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http://dx.doi.org/10.5345/JKIC.2011.06.3.238

Support Vector Machine Model to Select Exterior Materials  

Kim, Sang-Yong (School of Construction Management and Engineering, University of Reading)
Publication Information
Journal of the Korea Institute of Building Construction / v.11, no.3, 2011 , pp. 238-246 More about this Journal
Abstract
Choosing the best-performance materials is a crucial task for the successful completion of a project in the construction field. In general, the process of material selection is performed through the use of information by a highly experienced expert and the purchasing agent, without the assistance of logical decision-making techniques. For this reason, the construction field has considered various artificial intelligence (AI) techniques to support decision systems as their own selection method. This study proposes the application of a systematic and efficient support vector machine (SVM) model to select optimal exterior materials. The dataset of the study is 120 completed construction projects in South Korea. A total of 8 input determinants were identified and verified from the literature review and interviews with experts. Using data classification and normalization, these 120 sets were divided into 3 groups, and then 5 binary classification models were constructed in a one-against-all (OAA) multi classification method. The SVM model, based on the kernel radical basis function, yielded a prediction accuracy rate of 87.5%. This study indicates that the SVM model appears to be feasible as a decision support system for selecting an optimal construction method.
Keywords
exterior material; one-against-all; support vector machine;
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1 Cristianini N, Shawe-Tayler J. An introduction to support vector machine and other kernel-based learning methods. 1st Ed. Cambridge (UK): Cambridge university press; 2000. p. 1-204.
2 Bertsekas DP. Constrained optimization and Lagrange multiplier methods. 1st Ed. Sandiego (CA): Athena Scientific; 1996. p. 1-410.
3 Mercer T. Functions of positive and negative type and their connection with the theory of integral equations. Philosophical Transactions of the Royal Society: Reference. 1909 Jan;209:415-46.   DOI
4 Kim KJ. Finanacial time series forecasting using support vector machines, Neurocomputing 2003 Sept;55(1):307-19.   DOI   ScienceOn
5 Ullah AMMS, Khalifa HH. An intelligent method for selecting optimal materials and its application. Advanced Engineering Informatics. 2008 Oct;22(4):473-83.   DOI   ScienceOn
6 Athanasopoulos G, Riba CR, Athanasopoulou C. A decision support system for coating selection based on fuzzy logic and multi-criteria decision making. Expert Systems with Applications 2009 Oct;36(8):10848-53.   DOI   ScienceOn
7 Park UY, Kim GH. A study on predicting construction cost of apartment projects based on Support Vector Regression at the Early Project Stage. Journal of the Architectural Institute of Korea Planning and Design 2007 April;23(4):165-72. Korean.   과학기술학회마을
8 Sewell M. SVMdark: A Windows implementation of a support vector machine. London (UK): UCL; 2005.
9 Joachims T. Making large-scale SVM learning practical. Support Vector Learning. Massachusetts (MA): MIT Press Cambridge; 1999. p. 41-56.
10 Weston J. Watkins C. Support vector machines for multi-class pattern recognition. In Proceeding European Symposium on Artificial Neural Networks 1999. Bruges (Belgium): ESANN; 1999. p. 219-24.
11 Nunn C, Kummert A, Muller D, Meuter M, Muller-Schneiders S. An improved adaboost learning scheme using LDA features for object recognition. Intelligent Transportation Systems 2009 ITSC 09 12th International IEEE Conference on; St. Louis: IEEE Conference Proceedings; 2009 January. p.1-6.
12 Kolodner JL. An introduction to case-based reasoning. Artificial Intelligence Review 1992;6(1):3-34.   DOI
13 Osuna E, Freund R, Girosi F. Training support vector machines: an application to face detection. CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition; Washington, D.C; IEEE Computer Society; 1997 Jan. p. 130-6.
14 Dibike YB, Velickov S, Solomantine D, Abbott MB. Model induction with support vector machines: induction and applications, Journal of Computing in Civil Engineering 2001 July;15(3):208-16.   DOI   ScienceOn
15 Mattera D, Haykin S. Support vector machines for dynamic reconstruction of a chaotic system. Advances in Kernel Methods. Massachusetts (MA): MIT Press Cambridge; 1999. p. 211-42.
16 Burges CJC. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 1998 June;2(2):121-67.   DOI   ScienceOn
17 Steinwart I. Support vector machines are universally consistent, Journal of Complexity 2002 Sept;18(3):768-91.   DOI   ScienceOn
18 Muller KR, Smola A, Ratsch G, Scholkopf B, Kohlmorgen J, Vapnik V. Predicting time series with support vector machines, ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks 1997; London (UK); 1997. p. 99-1004.
19 Vapnik V. The nature of statistical learning theory. 2nd Ed. New York: Springer; 1995. p. 1-314
20 Abe S. Analysis of multiclass support vector machines. In Proceeding of International Conference on Computational Intelligence for Modelling Control and Automation 2003; Vienna (Austria); CIMCA; 2003. p.385-96.
21 Ries R, Bilec M, Gokhan NM, Needy KL. The economic benefits of green buildings: a comprehensive case study. The Engineering Economist. 2006 Sept;51(3):259-95.   DOI   ScienceOn
22 Formoso CT, Revelo VH. Improving the materials supply chains system in small-sized building firms. Automation in Construction 1999 Aug;8(6):663-70.   DOI   ScienceOn
23 An SH, Park UY, Kang KI, Cho MY, Cho HH. Application of support vector machine in assessing conceptual cost estimates. Journal of Computing in Civil Engineering 2007 July;21(4):259-64.   DOI   ScienceOn
24 Akintoye A, Fitzgerald E. A survey of current cost estimating practices in the UK. Construction Management and Economics 2000 Mar;18(2):161-72.   DOI
25 Kumar PR, Ravi V. Bankruptcy prediction in banks and firms via statistical and intelligent techniques- a review. European Journal of Operational Research 2007 July;180(1):1-28.   DOI   ScienceOn
26 Huang Z, Chen H, Hsu CJ, Chen WH, Wu S. Credit rating analysis with support vector machines and neural networks: a market comparative study. Decision Support System 2004 Sept;37(4):543-58.   DOI   ScienceOn
27 Roy A, Barat P, De SK. Material classification through neural network. Ultrasonics 1995 May;33(3):175-80.   DOI   ScienceOn
28 Alsugair AM, Al-Qudrah AA. Artificial neural network approach for pavement maintenance. Journal of Computing in Civil Engineering 1998 Oct;12(4):249-55.   DOI   ScienceOn
29 Shin YS, Kim DW, Kim JY, Kang KI, Cho MY, Cho HH. Application of adaboost to the retaining wall method selection in construction. Journal of Computing in Civil Engineering 2009 May;23(3):188-92.   DOI
30 Construction Industry Institute (CII). Procurement and materials management: a guide to effective project execution. New York(US): University of Texas at Austin; 1999. 176. p
31 Farag MM. Quantitive method of material selction. Handbook of materials selection. New York (US): Wiley; 2007. 1520. p
32 Sefair JA, Castro-Lacouture D, Medaglia AL. Material selection in building construction using optimal scoring method (OSM). In: Samuel T, Ariaratnam, Rojas EM, editors;In Proceedings of the 2009 Construction Research Congress; Seattle. Washington: ASCE; 2009 April. 1079-86.
33 Amen R, Vomacka P. Case-based reasoning as a tool for materials selection. Material and Design 2001 Aug;21(5):353-8.
34 Jang H, Lee S, Choi S. Optimization of floor-level construction material-layout using genetic algorithms. Automation in Construction 2007 July;16(4):531-45.   DOI   ScienceOn