Conceptual Cost-Prediction Model for Public Road Planning via Rough Set Theory and Case-Based Reasoning
Publication: Journal of Construction Engineering and Management
Volume 140, Issue 1
Abstract
Long-term transportation policies require government officials to predict the cost of public road construction during the conceptual planning phase. However, early cost prediction is often inaccurate because public officials are not familiar with cost engineering practices, and moreover, have limited time and insufficient information for estimating the possible range of the cost distribution. This study develops a conceptual cost prediction model by combining rough set theory, case-based reasoning, and genetic algorithms to better predict costs in the conceptual planning phase. Rough set theory and qualitative in-depth interviews are integrated to select the proper input attributes for the cost prediction model. Case-based reasoning is then applied to predict road construction costs by considering users’ difficulties in the conceptual policy planning phase. A genetic algorithm is also used to assist the rough set model and case-based reasoning model to obtain optimal solutions. The result of the analysis shows that the proposed conceptual cost prediction model is reliable and robust compared to the existing cost prediction model.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This research was supported by a grant (11High-techUrbanG05) from the High-tech Urban Development Program (HUDP) funded by the Ministry of Land, Transport and Maritime Affairs of the Korean government.
References
Aamodt, A., and Plaza, E. (1994). “Case-based reasoning: Foundational issues, methodological variations, and system approaches.” AI Commun., 7(1), 39–59.
Alex, D. P., Al Hussein, M., Bouferguene, A., Fernando, S. (2010). “Artificial neural network model for cost estimation: City of Edmonton’s water and sewer installation services.” J. Constr. Eng. Manage., 136(7), 745–756.
An, S. H., Kim, G. H., and Kang, K. I. (2007). “A case-based reasoning cost estimating model using experience by analytic hierarchy process.” Build. Environ., 42(7), 2573–2579.
Arditi, D., and Tokdemir, O. B. (1999). “Comparison of case-based reasoning and artificial neural networks.” J. Comput. Civ. Eng., 13(3), 162–169.
Ashworth, A., and Skitmore, M. (1982). “Accuracy in estimating.”, Chartered Institute of Building, London.
Association for the Advancement of Cost Engineering International (AACEI). (2011). “cost estimate classification system.” Recommended Practice No. 17R-97, Association for the Advancement of Cost Engineering International, Morgantown, WV.
Beynon, M. J. (2004). “Stability of continuous value discretisation: An application within rough set theory.” Int. J. Approx. Reason., 35(1), 29–53.
CalTrans. (2007). “Project Development Cost Estimates.” Chapter 20, Project development procedures manual, California Dept. of Transportation, Sacramento, CA.
Carr, R. I. (1989). “Cost-estimating principles.” J. Constr. Eng. Manage., 115(4), 545–551.
Cheng, M. Y., Tsai, H. C., and Hsieh, W. S. (2009). “Web-based conceptual cost estimates for construction projects using evolutionary fuzzy neural inference model.” Automat. Constr., 18(2), 164–172.
Chernick, M. R. (1999). Bootstrap methods: A practitioner’s guide, Wiley Series in Probability and Statistics, Wiley-Interscience, New York.
Chou, J. S. (2009). “Web-based CBR system applied to early cost budgeting for pavement maintenance project.” Expert Syst. Appl., 36(2), 2947–2960.
Cong, R., Wang, X., Li, K., and Yang, N. (2010). “New method for discretization of continuous attributes in rough set theory.” J. Syst. Eng. Electron., 21(2), 250–253.
Doğan, S. Z., Arditi, D., and Günaydin, H. M. (2006). “Determining attribute weights in a CBR model for early cost prediction of structural systems.” J. Constr. Eng. Manage., 132(10), 1092–1098.
Doğan, S. Z., Arditi, D., and Günaydin, H. M. (2008). “Using decision trees for determining attribute weights in a case-based model of early cost prediction.” J. Constr. Eng. Manage., 134(2), 146–152.
Fayek, A. R., and Flores, J. R. R. (2010). “Application of fuzzy logic to quality assessment of infrastructure projects at conceptual cost estimating stage.” Can. J. Civ. Eng., 37(8), 1137–1147.
Forbes, D., Smith, S., and Horner, M. (2008). “Tools for selecting appropriate risk management techniques in the built environment.” Constr. Manage Econ., 26(11), 1241–1250.
Huang, C. C., and Tseng, T. L. (2004). “Rough set approach to case-based reasoning application.” Expert Syst. Appl., 26(3), 369–385.
Huang, R. Y., Mao, I. S., and Lee, H. K. (2010). “Exploring the deterioration factors of RC bridge decks: A rough set approach.” Comput. Aided Civ. Infrastruct. Eng., 25(7), 517–529.
Korea Development Institute (KDI). (2008). Fifth revised standard guidance for the preliminary feasibility study on road and railway projects, Korea Development Institute, Seoul, South Korea.
Korea Expressway Corporation (KEC). (2009). “Road planning and geometric structure.” Chapter 1, Road design manual, Korea Expressway Corporation, Seongnam, South Korea.
Kim, G. H., An, S. H., and Kang, K. I. (2004). “Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning.” Build. Environ., 39(10), 1235–1242.
Kim, K. J., and Kim, K. (2010). “Preliminary cost estimation model using case-based reasoning and genetic algorithms.” J. Comput. Civ. Eng., 24(6), 499–505.
Kim, M., Lee, S., Woo, S., and Shin, D. H. (2012). “Approximate cost estimating model for river facility construction based on case-based reasoning with genetic algorithms.” KSCE J. Civ. Eng., 16(3), 283–292.
Kinney, C. L., and Soubiran, N. (2004). “Interactive roadmap to conceptual cost estimating.” Cost Eng., 46(9), 31–40.
Koo, C. W., Hong, T. H., and Hyun, C. T. (2011). “The development of a construction cost prediction model with improved prediction capacity using the advanced CBR approach.” Expert Syst. Appl., 38(7), 8597–8606.
Leake, D. B. (1996). Case-based reasoning: Experiences, lessons & future directions, AAAI Press/MIT Press, Menlo Park, CA.
Li, Y., Shiu, S. C. K., and Pal, S. K. (2006). “Combining feature reduction and case selection in building CBR classifiers.” IEEE Trans. Knowl. Data Eng., 18(3), 415–429.
Lin, R. H., Wang, Y. T., Wu, C. H., and Chuang, C. L. (2009). “Developing a business failure prediction model via RST, GRA and CBR.” Expert Syst. Appl., 36(2 PART 1), 1593–1600.
Liu, G., and Yu, W. (2009). “Smart case-based indexing in worsted roving process: Combination of rough set and case-based reasoning.” Appl. Math. Comput., 214(1), 280–286.
Liu, L., and Zhu, K. (2007). “Improving cost estimates of construction projects using phased cost factors.” J. Constr. Eng. Manage., 133(1), 91–95.
Lu, P., Zheng, Y., Lu, J., and Fu, H. (2006). “New method of the discretization of rough set’s continuous attributes based on the importance of the attributes.” J. Comput. Inf. Syst., 2(3), 1103–1110.
Lucko, G., and Mitchell, Z. W. (2010). “Quantitative research: Preparation of incongruous economic data sets for archival data analysis.” J. Constr. Eng. Manage., 136(1), 49–57.
Lucko, G., and Rojas, E. M. (2010). “Research validation: Challenges and opportunities in the construction domain.” J. Constr. Eng. Manage., 136(1), 127–135.
Mafakheri, F., Dai, L., Slezak, D., and Nasiri, F. (2007). “Project delivery system selection under uncertainty: Multicriteria multilevel decision aid model.” J. Manage. Eng., 23(4), 200–206.
Ministry of Land, Transport & Maritime Affairs (MLTM). (2011). Road operation handbook 2011, Korea Ministry of Land, Transport & Maritime Affairs, Gwacheon, South Korea.
Oberlender, G. D., and Trost, S. M. (2001). “Predicting accuracy of early cost estimates based on estimate quality.” J. Constr. Eng. Manage., 127(3), 173–182.
Pawlak, Z. (1982). “Rough sets.” Int. J. Comput. Inf., 11(5), 341–356.
Pawlak, Z. (1998). “Rough set theory and its applications to data analysis.” Cybernet Syst., 29(7), 661–688.
Pawlak, Z. (2002). “Rough set theory and its applications.” J. Telecommun. Inf. Technol., 7–10.
Pheng, L. S., and Hongbin, J. (2006). “Analysing ownership, locational and internalization advantages of Chinese construction MNCs using rough sets analysis.” Constr. Manage. Econ., 24(11), 1149–1165.
Salamó, M., and López-Sánchez, M. (2011). “Rough set based approaches to feature selection for Case-Based Reasoning classifiers.” Pattern Recognit. Lett., 32(2), 280–292.
Schexnayder, C. J., Weber, S. L., and Fiori, C. (2003). Project cost estimating: A synthesis of highway practice, National Cooperative Highway Research Program, Transportation Research Board, Washington, DC.
Soibelman, L., and Kim, H. (2002). “Data preparation process for construction knowledge generation through knowledge discovery in databases.” J. Comput. Civ. Eng., 16(1), 39–48.
Sonmez, R. (2004). “Conceptual cost estimation of building projects with regression analysis and neural networks.” Can. J. Civ. Eng., 31(4), 677–683.
Sonmez, R. (2008). “Parametric range estimating of building costs using regression models and bootstrap.” J. Constr. Eng. Manage., 134(12), 1011–1016.
Sonmez, R., and Ontepeli, B. (2009). “Predesign cost estimation of urban railway projects with parametric modeling.” J. Civ. Eng. Manage., 15(4), 405–409.
Tam, C. M., Tong, T. K. L., and Chan, K. K. (2006). “Rough set theory for distilling construction safety measures.” Constr. Manage. Econ., 24(11), 1199–1206.
Trost, S. M., and Oberlender, G. D. (2003). “Predicting accuracy of early cost estimates using factor analysis and multivariate regression.” J. Constr. Eng. Manage., 129(2), 198–204.
Walczak, B., and Massart, D. L. (1999). “Rough sets theory.” Chemom. Intell. Lab. Syst., 47(1), 1–16.
Watson, I. (1997). Applying case-based reasoning: Techniques for enterprise systems, Morgan Kaufmann, San Francisco.
Watson, I. (1999). “Case-based reasoning is a methodology not a technology.” Knowl.-Based Syst., 12(5–6), 303–308.
Yau, N. J., and Yang, J. B. (1998). “Case-based reasoning in construction management.” Comput. Aided Civ. Infrastruct. Eng., 13(2), 143–150.
Yu, W. D. (2006). “PIREM: A new model for conceptual cost estimation.” Constr. Manage. Econ., 24(3), 259–270.
Yu, W. D., and Skibniewski, M. J. (2010). “Integrating neurofuzzy system with conceptual cost estimation to discover cost-related knowledge from residential construction projects.” J. Comput. Civ. Eng., 24(1), 35–44.
Zadeh, L. A. (1965). “Fuzzy sets.” Inf. Control, 8(3), 338–353.
Zhang, X. H., Deng, Z. H., Liu, W., and Cao, H. (2013). “Combining rough set and case based reasoning for process conditions selection in camshaft grinding.” J. Intell. Manuf., 24(2), 211–224.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 140 • Issue 1 • January 2014
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Nov 3, 2012
Accepted: May 28, 2013
Published online: May 30, 2013
Published in print: Jan 1, 2014
Discussion open until: Jan 19, 2014
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.