weighted Support Vector Machines (Weighted SVMs)	22
2.2.1 Basic Concept	22
2.2.2 Advantages and Disadvantages	25
2.3 Fast Messy Genetic Algorithms (fmGA)	27
2.3.1 Basic Concept	27
2.3.2 Advantages and Disadvantages	32
2.4 Object-Oriented System Development (OOSD)	33
2.4.1 Basic Concept	33
2.4.3 Advantages and Disadvantages	36

3. 	EVOLUTIONARY FUZZY SUPPORT VECTOR MACHINES INFERENCE MODEL	38
3.1 Model Architecture	38
3.2 Model Adaptation Pocess	40
3.2.1 Initialize Competitive Template	42
3.2.1.1 Use the SWRM to Encode MFs	44
3.2.1.2. Encode dfp, C,  and	
3.2.2 Initial Phase	46
3.2.2.1 Probabilistic Initialization	46
3.2.2.2. Evaluate Individuals	
3.2.3 Primordial Phase	52
3.2.3.1 Threshold Selection	53
3.2.3.2. Building Blocks Filtering	
3.2.4 Juxtapositional Phase	54
3.2.4.1 Cut and Splice	54
3.2.4.2. Mutation	
3.3 Model Application Process	55
3.4 Model Requirements and Limitations	58
3.5 Potential Application Areas	59

4. EVOLUTIONARY FUZZY SUPPORT VECTOR MACHINE INFERENCE SYSTEM	60
4.1 Object-Oriented System Development Process	60
4.1.1 Planning Phase	61
4.1.2 Building Phase	62
4.1.2.1 System Analysis	63
4.1.2.2 System Design	68
4.1.2.3 System Construction	73
4.1.2.4 System Testing	74
4.1.3 Deploying Phase	74
4.2 System Demonstration	78
4.2.1 System Main View and Case Problem Parameters Setting 	78
4.2.2 Result Files 	80

5. SYSTEM VALIDATION	83
5.1 Prepare for System Validation	84
5.1.1 Data Preprocessing	84
5.1.2 Configuration of Model Parameters	85
5.1.3 Performance Evaluation	86
5.2 Function Approximation	87
5.2.1 Problem Statement	87
5.2.2 Model Application	87
5.3 Conceptual Cost Estimates	88
5.3.1 Problem Statement	88
5.3.2 Model Application	89
5.4 Estimate at Completion for Construction Projects	93
5.4.1 Problem Statement	93
5.4.2 Model Application	95
5.5 Controlling Project Cash Flow	99
5.5.1 Problem Statement	99
5.5.2 Model Application	101
5.6 Prediction of Project Success	105
5.6.1 Problem Statement	105
5.6.2 Model Application	109
5.7 Discussion Implementation of EFSIM	121

6. CONCLUSIONS AND RECOMMENDATIONS	122
6.1 Review the Research Purpose	122
6.2 Research Accomplishment	123
6.3 Conclusions	124
6.4 Research Contributions	126
6.5 Future Research Direction and Recommendations	127

BIBLIOGRAPHY	129
APPENDIX A (Table of Fittest Membership Functions)	A-1
APPENDIX B (EFSIS Manual)	B-1
CURRICULUM VITAE

LIST OF FIGURES
Figure 1.1 	Research Flow Chart	8
Figure 1.2 	Detailed Research Flow Chart	9
Figure 2.1 	General Schema of Fuzzy Logic System (FLS).	17
Figure 2.2 	Underspecified messy chromosomes are evaluated by taking 
	the missing genes from the competitive template (Knjazew 2002)	29
Figure 2.3 	Cut–splice operator. (Feng et al. 2006)	29
Figure 2.4 	Mutation operator. (Feng et al. 2006)	30
Figure 2.5	The Incremental and Iterative Method (Ko, 2002)	35  
Figure 3.1 	EFSIM Architecture	40
Figure 3.2 	EFSIM Adaptation Process	41
Figure 3.3 	EFSIM Adaptation Structure	42
Figure 3.4 	EFSIM Chromosome Structure	43
Figure 3.5	Membership function: (a) trapezoidal; (b) triangular; (c) complete MF 
	set (Ko, 2002)	44
Figure 3.6 	Fuzzification process	50
Figure 3.7 	Illustration curve of the time function: (a) linear; (b) quadratic 
	(Lin and Wang, 2002)	51
Figure 3.8 	Cut–splice operators	54
Figure 3.9 	Mutation (Genotype)	55
Figure 3.10 	Model Application Process	56
Figure 3.11 	Potential Application Areas of the EFSIM in Construction 
	Management	59
Figure 4.1 	EFSIS Object-Oriented System Development Process	61
Figure 4.2 	System Usage Process	64
Figure 4.3 	System Concepts	66
Figure 4.4 	System Behaviors: (a) Sequence Diagram of Handle Record Use Case; (b) Sequence Diagram of Search Optimal Solution Use Case; 
	(c) Sequence Diagram of Infer Possible Results Use Case	67
Figure 4.5 	Two-Tiered Object-Oriented EFSIS Architecture	69
Figure 4.6 	Object Interactions: (a) Collaboration Diagram of Handle Record Use
 	Case; (b) Collaboration Diagram of Search Optimal Solution Use Case;
 	(c) Collaboration Diagram of Calculate Actual Output Use Case	70
Figure 4.7 	System Software Classes: (A) Class Diagram: Management Concept; 
	(B) Class Diagram: Adaptation Concept; (C) Class Diagram: Inference Concept	72
Figure 4.8 	System Application Process	77
Figure 4.9 	EFSIS Main View	78
Figure 4.10 	Examples of Input Case Problem Parameters and Characteristic	79
Figure 4.11 	Input Views for Number of Cases per Project	80
Figure 4.12 	Example of the Output Text File	81
Figure 4.13 	Example of the Optimal Text File	81
Figure 4.14 	Example of the Pred Text File	82
Figure 4.15 	Example of the Summwidth Text File	82
Figure 5.1 	Example of Three Sequential Period of ECF as One Set of 
	Historical Data	103
Figure 5.2 	Specific Model Application Process for Prediction of Project 
	Success.	109
Figure 5.3 	Average s-curves graphs for actual owner expenditures and 
	zone apportion of project outcomes degree	114
Figure 5.4 	The project outcome degree of project I.D. 233 until 50% completion	115

 

LIST OF TABLES

Table 3.1	Summary of EFSIM parameter settings	46
Table 4.1 	Type of Function in the System	62
Table 4.2 	High-Level Use Case of the EFSIS	65
Table 4.3 	System Operation Contrast	68
Table 5.1 	Model Parameters	86
Table 5.2 	Detailed Information on SVMs Implementation	86
Table 5.3 	Detailed Information on ESIM Implementation	87
Table 5.4 	Input and output pattern	88
Table 5.5 	Performance comparison of Function Approximation	88
Table 5.6	Patterns for Conceptual Estimating of Building Costs	91
Table 5.7 	Description of Qualitative Factors Involved in Conceptual 
	Cost Estimations	92
Table 5.8 	Generalization Comparison for Conceptual Estimating of Building 
	Cost	93
Table 5.9 	Description of 10 input factors and 1 output factor	96
Table 5.10 	Example of 24 Training Cases Taken from 1 Project	97
Table 5.11 	Data of 17 Testing Cases from 1 Project	97
Table 5.12 	Generalization Comparison for Estimate at Completion for 
	Construction Projects	98
Table 5.13 	Example of sequential ECF training data from 1 project (Liu, 2006)	103
Table 5.14 	Generalization Comparison for Project Cash Flow Problem	105
Table 5.15 	Description of 11 time-dependent factors with level of 
	significance below 0.10	112
Table 5.16 	Four Quantitative Values Associated to Degree of Project Outcomes	116 
Table 5.17 	Example learning and testing data on 50% completion data of project 
	I.D. 233 (11 time-dependent variables and 1 output)	117
Table 5.18 	RSME and the average error percentage comparison between 
	EFSIM, SVMs and ESIM on Three Learning Sets	119
Table 5.19 	Detail error percentage comparison on 50%, 67%, 90% completion	120
                                

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