物件導向演化式支持向量機推論模式於營建管理決策之研究

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研究生：	吳育偉 Yu-Wei Wu
論文名稱：	物件導向演化式支持向量機推論模式於營建管理決策之研究 Object-Oriented Evolutionary Support Vector Machine Inference Model (ESIM) for Decision-Making in Construction Management
指導教授：	鄭明淵 Min-Yuan Cheng
口試委員:	黃榮堯 none 卿建業 none 王維志 none 鄭道明 none 楊亦東 none 周瑞生 none
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：	2009
畢業學年度：	97
語文別：	英文
論文頁數：	129
中文關鍵詞：	營建管理決策、快速混雜基因演算法、支持向量機、物件導向
外文關鍵詞：	construction management, fast messy genetic algorithms, support vector machine, object-oriented
相關次數：	點閱：330 下載：17
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營建管理方面的問題具有複雜、不確定與隨環境變動的特性，因此，在解決相關問題時多仰賴該領域專家經驗與知識進行決策。本研究的主要目的為發展一最佳化決策模式，透過過去案例與經驗，學習歸納專家決策過程與分析邏輯，以提昇營建管理決策的有效性。而支持向量機（Support Vector Machine，SVM）與快速混雜基因演算法（Fast Messy GA，fmGA）在許多文獻中也已分別成功的應用在營建管理領域中解決各種不同的問題。支持向量機為結合統計學VC維度理論與結構風險最小化原理所發展之一種機械學習演算法。然而此模式須先決定其參數的數值，才能使模式的結果最佳化。
因此本研究目的在於提出一新的模式-「演化式支持向量機推論模式」（Evolutionary SVM Inference Model，ESIM）來解決營建管理中隨環境變動的複雜以及不確定的問題，此模式融合SVM 與fmGA的優點與特性，模式中SVM用於歸納輸入變數與輸出變數間複雜的關係；而fmGA搜尋SVM所需的最佳參數（C與γ），藉此提高SVM 的預測準確度。因此，本模式可藉由工程經驗學習累積，自動求得適應環境的最佳解。另外，本研究擬將「演化式支持向量機推論模式」與物件導向電腦技術相整合，發展「物件導向演化式支持向量機推論系統」（Evolutionary SVM Inference System，ESIS）。本研究所預計發展的ESIS，係以案例為基礎進行自我調適之電腦系統，此系統可避免傳統人工智慧系統中人為的主觀介入，此外，亦可大幅改善使用傳統技術尋找最佳系統參數所需耗費的大量時間與人力，本研究所預期發展的系統，可作為輔助決策者進行決策的智慧型決策支援系統，解決營建管理中，隨環境變動的各種複雜以及不確定的問題。

Problems in construction management are complex, full of uncertainty, and vary based on site environment. Two tools, the fast messy genetic algorithms (fmGA) and support vector machine (SVM) have been successfully applied to solve various problems in construction management. Considering the characteristics and merits of each, this paper combines the two to propose an Evolutionary Support Vector Machine Inference Model (ESIM).
In the ESIM, the SVM is primarily employed to address learning and curve fitting, while fmGA addresses optimization. This model was developed to achieve the fittest C and γ parameters with minimal prediction error. This research further integrates the developed ESIM with an object-oriented (OO) computer technique to create an Evolutionary Support Vector Machine Inference System (ESIS). Simulations conducted to demonstrate the robustness of the model in application indicate that ESIS may be used as a multifarious intelligent decision support system in decision-making to help solve a wide range of construction management problems.

TABLE OF CONTENTS
ABSTRACT	I
ABSTRACT (in Chinese)	II
ACKNOWLEDGEMENTS	IV
TABLE OF CONTENTS	V
ABBREVIATIONS AND SYMBOLS	VIII
LIST OF FIGURES	XIII
LIST OF TABLES	XV

1. INTRODUCTION	1
1.1 Research Motivation	1
1.2 Research Objectives	5
1.3 Scope Definition	5
1.3.1 Boundary Identification	5
1.3.2 Research Hypotheses and Assumptions	6
1.4 Research Methodology	7
1.4.1 Problem Formulation	9
1.4.2 Literature Review	9
1.4.3 Model Construction	10
1.4.4 System Development	11
1.4.5 Assessment	12
1.5 Study Outline	12
2. FAST MESSY GENETIC ALGORITHMS (fmGA), SUPPORT VECTOR MACHINES (SVM), AND OBJECT-ORIENTED SYSTEM DEVELOPMENT (OOSD)	14
2.1 Fast Messy Genetic Algorithms (fmGA)	14
2.1.1 Basic Concept	14
2.1.2 Advantages and Disadvantages	18
2.2 Support Vector Machines (SVMs)	19
2.2.1 Basic Concept	19
2.2.2 Advantages and Disadvantages	23
2.3 Object-Oriented System Development (OOSD)	24
2.3.1 Basic Concept	24
2.3.2 Advantages and Disadvantages	28
3. EVOLUTIONARY SUPPORT VECTOR MACHINES INFERENCE MODEL (ESIM)	30
3.1 Model Architecture	30
3.2 Adaptation Process for ESIM	33
3.2.1 Initialize Competitive Template	35
3.2.2 Initial Phase	36
3.2.2.1 Probabilistically Initialization	36
3.2.2.2 Evaluate Individual	37
3.2.3 Primordial Phase	43
3.2.3.1 Threshold Selection	43
3.2.3.2 Building Blocks Filter	43
3.2.4 Juxtapositional Phase	43
3.2.4.1 Cut and Splice	44
3.2.4.2 Mutation	44
3.3 Potential Application Areas	45
3.4 Model Application Process	46
3.5 Model Requirements and Limitations	49
4. Object-Oriented EVOLUTIONARY SUPPORT VECTOR MACHINES INFERENCE SYSTEM (OO-ESIS)	50
4.1 Object-Oriented System Development Process	50
4.1.1 Planning Phase	51
4.1.1.1 Definitions for System Requirements	51
4.1.2 Building Phase	52
4.1.2.1 System Analysis	52
4.1.2.2 System Design	58
4.1.2.3 System Construction	63
4.1.2.4 System Testing	64
4.1.3 Deploying Phase	64
4.2 System Demonstration	68
4.2.1 System Main Form	68
4.2.2 Management Module	68
4.2.3 Adaptation Module	70
4.2.4 Inference Module	71
5. MODEL VALIDATION	72
5.1 Preparations for Model Validation	73
5.1.1 Data Preprocessing	73
5.1.2 Configuration of Model Parameters	74
5.1.3 Performance Evaluation	74
5.2 Exclusive-Or (XOR)	75
5.2.1 Problem Statement	75
5.2.2 Model Application	75
5.3 Conceptual Estimating of Construction Cost	76
5.3.1 Problem Statement	76
5.3.2 Model Application	77
5.4 Performance Prediction of Subcontractor	82
5.4.1 Problem Statement	82
5.4.2 Model Application	84
5.5 Diaphragm Wall Deflection Prediction in Deep Excavations	90
5.5.1 Problem Statement	90
5.5.2 Model Application	92
5.6 Dynamic Prediction of Project Success	99
5.6.1 Problem Statement	99
5.6.2 Model Application	101
5.7 Discussion	111
5.7.1 Support Vector Machine Implementation	111
5.7.2 Evolutionary Support Vector Machines Inference Model Implementation	112
6. CONCLUSIONS AND RECOMMENDATIONS	113
6.1 Objective Revisited	113
6.2 Summary	113
6.3 Conclusions	115
6.4 Research Contributions	116
6.5 Recommendations and Future Directions	117
BIBLIOGRAPHY	119
VITA	128

LIST OF FIGURES
Figure 2.1 Evaluation of an underspecified messy chromosome	15
Figure 2.2 Cut–splice operator	16
Figure 2.3 Mutation operator	16
Figure 2.4 The SVM procedure	22
Figure 3.1 ESIM Architecture	31
Figure 3.2 ESIM Adaptation Process	33
Figure 3.3 ESIM Adaptation Structure	34
Figure 3.4 Initial Population	36
Figure 3.5 Cut–splice operator (Genotype)	44
Figure 3.6 Mutation (Genotype)	45
Figure 3.7 Potential ESIM Application Areas	46
Figure 3.8 Model Application Process	47
Figure 4.1 The ESIS Object-Oriented System Development Process	51
Figure 4.2 System Usage Process	54
Figure 4.3 System Concepts	56
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	57
Figure 4.5 Multi-Tiered Object-Oriented ESIS Architecture	60
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	61
Figure 4.7 System Software Classes: (A) Class Diagram: Management Concept; (B) Class Diagram: Adaptation Concept; (C) Class Diagram: Inference Concept	62
Figure 4.8 Database Schema	63
Figure 4.9 System Application Process	67
Figure 4.10 ESIS Initialization Screen	68
Figure 4.11 Manage Problems of ESIS	69
Figure 4.12 Manage Cases of ESIS	69
Figure 4.13 Manage Results of ESIS	69
Figure 4.14 Adaptation Module of ESIS	70
Figure 4.15 Support Vector Machines Parameters	70
Figure 4.16 Fast Messy Genetic Algorithm Parameters	71
Figure 4.17 Inference Module of ESIS	71
Figure 5.1 Representation of the Diaphragm Wall Structure	94
Figure 5.2 Measured vs. Predicted Maximum Diaphragm Wall Displacement.	97
Figure 5.3 Wall Deflection Predictions Using the Modified Process	99
Figure 5.4 CAPP Graphics for Cost of Change Orders	104



LIST OF TABLES
Table 4.1 System Functions of ESIM	52
Table 4.2 High-Level Use Cases	55
Table 4.3 System Operation Contrast	58
Table 5.1 Model Parameters of ESIM	74
Table 5.2 Detailed Information on SVM Implementation	75
Table 5.3 XOR Simulation Results	76
Table 5.4 Patterns for the Conceptual Estimation of Building Costs	79
Table 5.4 Patterns for the Conceptual Estimation of Building Costs (Continued)	80
Table 5.5 Description of Qualitative Factors Involved in Conceptual Cost Estimations	80
Table 5.6 Generalization Comparison for Conceptual Building Cost Estimation	82
Table 5.7 Influencing Factors for Performance Prediction of Subcontractor	85
Table 5.8 Subcontractor Historical Data	86
Table 5.8 (Continued) Subcontractor Historical Data	87
Table 5.8 (Continued) Subcontractor Historical Data	88
Table 5.9 Subcontractor Test Case	89
Table 5.10 Generalization Comparison for Subcontractor Performance Prediction	90
Table 5.11 Patterns for Diaphragm Wall Deflection Prediction of Deep Excavations	94
Table 5.12 Historical Excavation Projects in Metropolitan Taipei.	95
Table 5.13 Results of the Modified Process Applied to the New Excavation Project	98
Table 5.14 Project Assessment for Dynamic Prediction of Project Success	103
Table 5.15 Time-dependent factors identified by CAPP	104
Table 5.16 Patterns for Dynamic Prediction of Project Success (CAPPR Database Shown and Reused with CII’s Kind Permission)	105
Table 5.16 (Continued) Patterns for Dynamic Prediction of Project Success (CAPPR Database Shown and Reused with CII’s Kind Permission)	106
Table 5.17 Project Assessment for Dynamic Prediction of Project Success	107
Table 5.18 Results for Project Success Assessment without Prepared Data Clustering	108
Table 5.19 Results of K-means Clustering	109
Table 5.20 Comparisons of Performance Assessment Results for K-means Clustering	111

                                

Akintoye, A. (2000). “Analysis of factors influencing project cost estimating practice.” Construction Management and Economics, 18(1), 77-89.
Akintoye, A. and Skitmore, M. (1991). “Profitability of UK construction contractors.” Construction Management and Economics, 9(4), 311-325.
Albino,V. and Garavelli, A.C. (1998). “A neural network application to subcontractor rating in construction firms.” International Journal of Project Managemen, 16(1). 9-14.
Bent, J.A. (1978). “Scheduling and controlling construction subcontracts.” Transactions of the Annual Technical Conference, Miami Beach, FL, 51-72.
Booch, G. (1994a). “Coming of age in an object-oriented world.” IEEE Software, 11 (6), 33-41.
Booch, G. (1994b). Object-oriented analysis and design with applications. 2nd ed., Benjamin, Redwood City, California.
Booch, G., Rumbaugh, J., and Jacobson, I. (1999). The unified modeling language user guide. Addison-Wesley, Reading, Massachusetts.
Bradley, P. S., and Mangasarian, O. L. (2000). “Massive data discrimination via linear support vector machines.” Optimization Methods and Software, 13, 1-10.
Burges, C. (1998). “A tutorial on support vector machines for pattern recognition.” Data Mining and Knowledge Discovery, 2, 121-167.
Bush, V. G. (1973). Construction management: A handbook for contractors, architects, and students. Reston, Reston, Virginia, 1-6.
Caspers, J. (1994). Object-oriented programming: Analysis, design and implementation methods. 1st ed., Computer Technology Research Corp., Charleston, South Carolina.
Chan A.P.C., Scott D., Chan A.P.L. (2004). “Factors affecting the success of a construction project.” Journal of Construction Engineering and Management, 130 (1), 153-155.
Cheng, M. Y. and Ko, C. H. (2000a). “Safety evaluation and improvement suggestions for tower foundation: #1 Keelung to Peishyang and #3 Hsiehho to Shenmei.” Research Report, Taiwan Power Company, Taipei, Taiwan (in Chinese).
Cheng, M. Y. and Ko, C. H. (2000b). “Safety evaluation and improvement suggestions for tower foundation: #37 Hischi to Panchiao.” Research Report, Taiwan Power Company, Taipei, Taiwan (in Chinese).
Cheng, M. Y. and Ko, C. H. (2000c). “Safety evaluation and improvement suggestions for tower foundation: #76 Hischi to Panchiao.” Research Report, Taiwan Power Company, Taipei, Taiwan (in Chinese).
Cheng, T.M. and Feng, C.W. (2003). “An effective simulation mechanism for construction operations.” Automation in Construction, 12(3), 227-244.
Chi, S.Y., Chern, J.C. and Lin, C.C. (2001). “Optimized back-analysis for tunneling-induced ground movement using equivalent ground loss model.” Tunnelling and Underground Space Technology, 16 (3), 159-165.
Chua D.K.H., Loh P.K., Kog Y.C., Jaselskis E.J. (1997). “Neural networks for construction project success.” Expert Systems with Applications, 13(4), 317-328.
CII (1996). Predictive tools: Closing the performance gap. Research Summary, RS107-1, The Construction Industry Institute, Austin, Texas.
CIOB (1997). Code of Estimating Practice. 5th ed., The Chartered Institute of Building, Ascot, United Kingdom.
Coleman, D., Artim, J., Ohnjec, V., Rivas, E., Rumbaugh, J., and Wirfs-Brock, R. (1997). “UML: The Language of Software Blueprints?.” SIGPLAN Notices, 32(10), 201-205.
Clough, G. and Hansen, L.A. (1981). “Clay anisotropy and braced wall behavior.” Journal of the Geotechnical Engineering Division, 107 (7), 893-913.
Dahl, O., and Nygaard, K. (1966). “Simula: An algol-based simulation language.” Communications of the ACM, 9, 671-678.
Davis, D. (1996). Business research for decision making. 4th ed., Belmont, Duxbury Press, 4.
Drucker, H., Burges, C., Kaufman, L., Smola, A., and Vapnik, V. N. (1996). “Support vector regression machines.” Advances in Neural Information Processing Systems, 9, 155-161.
D’Souza, D. F., and Wills, A. C. (1999). Objects, components, and frameworks with UML: The catalysis approach. Addison-Wesley, Reading, Massachusetts.
Ekstrom, M.A., Bjornsson, H.C. and Nass, C.I. (2003). “Accounting for rater credibility when evaluating AEC subcontractors.” Construction Management and Economics, 21(2), 197-208.
El-Rayes, K. (2001). “Object-oriented model for repetitive construction scheduling.” Journal of Construction Engineering and Management, ASCE, 127(3), 199-205.
El-Rayes, K., Ramanathan, R., and Moselhi, O. (2002). “An object-oriented model for planning and control of housing construction.” Construction Management and Economics, 20(3), 201-210.
Fayad, M. E. (2000). “Introduction to the computing surveys’ electronic symposium on object-oriented application frameworks.” ACM Computing Surveys, 32(1), 1-9.
Feng, C. W. and Wu, H (2006). T. Integrating fmGA and CYCLONE to optimize the schedule of dispatching RMC trucks, Automation in Construction, 15(2), 186-199.
Fichman, R. G., and Kemerer, C. F. (1997). “Object technology and reuse: Lessons from early adopters.” Computer, 30(10), 47-59.
Firesmith, D. G. (1993). Object-oriented requirements analysis and logical design: A software engineering approach. Wiley, New York, New York, 16-19.
Fowler, M. and Scott, K. (2000). UML distilled: A brief guide to the standard object modeling language. 2nd ed., Addison-Wesley, Reading, Massachusetts.
Fukahori, K. and Kubota, Y. (2000). “Consistency evaluation of landscape design by a decision support system.” Computer-Aided Civil and Infrastructure Engineering, 15(5), 342-354.
Gen, M., and Cheng, R. (1997). Genetic algorithms and engineering design. Wiley, New York, New York.
Goldberg, D.E., Deb, K., and Krob, B. (1991). “Don’t worry, be messy.” Proceedings of the Forth International Conference on Genetic Algorithms and their Applications, San Diego, USA, 24-30.
Goldberg, D.E., Deb, K., Kaegupta, H., and Harik, G. (1994). “Rapid, accurate optimization of difficult problems using fast messy genetic algorithms.” Australian Electronics Engineering, 27(2), 56-64.
Gioda, G. and Sakurai, S. (1987), “Back analysis procedures for the interpretation of field measurements in geomechanics.” International Journal for Numerical and Analytical Methods in Geomechanics, 11 (6), 555-583.
Goldberg, D.T., Jaworski, W.E., and Gordon, M.D. (1976). “Lateral support systems and underpinning.” Design and construction, Federal Highway Administration Report, Volume 1, FHWA RD-75-128.
Griffith, A. F., Gibson, G. E., Jr., Hamilton, M. R., Tortora, A. L., and Wilson, C. T. (1999). “Project success index for capital facility construction projects.” Journal of Performance of Constructed Facilities, 13 (1), 39-45.
Hao, P. Y. and Chiang, J. H. (2007). “A Fuzzy Model of Support Vector Regression Machine.” International Journal of Fuzzy Systems, 9(1), 45-50.
Haykin, S. (1999). Neural networks: A comprehensive foundation, Prentice-Hall, Upper Saddle River, New Jersey.
Henderson-Sellers, B., Due, R., Graham, I., and Collins, G. (2000). “Third generation OO processes: A critique of RUP and OPEN from a project management perspective.” Proceedings of the Seventh Asia-Pacific Software Engineering Conference, IEEE, Piscataway, New Jersey, 428-435.
Holloway, C. A. (1979). Decision making under uncertainty: Models and choices, Englewood Cliffs, New Jersey, Prentice-Hall.
Hsieh, T. Y. (1998). “Impact of subcontracting on site productivity: Lessons learned in Taiwan.” Journal of Construction Engineering and Management, ASCE, 124(2), 91-100.
Hsieh, W. S. (2002). “Construction conceptual cost estimates using Evolutionary Fuzzy Neural Inference Model.” MS thesis, National Taiwan University of Science and Technology, Taipei, Taiwan (in Chinese).
Hsu, C. W., and Lin, C. J. (2002). “A simple decomposition method for support vector machine.” Machine Learning, 46(1-3), 291-314.
Hsu, C. W., Chang, C. C., and Lin, C. J. (2003). “A Practical Guide to Support Vector Classification.” Technical report, Department of Computer Science, National Taiwan University.
Huang, C. L., and Wang, C.J. (2006). “A GA-based feature selection and parameters optimization for support vector machines.” Expert Systems with Applications, 31(2), 231-240.
Huang, C.F. (1995), “A study on the vertical integration of general contractors and their subcontractors”, MS thesis, Department of Civil Engineering, National Central University, Taoyuan, Taiwan, (in Chinese).
Hughes, S. W., Tippett, D. D. and Thomas, W. K. (2004). “Measuring project success in the construction industry.” Engineering Management Journal, 16 (3), 31-37.
Ishigami, H., Fukuda, T., Shibata, T., and Arai, F. (1995). “Structure optimization of fuzzy neural network by genetic algorithm.” Fuzzy Sets and Systems, 71(3), 257-264.
Jacobson, I. (1987). “Object-oriented development in an industrial environment.” Proceedings of the Conference on Object-oriented Programming Systems, Languages and Applications, ACM, New York, New York, 183-191.
Jacobson, I. (2000). The Road to the Unified Software Development Process. Cambridge University Press, Cambridge, United Kingdom, 103－108.
Jacobson, I., Christerson, M., Jonsson, P., and Overgaard, G. (1992). Object-oriented software engineering. Addison-Wesley, Reading, Massachusetts.
Jan, J.C., Hung, S.L., Chi, S.Y., and Chern, J.C. (2002). “Neural network forecast model in deep excavation.” Journal of Computing in Civil Engineering, 16 (1), 59-65.
Jankowicz, D. (2001). “Why does subjectivity make us nervous? Making the tacit explicit.” Journal of Intellectual Capital, 2(1), 61-73.
Joachims, T. (2002), “Optimizing Search Engines Using Clickthrough Data.” Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), ACM.
Johnson, R. A. (2000). “The ups and downs of object-oriented systems development.” Communications of the ACM, 43(10), 68-73.
Kalakota, R., Rathnam, S., and Whinston, A. B. (1993). “The role of complexity in object-oriented systems development.” Proceeding of the Twenty-Sixth Hawaii International Conference on System Sciences, IEEE, Piscataway, New Jersey, 4, 759-768.
Karim, A., and Adeli, H. (1999a). “A new generation software for construction scheduling and management.” Engineering Construction and Architectural Management, 6(4), 380-390.
Karim, A., and Adeli, H. (1999b). “CONSCOM: An OO construction scheduling and change management system.” Journal of Construction Engineering and Management, ASCE, 125(5), 368-376.
Karim, A., and Adeli, H. (1999c). “OO information model for construction project management.” Journal of Construction Engineering and Management, ASCE, 125(5), 361-367.
Kale, S. and Arditi, D. (2001), “General contractors' relationships with subcontractors: a strategic asset.” Journal of Construction Management and Economics, 19(5), 541-549.
Kecman, V., and Hadzic, I. (2000). “Support vector selection by linear programming.” Process of IJCNN, 5, 193-198.
Keerthi, S. S., and Lin, C. J. (2003). “Asymptotic behaviors of support vector machines with Gaussian kernel.” Neural Computation, 15(7), 1667-1689.
Ko, C. H. (1999). “Computer-aided decision support system for disaster prevention of hillside residents.” MS thesis, National Taiwan University of Science and Technology, Taipei, Taiwan (in Chinese).
Ko, C. H. (2002). “Evolutionary Fuzzy Neural Inference Model (EFNIM) for Decision-Making in Construction Management.” PhD. thesis, National Taiwan University of Science and Technology, Taipei, Taiwan.
Kong, F., Wu, X., and Cai, L. (2008). “Application of RS-SVM in construction project cost forecasting.” International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008,
Kumaraswamy, M. M., and Matthews, J. D. (2000). “Improved subcontractor selection employing partnering principles.” Journal of Management in Engineering, ASCE, 16(3), 47-57.
Larman, C. (1998). Applying UML and patterns: An introduction to object-oriented analysis and design, Prentice Hall PTR, Upper Saddle River, New Jersey.
Lauesen, S. (1998). “Real-life object-oriented systems.” IEEE Software, 15(2), 76-83.
Lazzerini, B., Reyneri, L. M., and Chiaberge, M. (1999). “A neuro-fuzzy approach to hybrid intelligent control.” IEEE Transactions on Industry Applications, 35(2), 413-425.
Li, H. (1996). “Case-based reasoning for intelligent support of construction negotiation.” Information & Management, 30(5), 231-238.
Lin, C. Fu. (2004). “Fuzzy Support Vector Machines.” Ph.D. dissertation, Dept. of Electrical Engineering, National Taiwan University, Taipei, Taiwan.
Lin, H.-T., and Lin, C. J. (2003). “A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods.” Technical report, Department of Computer Science, National Taiwan University.
Long, M. (2001). “Database for retaining wall and ground movements due to deep excavations.” Journal of Geotechnical and Geoenvironmental Engineering, 127 (3), 203-224.
Martin, J. (1993). Principles of object-oriented analysis and design. Prentice-Hall, Englewood Cliffs, New Jersey.
Martin, J. and Odell, J. (1995). Object-oriented methods: A foundation. Prentice-Hall, Englewood Cliffs, New Jersey.
Martino, J. P. (1993). Technological Forecasting for Decision Making. 3rd ed., New York, McGraw-Hill, 251-252.
Michalewicz, Z. (1996). Genetic algorithms + data structures = evolution programs. 3rd ed., Springer-Verlag, New York, New York.
Minsky, M. and Papert, S. (1969). Perceptrons: An introduction to computational geometry. MIT Press, Cambridge, Massachusetts.
Navon, R., Shapira, A., and Shechori, Y. (2000). “Automated rebar constructability diagnosis.” Journal of Construction Engineering and Management, ASCE, 126(5), 389-397.
Nguyen L.D., Ogunlana S.O., Lan D.T.X. (2004). “A study on project success factors in large construction projects in Vietnam.” Engineering, Construction and Architectural Management, 11(6), 404-413.
Ou, C.Y. and Tang, Y.G., (1994). “Soil parameter determination for deep excavation analysis by optimization.” Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers, Series A/Chung-kuo Kung Ch'eng Hsuch K'an, 17 (5), 671-688. (In Chinese)
Parfitt M.K., Sanvido V.E. (1993). “Checklist of critical success factors for building projects.” Journal of Management in Engineering, 9 (3), 243-249.
Peck, R.B. (1969). “Deep excavations and tunneling in soft ground.” Proceeding 7th International Conference Soil Mechanics and Foundation Engineering, University Nacional Autonoma de Mexico Instituto de Ingenira, Mexico City, 225-290.
Pedroso, J. P., and Murata, N. (2001). “Support vector machines with different norms: motivation, formulations and results.” Pattern recognition Letters, 22, 1263-1272.
Pittman, M. (1993). “Lesson learned in managing object-oriented development.” IEEE Software, 10(1), 43-53.
Powrie, W. and Li, E.S.F. (1991). “Finite element analysis of an in situ wall propped at formation level.” Geotechnique, 41 (4), 499-514.
Quatrani, T. (1998). Visual modeling with rational rose and UML. Addison-Wesley, Reading, Massachusetts.
Ramirez, R.R and Alarcon, L.F.C. (2004). “P. Knights, Benchmarking system for evaluating management practices in the construction industry.” Journal of Management in Engineering, ASCE 20(3), 110-117.
Rumbaugh, J., Jacobson, I., and Booch, G. (1999). The unified modeling language reference manual. Addison-Wesley, Reading, Massachusetts.
Russell, J. S., Jaselskis, E. J., Lawrence, S. P., Tserng, H. P., and Prestine, M. T. (1996). “Development of a predictive tool for continuous assessment of project performance.” Research Report, RR107-11, The Construction Industry Institute, Austin, Texas.
Russell, J. S., Jaselskis, E. J., and Lawrence, S. P. (1997). “Continuous Assessment of Project Performance.” Journal of Construction Engineering and Management, ASCE, 123(1), 64-71.
Sanvido, V., Grobler, F., Parfitt, K., Guvenis, M., and Coyle, M. (1992). “Critical success factors for construction projects.” Journal of Construction Engineering and Management, 118 (1), 94-111.
Satzinger, J. W., Jackson, R. B., and Burd, S. D. (2000). System Analysis and Design in a Changing World. Course Technology, Cambridge, Massachusetts.
Schaufelberger, J.E. (2003). “Causes of subcontractor business failure and strategies to prevent failure, Proceedings of the Construction Research Congress.” Honolulu, HI, 593-599.
Sergio Maturana, Luis Fernando Alarcon, Pedro Gazmuri, and Mladen Vrsalovic. (2007). “On-Site Subcontractor Evaluation Method Based on Lean Principles and Partnering Practices.” Journal of Management in Engineering, ASCE 23 (2), 67-74.
Shawetaylor J. and Cristianini, N. (2004). “Kernel methods for pattern analysis.” Cambridge.
Smola, A., Scholkopf, B. and Muller, K. R. (1998). “The connection between regularization operations and support vector kernels.” Neural network, 11, 637-649.
Steinwart, I. (2002). “Support vector machines are universally consistent.” Journal of. Complexity, 18, 768-791.
Sundin, S., and Braban-Ledoux, C. (2001). “Artificial intelligence-based decision support technologies in pavement management.” Computer-Aided Civil and Infrastructure Engineering, 16(2), 143-157.
Systa, T. (2000). “Incremental construction of dynamic models for object-oriented software systems.” Journal of Object Oriented Programming, 13(5), 18-27.
Tah, J. H. M., and Carr, V. (2000). “Information modelling for a construction project risk management system.” Engineering Construction and Architectural Management, 7(2), 107-119.
Tam, C. M., Tong, T. K. L., Leung, A. W. T., and Chiu, G. W. C. (2002). “Site layout planning using nonstructural fuzzy decision support system.” Journal of Construction Engineering and Management, ASCE, 128(3), 220-231.
Tommelein, I. D., Levitt, R. E., and Hayes-Roth, B. (1992). “Site-layout modeling: How can artificial intelligence help?.” Journal of Construction Engineering and Management, ASCE, 118(3), 594-611.
Vapnik,V. N. (1995). “The nature of statistical learning theory.” New York: Springer.
Vadaparty, K. (2000). “UML & beyond- use cases-basics.” Journal of Object Oriented Programming, 12(9), 4-8.
Wang, D., Yung, K.L., and Ip, W.H. (2001), “A heuristic genetic algorithm for subcontractor selection in a global manufacturing environment,” IEEE Transactions on Systems, Man and Cybernetics. Part C, Applications and Reviews 31(2), 189-198.
Whittle, A.J., Hashash, Y.M.A., and Whitman, R.V., (1993). “Analysis of deep excavation in Boston.” Journal of Geotechnical Engineering, 119 (1), 69-90.
Wu, T. K. (2001). “Performance evaluation and prediction model for construction subcontractor.” MS thesis, National Taiwan University of Science and Technology, Taipei, Taiwan (in Chinese).
Yang, J. B. (1997). “An integrated knowledge acquisition and problem solving model for experience-oriented problems in construction management.” PhD thesis, National Central University, Chungli, Taiwan.
Yang, J. B., and Yau, N. J. (2000). “Integrating case-based reasoning and expert system techniques for solving experience-oriented problems.” Journal of the Chinese Institute of Engineers, 23(1), 83－95.
Zhang, T. (2004). “Statistical behavior and consistency of classification methods based on convex risk minimization.” Annals of Statistics, 32, 56-85.
Zhong, S. (1992). “The analysis of object-oriented model for cost estimating system.” MS thesis, National Taiwan University, Taipei, Taiwan (in Chinese).

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