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研究生:黃日德
研究生(外文):Nhat-Duc Hoang
論文名稱:Decision Support System for Construction Management Based on Evolutionary Least Squares Support Vector Machine
論文名稱(外文):Decision Support System for Construction Management Based on Evolutionary Least Squares Support Vector Machine
指導教授:鄭明淵鄭明淵引用關係
指導教授(外文):Min-Yuan Cheng
口試委員:鄭明淵
口試委員(外文):Min-Yuan Cheng
口試日期:2013-07-02
學位類別:博士
校院名稱:國立臺灣科技大學
系所名稱:營建工程系
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:英文
論文頁數:179
中文關鍵詞:Construction ManagementArtificial IntelligenceDecision Support SystemLeast Squares Support Vector MachineDifferential EvolutionAdaptive Time FunctionFuzzy Logic
外文關鍵詞:Construction ManagementArtificial IntelligenceDecision Support SystemLeast Squares Support Vector MachineDifferential EvolutionAdaptive Time FunctionFuzzy Logic
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Problems in the field of construction management are sophisticated, highly uncertain, and context-dependent. Thus, the application of artificial intelligence (AI) to tackle such problems can be a promising research direction. Considering the features and advantages of each AI technique, this research integrates various prevalent advanced approaches to establish a novel decision support system that utilizes Least Squares Support Vector Machine (LS-SVM), Differential Evolution (DE), Adaptive Time Function (ATF), and Fuzzy Logic (FL). At the first stage, LS-SVM is incorporated with DE to create Evolutionary Least Squares Support Vector Machine Inference System (ELSIS) in which LS-SVM is utilized as a supervised learning method used for regression analysis/ classification in high dimensional space and Differential Evolution is employed to identify the optimal set of tuning parameters. At the second stage, ATF is integrated into ELSIS to establish Adaptive Time-Dependent Evolutionary Least Squares Support Vector Machine Inference System (ELSIST). In ELSIST, ATF is deployed to deal with the unbalanced nature of time series data. At the final stage, ELSIS incorporates FL to develop Evolutionary Fuzzy Least Squares Support Vector Machine Inference System (EFLSIS) in which FL aims at facilitating the system capability of approximate reasoning and coping with vague information. Experimental results obtained from system applications demonstrate that the newly established inference system can be a highly beneficial for decision-makers when solving various problems in the field of construction management.
Problems in the field of construction management are sophisticated, highly uncertain, and context-dependent. Thus, the application of artificial intelligence (AI) to tackle such problems can be a promising research direction. Considering the features and advantages of each AI technique, this research integrates various prevalent advanced approaches to establish a novel decision support system that utilizes Least Squares Support Vector Machine (LS-SVM), Differential Evolution (DE), Adaptive Time Function (ATF), and Fuzzy Logic (FL). At the first stage, LS-SVM is incorporated with DE to create Evolutionary Least Squares Support Vector Machine Inference System (ELSIS) in which LS-SVM is utilized as a supervised learning method used for regression analysis/ classification in high dimensional space and Differential Evolution is employed to identify the optimal set of tuning parameters. At the second stage, ATF is integrated into ELSIS to establish Adaptive Time-Dependent Evolutionary Least Squares Support Vector Machine Inference System (ELSIST). In ELSIST, ATF is deployed to deal with the unbalanced nature of time series data. At the final stage, ELSIS incorporates FL to develop Evolutionary Fuzzy Least Squares Support Vector Machine Inference System (EFLSIS) in which FL aims at facilitating the system capability of approximate reasoning and coping with vague information. Experimental results obtained from system applications demonstrate that the newly established inference system can be a highly beneficial for decision-makers when solving various problems in the field of construction management.
ABSTRACT 1
ACKNOWLEDGEMENTS 3
TABLE OF CONTENTS 7
ABBREVIATIONS AND SYMBOLS 11
Abbreviations 11
Symbols 13
LIST OF FIGURES 17
LIST OF TABLES 19
CHAPER 1: INTRODUCTION 23
1.1 Research Motivation 23
1.2 Research Objectives 29
1.3 Scope Definition 30
1.3.1 Boundary Identification 30
1.3.2 Research Assumptions and Hypotheses 31
1.4 Research Methodology 32
1.5 Study Outline 38
CHAPER 2: LITERATURE REVIEW 40
2.1 Least Squares Support Vector Machine 41
2.1.1 Basic Concepts 41
2.1.2 Advantages and Disadvantages 44
2.2 Differential Evolution 45
2.2.1 Basic Concepts 45
2.2.2 Advantages and Disadvantages 48
2.3 Unbalanced Learning for Time Series Prediction 50
2.3.1 Basic Concepts 50
2.3.2 Advantages and Disadvantages 54
2.4 Fuzzy Logic 56
2.4.1 Basic Concepts 56
2.4.2 Advantages and Disadvantages 60
2.5 Object-Oriented System Development 61
2.5.1 Basic Concepts 61
2.5.2 Advantages and Disadvantages 63
CHAPER 3: MODEL CONSTRUCTION 65
3.1 Model Architecture 67
3.1.1 EFLSIMT Model Architecture 67
3.1.2 ELSIM Model Architecture (The 1st Phase) 75
3.1.3 ELSIMT Model Architecture (The 2nd Phase) 76
3.1.4 EFLSIM Model Architecture (The 3rd Phase) 80
3.2 Model Application Process 82
3.2.1 Feasibility Study 83
3.2.2 Identifying Influencing Factors 84
3.2.3 Collecting Data 84
3.2.4 Processing Data 84
3.2.5 Training the Inference Model 85
3.2.6 Obtaining Prediction Results 85
3.2.7 Validation Method 85
3.2.8 Evaluating Prediction Results 86
3.2.9 Applying the Derived Solution 86
3.3 Model Limitations 87
3.4 Potential Application Areas 87
CHAPER 4: SYSTEM DEVELOPMENT 88
4.1 System Planning 90
4.2 System Building 91
4.3 System Deploying 105
CHAPER 5: CASE STUDIES AND SYSTEM VALIDATION 108
5.1 Estimating Groutability Using ELSIS 109
5.1.1 Problem Statement – Case 1 109
5.1.2 System Validation – Case 1 111
5.2 Predicting Construction Project Cost at Completion Using ELSIS 117
5.2.1 Problem Statement – Case 2 117
5.2.2 System Validation – Case 2 120
5.3 Estimating Construction Cost Index Using ELSIS 128
5.3.1 Problem Statement – Case 3 128
5.3.2 System Validation – Case 3 131
5.4 Forecasting Construction Project Cash Flow Demand Using ELSIST 139
5.4.1 Problem Statement – Case 4 139
5.4.2 System Validation – Case 4 142
5.5 Prioritizing Bridges for Maintenance Projects Using EFLSIS 151
5.5.1 Problem Statement – Case 5 151
5.5.2 System Validation – Case 5 153
CHAPER 6: CONCLUSIONS AND RECOMMENDATIONS 163
6.1 Review Research Purposes 163
6.2 Research Accomplishments 164
6.3 Conclusions 165
6.4 Research Contributions 166
6.5 Future Research Directions and Recommendations 167
REFERENCES 169
CURRICULUM VITAE 178
Abba, W.F., 1997. Earned value management: reconciling government and commercial practices. Program Manager, 26 (February), 58–63.
Adey, B., Hajdin, R., Bruhwiler, E., 2003. Risk–based approach to the determination of optimal interventions for bridges affected by multiple hazards. Engineering Structures 25 (7), 903–912.
Akbulut, S., Saglamer, A., 2002. Estimating the groutability of granular soils: a new approach. Tunnelling and Underground Space Technology 17 (4), 371–380.
Akintoye, A., Bowen, P., Hardcastle, C., 1998. Macro–economic leading indicators of construction contract prices. Construction Management and Economics 16 (2), 159–175.
Alavala, C.R., 2008. Fuzzy Logic and Neural Networks – Basic Concepts and Applications. New Age International Publishers.
An, S.–H., Park, U.Y., Kang, K.–I., Cho, M.–Y., Cho, H.–H., 2007. Application of Support Vector Machines in Assessing Conceptual Cost Estimates. Journal of Computing in Civil Engineering 21 (4), 259–264.
Anumba, C.J., Egbu, C.O., Carrillo, P.M., 2005. Knowledge Management in Construction. Blackwell Publishing Ltd.
Arlot, S., 2010. A survey of cross-validation procedures for model selection. Statistics Surveys Vol. 4 (2010) 40–79.
Ashuri, B., Lu, J., 2010. Time Series Analysis of ENR Construction Cost Index. Journal of Construction Engineering and Management 136 (11), 1227–1237.
Ashuri, B., Shahandashti, S.M., Lu, J., 2012. Is the Information Available from Historical Time Series Data on Economic, Energy, and Construction Market Variables Useful to Explain Variations in ENR Construction Cost Index?. Construction Research Congress 2012. American Society of Civil Engineers, 457–464.
Bao, Y.–K., Liu, Z.–T., Guo, L., Wang, W., 2005. Forecasting stock composite index by fuzzy support vector machines regression, In Proceedings of the International Conference on Machine Learning and Cybernetics, Guangzhou, China, 18-21 Aug. 2005, 3535–3540.
Beale, M.H., Hagan, M.T., Demuth, H.B., 2010. Neural Network Toolbox Version 7 User's Guide. The MathWorks, Inc.
Bishop, C., 2006. Pattern Recognition and Machine Learning. Springer Science+Business Media, Singapore.
Bojadziev, G., Bojadziev, M., 2007. Advances in Fuzzy Systems Applications and Theory, 2nd Edition. Advances in Fuzzy Systems – Applications and Theory – Vol. 23, World Scientific Publishing Co. Pte. Ltd.
Booch, G., 1993. Object–oriented analysis and design with applications (2nd ed.). Benjamin–Cummings Publishing Co., Inc. Redwood City, CA, USA.
Brockwell, P.J., Davis, R.A., 2006. Introduction to time series and forecasting. Springer–Verlag New York, Inc.
Burman, P., 1989. A comparative study of ordinary cross-validation, r-fold cross-validation and the repeated learning-testing methods. Biometrika 76, 503-514.
Butron, C., Gustafson, G., Fransson, A., Funehag, J., 2009. Drip sealing of tunnels in hard rock: A new concept for the design and evaluation of permeation grouting. Tunnelling and Underground Space Technology 25 (2), 114–121.
Capano, C., Karshenas, S., 2003. Applying Accepted Economic Indicators to Predict Cost Escalation for Construction. In Proceedings of the 39th Annual Conference, Clemson University – Clemson, South Carolina, 277–288.
Carbonneau, R., Laframboise, K., Vahidov, R., 2008. Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research 184 (3), 1140–1154.
Chakraborty, U.K., 2008. Advances in Differential Evolution. Studies in Computational Intelligence, Volume 143, Springer–Verlag Berlin Heidelberg.
Chassiakos, A.P., Vagiotas, P., Theodorakopoulos, D.D., 2005. A knowledge–based system for maintenance planning of highway concrete bridges. Advances in Engineering Software 36 (11-12), 740–749.
Chatfield, C., 2000. Time–Series Forecasting. Chapman & Hall/CRC.
Chen, H.–L., Yang, B., Wang, G., Liu, J., Xu, X., Wang, S.–J., Liu, D.–Y., 2011. A novel bankruptcy prediction model based on an adaptive fuzzy k–nearest neighbor method. Knowledge–Based Systems 24 (8), 1348–1359.
Chen, S., Cowan, C.F.N., Grant, P.M., 1991. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on Neural Networks 2, 302–309.
Chen, T.L., 2008. Estimate at Completion for Construction Projects using Evolutionary Fuzzy Neural Inference Model, MS Thesis. Department of Construction Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
Cheng, M.–Y., Hoang, N.–D., 2013a. Cash Flow Prediction for Construction Project Using a Novel Adaptive Time–Dependent Least Squares Support Vector Machine Inference Model. Journal of Civil Engineering and Management (accepted, in press).
Cheng, M.–Y., Hoang, N.–D., 2013b. Groutability Prediction of Microfine Cement Based Soil Improvement Using Evolutionary LS–SVM Inference Model. Journal of Civil Engineering and Management (accepted, in production). doi: 10.3846/13923730.2013.802717.
Cheng, M.–Y., Hoang, N.–D., 2013c. Hybrid Intelligence Approach Based on LSSVM and Differential Evolution for Construction Cost Index Estimation: A Taiwan Case Study. Automation in Construction (accepted, in production). doi: 10.1016/j.autcon.2013.05.018.
Cheng, M.–Y., Hoang, N.–D., 2013d. Interval Estimation of Construction Cost at Completion Using Least Squares Support Vector Machine. Journal of Civil Engineering and Management (accepted, in production). doi: 10.3846/13923730.2013.801891.
Cheng, M.–Y., Hoang, N.–D., 2013e. Risk Score Inference for Bridge Maintenance Project Using Evolutionary Fuzzy Least Squares Support Vector Machine. Journal of Computing in Civil Engineering, ASCE (accepted, in production). doi:10.1061/(ASCE)CP.1943–5487.0000275.
Cheng, M.–Y., Hoang, N.–D., Roy, A.F.V., Wu, Y.–W., 2011a. A novel time–depended evolutionary fuzzy SVM inference model for estimating construction project at completion. Engineering Applications of Artificial Intelligence 25 (4), 744–752.
Cheng, M.–Y., Lien, L.–C., 2011. A hybrid AI–based particle bee algorithm for facility layout optimization. Engineering with Computers, 1–13.
Cheng, M.–Y., Peng, H.–S., Wu, Y.–W., Chen, T.–L., 2010. Estimate at Completion for construction projects using Evolutionary Support Vector Machine Inference Model. Automation in Construction 19 (5), 619–629.
Cheng, M.–Y., Roy, A.F.V., 2010. Evolutionary fuzzy decision model for construction management using support vector machine. Expert Systems with Applications 37 (8), 6061–6069.
Cheng, M.–Y., Roy, A.F.V., 2011. Evolutionary fuzzy decision model for cash flow prediction using time–dependent support vector machines. International Journal of Project Management 29 (1), 56–65.
Cheng, M.–Y., Tsai, H.–C., Ko, C.–H., Chang, W.–T., 2008. Evolutionary Fuzzy Neural Inference System for Decision Making in Geotechnical Engineering. Journal of Computing in Civil Engineering 22 (4), 272–280.
Cheng, M.–Y., Tsai, H.–C., Liu, C.–L., 2009. Artificial intelligence approaches to achieve strategic control over project cash flows. Automation in Construction 18 (4) 386–393.
Cheng, M.–Y., Tsai, H.–C., Sudjono, E., 2011b. Evaluating subcontractor performance using evolutionary fuzzy hybrid neural network. International Journal of Project Management 29 (3), 349–356.
Cheng, M.–Y., Tsai, H.–C., Sudjono, E., 2012. Evolutionary fuzzy hybrid neural network for dynamic project success assessment in construction industry. Automation in Construction 21, 46–51.
Cheng, M.–Y., Wu, Y.–W., 2009. Evolutionary support vector machine inference system for construction management. Automation in Construction 18 (5), 597–604.
Cheng, Y.–M., Leu, S.–S., 2008. Constraint–based clustering model for determining contract packages of bridge maintenance inspection. Automation in Construction 17 (6), 682–690.
Chou, J.–S., Chen, H.–M., Hou, C.–C., Lin, C.–W., 2010. Visualized EVM system for assessing project performance. Automation in Construction 19 (5), 596–607.
Chou, J.–S., Chiu, C.–K., Farfoura, M., Al–Taharwa, I., 2011. Optimizing the Prediction Accuracy of Concrete Compressive Strength Based on a Comparison of Data–Mining Techniques. Journal of Computing in Civil Engineering, ASCE 25 (3), 242–253.
Christensen, D.S., 1993. Determining an accuracy estimate at completion. National Contract Management Journal 25, 17–25.
Christensen, D.S., Antolini, R.C., McKinney, J.W., 1995. A review of estimate at completion research. Journal of Cost Analysis and Management, Spring 1995, 41–62.
Dainty, A., Cheng, M., Moore, D., 2005. Competency–Based Model for Predicting Construction Project Managers’ Performance. Journal of Management in Engineering 21, 2–9.
De Brabanter, K., Karsmakers, P., Ojeda, F., Alzate, C., De Brabanter, J., Pelckmans, K., De Moor, B., Vandewalle, J., Suykens, J.A.K., 2010. LS–SVMlab Toolbox User's Guide version 1.8. Internal Report 10–146, ESAT–SISTA, K.U.Leuven (Leuven, Belgium).
Elhag, M.S., Wang, Y.–M., 2007. Risk Assessment for Bridge Maintenance Projects: Neural Networks versus Regression Techniques. Journal of Computing in Civil Engineering 21 (6), 402–409.
Engels, G., Groenewegen, L., 2000. Object–Oriented Modeling: A Roadmap. in Proceedings of the Conference on The Future of Software Engineering, Limerick, Ireland, 103 – 116.
Eyke, H., 2011. Fuzzy sets in machine learning and data mining. Applied Soft Computing 11 (2), 1493–1505.
Fellows, R.F., 1991. Escalation management: forecasting the effects of inflation on building projects. Construction Management and Economics, 9, 187–204.
Flower, M., Scott, K., 2000. UML distilled: A breif guide to the standard object modeling language, second edition. Addison Wesley, Reading, Massachusetts.
Fu, C., Liu, C., 2010. An Economic Analysis of Construction Industries Producer Prices in Australia. International Journal of Economics and Finance 2, 3–14.
Gestel, T.V., Suykens, J.A.K., Baesens, B., Viaene, S., Vanthienen, J., Dedene, G., Moor, B.D., Vandewalle, J., 2004. Benchmarking Least Squares Support Vector Machine Classifiers. Machine Learning 54, 5–32.
Guo, X., Ma, X., 2010. Mine water discharge prediction based on least squares support vector machines. Mining Science and Technology (China) 20, 738–742.
Guo, Z., Bai, G., 2009. Application of Least Squares Support Vector Machine for Regression to Reliability Analysis. Chinese Journal of Aeronautics 22, 160–166.
Hanss, M., 2005. Applied Fuzzy Arithmetic – An Introduction with Engineering Applications. Springer–Verlag Berlin Heidelberg.
Hussain, A.J., Knowles, A., Lisboa, P.J.G., El–Deredy, W., 2008. Financial time series prediction using polynomial pipelined neural networks. Expert Systems with Applications 35, 1186–1199.
Hwang, S., 2009. Dynamic Regression Models for Prediction of Construction Costs. Journal of Construction Engineering and Management 135, 360–367.
Hwang, S., 2011. Time Series Models for Forecasting Construction Costs Using Time Series Indexes. Journal of Construction Engineering and Management 137, 656–662.
Hwang, S., Liu, L.Y., 2005. Proactive Project Control Using Productivity Data and Time Series Analysis, in: Lucio, S., Feniosky, P.–M. (Eds.), in Proceedings of the 2005 ASCE International Conference on Computing in Civil Engineering, ASCE, Mexico, 12–15, 179.
Hwee, N.G., Tiong, R.L.K., 2002. Model on cash flow forecasting and risk analysis for contracting firms. International Journal of Project Management 20, 351–363.
Iranmanesh, H., Mojir, N., Kimiagari, S., 2007. A new formula to “Estimate At Completion” of a Project's time to improve “Earned Value Management System“, In Proceeding of the IEEE International Conference on Industrial Engineering and Engineering Management, 2007, 1014–1017.
Ismail, S., Samsudin, R., Shabri, A., 2010. River Flow Forecasting: a Hybrid Model of Self Organizing Maps and Least Square Support Vector Machine. Hydrology and Earth System Sciences Discussions 7, 8179–8212.
Ismail, S., Shabri, A., Samsudin, R., 2011. A hybrid model of self–organizing maps (SOM) and least square support vector machine (LSSVM) for time–series forecasting. Expert Systems with Applications 38, 10574–10578.
Issa, R.R.A., 2000. Application of Artificial Neural Networks to Predicting Construction Material Prices, 40513 ed. ASCE, Stanford, California, USA, 147–147.
Jang, J.–S.R., C.–T.Sun, Mizutani, E., 1997. Neuro–fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice–Hall, Inc.
Kamruzzaman, J., Begg, R.K., Sarker, R.A., 2006. Artificial Neural Networks in Finance and Manufacturing. Idea Group Publishing.
Kandel, A., 1986. Fuzzy Mathematic Techniques with Applications. Adison Wesley.
Kawamura, K., Miyamoto, A., 2003. Condition state evaluation of existing reinforced concrete bridges using neuro–fuzzy hybrid system. Computers & Structures 81, 1931–1940.
Khemchandani, R., Jayadeva, Chandra, S., 2009. Regularized least squares fuzzy support vector regression for financial time series forecasting. Expert Systems with Applications 36, 132–138.
Khosrowshahi, F., Kaka, A.P., 2007. A Decision Support Model for Construction Cash Flow Management. Computer–Aided Civil and Infrastructure Engineering 22, 527–539.
Kim, G.–H., An, S.–H., Kang, K.–I., 2004. Comparison of construction cost estimating models based on regression analysis, neural networks, and case–based reasoning. Building and Environment 39, 1235–1242.
Kiranyaz, S., Ince, T., Yildirim, A., Gabbouj, M., 2009. Evolutionary artificial neural networks by multi–dimensional particle swarm optimization. Neural Networks 22, 1448–1462.
Ko, C.–H., Cheng, M.–Y., 2003. Hybrid use of AI techniques in developing construction management tools. Automation in Construction 12, 271–281.
Ko, C.–H., Cheng, M.–Y., Wu, T.–K., 2007. Evaluating sub–contractors performance using EFNIM. Automation in Construction 16, 525–530.
Kong, F., Wu, X.–j., Cai, L.–y., 2008. A Novel Approach Based on Support Vector Machine to Forecasting the Construction Project Cost. 2008 International Symposium on Computational Intelligence and Design.
Larman, C., 1988. Applying UML and patterns: An introduction to object–oriented analysis and design. Prentice Hall, Upper Saddle River, New Jersey.
Lee, J.–H., Le, K.N., Loo, Y.–C., Blumenstein, M.M., Guan, H., 2008. ANN–Based Bridge Condition Rating Models Using Limited Structural Inspection Records. in Proceedings of the International Conference on Transport Infrastructure (ICTI2008).
Leu, S.–S., Lo, H.–C., 2004. Neural–network–based regression model of ground surface settlement induced by deep excavation. Automation in Construction 13, 279–289.
Li, S.–T., Ho, H.–F., 2009. Predicting financial activity with evolutionary fuzzy case–based reasoning. Expert Systems with Applications 36, 411–422.
Liao, K.–W., Fan, J.–C., Huang, C.–L., 2011. An artificial neural network for groutability prediction of permeation grouting with microfine cement grouts. Computers and Geotechnics 38, 978–986.
Lin, C.–F., Wang, S.–D., 2002. Fuzzy support vector machines. IEEE Transactions on Neural Networks 13, 464–471.
Lin, C.T., Lee, C.S.G., 1996. Neural Fuzzy Systems – A Neuro–Fuzzy Synergism to Intelligence Systems. Prentice Hall.
Liu, L., Wang, W., 2008. Exchange Rates Forecasting with Least Squares Support Vector Machine, in Proceedings of the International Conference on Computer Science and Software Engineering, 12-14 Dec. 2008, Wuhan, Hubei, China, 1017–1019.
Liu, L., Zhu, K., 2007. Improving Cost Estimates of Construction Projects Using Phased Cost Factors. Journal of Construction Engineering and Management 133, 91–95.
Malviya, R., Pratihar, D.K., 2011. Tuning of neural networks using particle swarm optimization to model MIG welding process. Swarm and Evolutionary Computation 1, 223–235.
Mamdani, E.H., Assilian, S., 1975. An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1–13.
Mubarak, S., 2010. Construction Project Scheduling and Control. John Wiley & Sons, Inc.
Muller, K.–R., Smola, A.J., Ratsch, G., Scholkopf, B., Kohlmorgen, J., Vapnik, V., 1997. Predicting Time Series with Support Vector Machines. in Proceedings of the 7th International Conference on Artificial Neural Networks, Springer–Verlag London, UK.
Nam, H., Han, S.H., Kim, H., 2007. Time series analysis of construction cost index using wavelet transformation and a neural network. in Proceedings of the 24th International Symposium on Automation & Robotics in Construction (ISARC 2007). Construction Automation Group, I.I.T. Madras.
Nassar, K.M., Nassar, W.M., Hegab, M.Y., 2005. Evaluating Cost Overruns of Asphalt Paving Project Using Statistical Process Control Methods. Journal of Construction Engineering and Management, ASCE 131 (11), 1173–1178.
Oliveira, J.V.d., Pedrycz, W., 2007. Advances in Fuzzy Clustering and Its Applications. John Wiley & Sons Ltd.
Onwubolu, G.C., Davendra, D., 2009 Differential Evolution: A Handbook for Global Permutation–Based Combinatorial Optimization. Springer–Verlag Berlin Heidelberg.
Ozgurel, H.G., Cumaraswamy, V., 2005. Effect of Grain Size and Distribution on Permeability and Mechanical Behavior of Acrylamide Grouted Sand. Journal of Geotechnical and Geoenvironmental Engineering 131, 1457–1465.
Pan, N.–F., Ko, C.–H., Yang, M.–D., Hsu, K.–C., 2011. Pavement performance prediction through fuzzy regression. Expert Systems with Applications 38, 10010–10017.
Pasino, K.M., Yurkovich, S., 1998. Fuzzy Control. Adison Wesley.
Perret, S., Khayat, K.H., Gagnon, E., Rhazi, J., 2002. Repair of 130–Year Old Masonry Bridge using High–Performance Cement Grout. Journal of Bridge Engineering ,ASCE 7, 31–38.
Perret, S., Palardy, D., Ballivy, G., 2000. Rheological Behavior and Setting Time of Microfine Cement–Based Grouts. Materials Journal 97, 472–8.
Price, K.V., Storn, R.M., Lampinen, J.A., 2005. Differential Evolution A Practical Approach to Global Optimization. Springer–Verlag.
Quan, T., Liu, X., Liu, Q., 2010. Weighted least squares support vector machine local region method for nonlinear time series prediction. Applied Soft Computing 10, 562–566.
Ross, T.J., 2004. Fuzzy Logic With Engineering Application. John Wiley & Sons Ltd.
Roy, 2010. Evolutionary Fuzzy Decision Model For Construction Management Using Weighted Support Vector Machine. PhD Dissertation, National Taiwan University of Science and Technology.
Rubio, G., Pomares, H., Rojas, I., Herrera, L.J., 2011. A heuristic method for parameter selection in LS–SVM: Application to time series prediction. International Journal of Forecasting 27, 725–739.
Russell, Jaselskis, E., Lawrence, 1997. Continuous Assessment of Project Performance. Journal of Construction Engineering and Management 123, 64–71.
Russell, S.J., Norvig, P., 2003. Artificial Intelligence A Modern Approach, 2nd Edition. Prentice Hall, Person Education, Inc.
Samarasinghe, S., 2006. Neural Networks for Applied Sciences and Engineering. Taylor and Francis.
Samui, P., 2011. Least square support vector machine and relevance vector machine for evaluating seismic liquefaction potential using SPT. Natural Hazards 59, 811–822.
Samui, P., Kothari, D.P., 2011. Utilization of a least square support vector machine (LSSVM) for slope stability analysis. Scientia Iranica 18, 53–58.
Samui, P., Lansivaara, T., Kim, D., 2011. Utilization relevance vector machine for slope reliability analysis. Applied Soft Computing 11, 4036–4040.
Sapankevych, N., Sankar, R., 2009. Time Series Prediction Using Support Vector Machines: A Survey. IEEE Computational Intelligence Magazine 4, 24–38.
Satzinger, J.W., Jackson, R.B., Burd, S.D., 2008. Systems Analysis and Design in a Changing World, 5th edition. Course Technology, Cambridge, Massachusetts.
Sears, K., Sears, G., Clough, R., 2008. Construction Project Management: A Practical Guide to Field Construction Management (5th Edition). John Wiley and Son, Inc., Hoboken, New Jersey.
Shu, C.W., Chang, C.C., Lin, C.J., 2010. A practical guide to support vector classification. Technical Report. Department of Computer Science, National Taiwan University.
Sivanandam, S.N., Sumathi, S., Deepa, S.N., 2007. Introduction to Fuzzy Logic using MatLab. Springer–Verlag Berlin Heidelberg.
Son, H., Kim, C., Kim, C., 2012. Hybrid principal component analysis and support vector machine model for predicting the cost performance of commercial building projects using pre–project planning variables. Automation in Construction 27, 60–66.
Storn, R., Price, K., 1997. Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. J. Global Optim. 11, 341–359.
Suykens, J., Gestel, J.V., Brabanter, J.D., Moor, B.D., Vandewalle, J., 2002. Least Square Support Vector Machines. World Scientific Publishing Co. Pte. Ltd., Singapore.
Suykens, J.A.K., & Vandewalle, J., 1999. Least squares support vector machine classifiers. Neural Processing Letters 9, 293–300.
Takagi, T., Sugeno, M., 1985. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 15, 116–32.
Tay, F.E.H., Cao, L., 2001. Application of support vector machines in financial time series forecasting. Omega 29, 309–317.
Tekin, E., Akbas, S., 2011. Artificial neural networks approach for estimating the groutability of granular soils with cement–based grouts. Bulletin of Engineering Geology and the Environment 70, 153–161.
Thomas Ng, S., Cheung, S.O., Martin Skitmore, R., Lam, K.C., Wong, L.Y., 2000. Prediction of tender price index directional changes. Construction Management and Economics 18, 843–852.
Touran, A., Lopez, R., 2006. Modeling Cost Escalation in Large Infrastructure Projects. Journal of Construction Engineering and Management 132, 853–860.
Tsai, Y., Yang, C.–T., 2004. Constrained Fuzzy c–Mean Clustering Algorithm for Determining Bridge Let Projects. Journal of Computing in Civil Engineering, ASCE 18, 215–225.
Vadaparty, K., 2000. UML & Beyond Use Cases–Basics. Journal of Object Oriented Programming 12, 4–8.
Vapnik, V.N., 1998. Statistical Learning Theory John Wiley & Sons, Inc.
Vercellis, C., 2009. Business Intelligence Data Mining and Optimization for Decision Making. John Wiley & Sons Ltd.
Wang, H., Hu, D., 2005. Comparison of SVM and LS-SVM for Regression. in Proceedings of the International Conference on Neural Networks and Brain, 13–15 Oct. 2005, Beijing, China.
Wang, Y.–M., Elhag, T.M.S., 2007. A comparison of neural network, evidential reasoning and multiple regression analysis in modelling bridge risks. Expert Systems with Applications 32, 336–348.
Wang, Y.–M., Liu, J., Elhag, T.M.S., 2007. A fuzzy group decision making approach for bridge risk assessment. Computers & Industrial Engineering 53, 137–148.
Williams, T.P., 1994. Predicting Changes in Construction Cost Indexes Using Neural Networks. Journal of Construction Engineering and Management 120, 306–320.
Wilmot, C.G., Mei, B., 2005. Neural Network Modeling of Highway Construction Costs. Journal of Construction Engineering and Management 131, 765–771.
Wong, K.–P., Dong, Z., 2005. Differential Evolution, an Alternative Approach to Evolutionary Algorithm, In Proceedings of the 13th International Conference on Intelligent Systems Application to Power Systems, pp. 73–83.
Xu, J., Moon, S., 2011. Stochastic Forecast of Construction Cost Index Using a Cointegrated Vector Auto‐Regression Model. Journal of Management in Engineering, 73.
Yao, X., Liu, Y., 1997. A new evolutionary system for evolving artificial neural networks. IEEE Transactions on Neural Networks 8, 694–713.
Yoon, H., Jun, S.–C., Hyun, Y., Bae, G.–O., Lee, K.–K., 2011. A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. Journal of Hydrology 396, 128–138.
Yu, L., Chen, H., Wang, S., Lai, K.K., 2009. Evolving Least Squares Support Vector Machines for Stock Market Trend Mining. IEEE Transactions on Evolutionary Computation 13, 87–102.
Yu, W.–d., Liu, Y.–c., 2006. Hybridization of CBR and numeric soft computing techniques for mining of scarce construction databases. Automation in Construction 15, 33–46.
Zebovitz, S., Krizek, R.J., Atmatzidis, D.K., 1989. Injection of Fine Sands with Very Fine Cement Grout. Journal of Geotechnical Engineering 115, 1717–1733.
Zeng, J., An, M., Smith, N.J., 2007. Application of a fuzzy based decision making methodology to construction project risk assessment. International Journal of Project Management 25, 589–600.
Zhang, G., Hu, M.Y., 1998. Neural network forecasting of the British Pound/US Dollar exchange rate. Omega 26, 495–506.
Zhang, Y.Y., 2007. Forecasting the trend of construction cost indices for Taiwan employing Support Vector Machine. Master Thesis, Chao Yang University of Technology, Taiwan.
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