Abstract
The budget estimated in the planning phase of construction has to be accurate to be effective until after the design phase. Therefore the special method needs to improve the accuracy of estimating construction cost in the planning phase. Recently, to improve the accuracy of estimating construction cost for road bridges and architectural facilities, Case-based Reasoning (CBR) which makes use of Genetic Algorithm (GA) and Multiple Regression Analysis (MRA) and other principles are being used. This study suggests the cost estimation model which uses CBR and makes the database reflected the character of the railroad bridge. To establish this effective CBR model, this study examined combinations of attributes, criteria of similarities and retrieval ranks and applied GA for an optimization of attribute weights throughout learning process. According to the results of this study, CBR should use a five-attribute combination, retrieve six similar cases and use the similarity criteria by applying a method in which ten points are deducted for each 10% difference to get the minimum error. According to the verification results of the CBR model, the mean absolute error rate is 11.9% and the standard deviation is 12.7%. Therefore, the accuracy of this CBR model is 30% more effective than the criteria of Ministry of Construction & Transportation (MOCT) and Korea Development Institute (KDI) model.
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References
Ahn, S. H. and Kang, K. I. (2005). “A study on construction cost prediction for pre-phase of apartment house using expert knowledge, collection of learned papers (structural section).” Architectural Institute of Korea, Vol. 21 No. 6, pp. 81–88.
Albright, S. C. and Winston, W. L. (2007). Management science modeling, Thomson South-Western, Mason, Ohio.
Bell, L. C. and Kaminsky, A. (1987). “Data base for preliminary cost estimating.” Journal of Transportation Engineering, Vol. 113, No. 4, pp. 341–347.
Carr, R. I. (1989). “Cost estimating principles.” Journal of Construction Engineering and Management, Vol. 115, No. 4, pp. 545–551.
Chung, C. (2002). Optimization of sewer rehabilitation schedule and budget by genetic algorithm, Master’s Degree dissertation, Yonsei University, p. 56.
Dogan, O. and Kartal, M. (2008). “SAR raw data simulation of targets using three dimensional models.” EUSAR-European Conference, Vol. 7, No. 3, pp. 137–140.
Gentner, D. (1983). “Structure-mapping: A theoretical framework for analogy.” Cognitive Science, Vol. 7, No. 2, pp. 155–170.
Jenkins, W. M. (1997). “On the application of natural algorithm to structural design optimization.” Engineering Structure, Vol. 194, pp. 302–308
Kang, C. S., Lee, G. H., Kim, K. M., and Kim, K. J. (2008). “Development of approximate cost estimating model using case based reasoning-centering around PSC beam bridge.” Proceeding, 8th Conference, Korea Institute of Construction Engineering & Management.
Karshenas, S. (1984). “Pre-design cost estimating method for multistory buildings.” Journal of Construction Engineering and Management, Vol. 110, No. 1, pp. 79–86
Kim, J. H., Kim, C. W., Kim, Y. H., and Park, J. W. (1996). “Retrieving of similar case using fuzzy concept at CBR, proceeding.” Korean Society for Expert System, Vol. 2, No. 1, pp. 153–160.
Kim, K. H. (2009). A study on conceptual cost estimating using the case based reasoning for IPC girder bridge, Master’s Degree dissertation, KyungPook National University, Korea
Kim, K. H. and Kang, K. I. (2004). A Study on estimating initial construction cost for apartment house using case based reasoning, Collection of Learned Papers (Structural Section), Architectural Institute of Korea.
Kim, K. H. and Kim, B. S. (2009). “An approximate cost estimation model for IPC girder bridge.” Journal of Research Institute of Construction Technology, Kyungpook National University, Korea, Vol. 16, No. 1, pp. 117–141.
Kim, K. J. and Kim, K. M. (2010). “Preliminary cost estimation model using case-based reasoning and genetic algorithms.” ASCE Journal of Computing in Civil Engineering, Vol. 24, No. 6, pp. 499–505.
Kim, K. S. (2005). Prediction of foreign exchange crisis using case based reasoning, Sungkyunkwan University Publishing Department, Seoul, Korea.
Kim, T. G. (2006). Regression analysis for u-Can, the first edition, Human and Welfare, Seoul, pp. 53–88.
Kim, Y. S., Son, M. J., Hyeon, C. T., and Hong, T. H. (2009). “A division method using regression model in the division phase of cost prediction model based on CBR, proceeding.” Korea Institute Construction Engineering Management, No. 6, Track 2, pp. 291–296.
Korea Development Institute (2004). Pre-feasibility study standard guideline for road·railway projects, Amendment Fourth Edition.
Koumousis, V. K. and Georgiou, P. G. (1994). “Genetic algorithms in discrete optimization of steel truss roofs.” Journal of Computing in Civil Engineering, Vol. 8, No. 3, pp. 309–325.
Lee, C. K. (2000). Method to properly maintain bridge facility by fuzzy and genetic algorithm, PhD Dissertation, Chung-Ang University, Seoul, Korea, pp.74–84
Lee, J. E. (1998). “Theoretical study and embody status of case-based reasoning.” Sangmyung University Management Institute, No. 6, pp. 1–31.
Leu, S. S. and Yang, C. H. (1999). “A genetic-algorithm-based resource constrained construction scheduling system.” Construction Management Economic, Vol. 17, pp. 767–776.
Li, H. and Love, P. E. D. (1998). “Site-level facilities layout using genetic algorithms.” J. Comput. Civ. Eng., 124, 227–231.
Ministry of Construction & Transportation (2004). Investment assessment guideline for development project of public transport facility, Korea.
Ministry of Land, Transport & Maritime Affairs (2008). Road work handbook.
Oberlender, G. D. and Trost, S. M. (2001). “Predicting accuracy of early cost estimates based on estimate quality.” Journal of Construction Engineering and Management, Vol. 127, No. 3, pp. 173–182.
Park, M., Seong, K., Lee, H., Ji, S., and Kim, S. (2010). “Schematic cost estimation method using case-based reasoning: Focusing on determining attribute weight.” Korea Institute Construction Engineering Management, Vol. 11, No. 4, pp. 22–31.
Seo, J., Kim, S., Cho, J., and Chun, J. (2010). “Supporting model of cost planning using case based reasoning in pre-design phase - Focused on the case of public office building —.” Architectural Institute of Korea, Vol. 26, No. 4, pp. 159–168.
Schank, R. (1982). Dynamic memory: A theory of learning in computers and people, New York, Cambridge University Press.
Turban, E. (1995). Decision support and expert systems management support systems, Fourth Edition, Prentice Hall.
Tam, C. M., Tong, T. K. L., and Chan, W. K. W. (2001). “Genetic algorithm for optimizing supply locations around tower crane.” Journal of Construction Engineering and Management, Vol. 127, No. 4, pp. 315–321.
Willmot, C. G. and Cheng, G. (2003). “Estimating future highway construction costs.” Journal of Construction Engineering and Management, Vol. 129, No. 3, pp. 272–279.
Willmot, C. G. and Mei, B. (2005). “Neural network modeling of highway construction costs.” Journal of Construction Engineering and Management, Vol. 131, No. 7, pp. 765–771.
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Kim, B.S. The approximate cost estimating model for railway bridge project in the planning phase using CBR method. KSCE J Civ Eng 15, 1149–1159 (2011). https://doi.org/10.1007/s12205-011-1342-2
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DOI: https://doi.org/10.1007/s12205-011-1342-2