Predicting Seismic Retrofit Construction Cost for Buildings with Framed Structures Using Multilinear Regression Analysis
Publication: Journal of Construction Engineering and Management
Volume 140, Issue 3
Abstract
Attempts to predict construction cost represent a problem of continual concern and interest to both practitioners and researchers. Such an attempt is presented here for the specific challenge of cost prediction when undertaking seismic retrofitting of existing structures. Using multilinear regression analysis, 14 independent variables were analyzed to develop parametric models for predicting the retrofit net construction cost (RNCC). Half of these variables have never previously been studied in the literature. The required data for this study were collected from 158 earthquake-prone public schools in Iran, each having a framed structure. The backward elimination (BE) regression technique was used to identify any variables that made a statistically significant contribution to the RNCC. The suitability of the BE technique for this identification was examined and demonstrated using a number of model-selection criteria. Rather surprisingly, building age and compliance with the earliest practiced seismic design code were found to be insignificant predictors of the RNCC. As reflected by the BE technique, the significant predictors were building total plan area, number of stories, structural type, seismicity, soil type, weight, and plan irregularity. The causal analysis performed between the RNCC and these variables showed that the first two variables have the greatest influence on the determination of the RNCC. The primary contribution to the construction industry is the introduction of a simple double-log cost-area model for predicting seismic retrofit construction cost. The introduced model enables engineering consultants, managers, and policy makers to simply predict this cost at the early planning and budgeting stage of seismic retrofit projects.
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Acknowledgments
The authors acknowledge the companies and organizations that participated in this research. The authors also thank Mr. T. Honarbakhsh and Professor A. A. Aghakouchak for their professional contribution to this study. With great gratitude, the first author would additionally like to acknowledge the financial support provided by Farasaz Industrial Group Ltd during the full term of this study.
References
Abu Hammad, A. A., Alhaj Ali, S. M., Sweis, G. J., and Sweis, R. J. (2010). “Statistical analysis on the cost and duration of public building projects.” J. Manage. Eng., 105–112.
Ashworth, A., and Skitmore, R. M. (1983). Accuracy in estimating. The Chartered Institute of Building, Ascot, Berkshire, U.K.
Attalla, M., and Hegazy, T. (2003). “Predicting cost deviation in reconstruction projects: Artificial neural networks versus regression.” J. Constr. Eng. Manage., 405–411.
Barnes, N. M. L. (1974). “Financial control of construction.” Control of engineering projects, S. H. Wearne, ed., Thomas Telford, London, 129–142.
Bromilow, F. J. (1969). “Contract time performance expectations and the reality.” Build. Forum, 1(3), 70–80.
Building, and Housing Research Centre. (1988). “Iranian code of practice for seismic resistant design of buildings.” Standard No. 2800, 1st Ed., Ministry of Housing and Urban Development, Tehran, Iran.
California Seismic Safety Commission. (1999). “Earthquake risk management: Mitigation success stories.”, Oakland, CA.
Chan, D. W. M., and Kumaraswamy, M. M. (1995). “A study of the factors affecting construction durations in Hong Kong.” Constr. Manage. Econ., 13(4), 319–333.
Chatterjee, S., and Hadi, A. S. (2006). Regression analysis by example, 4th Ed., Wiley-Interscience, Hoboken, NJ.
Chen, W. T., and Huang, Y.-H. (2006). “Approximately predicting the cost and duration of school reconstruction projects in Taiwan.” Constr. Manage. Econ., 24(12), 1231–1239.
Elhag, T. M. S., and Boussabaine, A. H. (1999). “Tender price estimation: Neural networks vs regression analysis.” Paper presented at the RICS Construction, Building and Real Estate Research Conf. (COBRA), Royal Institution of Chartered Surveyors (RICS), London, 114–123.
Federal Emergency Management Agency (FEMA). (1994). Typical costs for seismic rehabilitation of existing buildings (FEMA 156), Vol. I, 2nd Ed., Santa Monica, CA.
Freund, R. J., Wilson, W. J., and Sa, P. (2006). Regression analysis: Statistical modeling of a response variable, 2nd Ed., Elsevier Academic Press, Burlington, MA.
Goh, B. H. (1999). “An evaluation of the accuracy of the multiple regression approach in forecasting sectoral construction demand in Singapore.” Constr. Manage. Econ., 17(2), 231–242.
Gujarati, D. N., and Porter, D. C. (2010). Essentials of econometrics, 4th Ed., McGraw-Hill/Irwin, New York.
Günaydın, H. M., and Doğan, S. Z. (2004). “A neural network approach for early cost estimation of structural systems of buildings.” Int. J. Proj. Manag., 22(7), 595–602.
Hopkins, D. C., and Stuart, G. (2003). “Strengthening existing New Zealand buildings for earthquake: An analysis of cost benefit using annual probabilities.” Paper presented at the 2003 Pacific Conf. on Earthquake Engineering, New Zealand Society for Earthquake Engineering (NZSEE), Wellington, New Zealand, 72.
Hwang, S. (2009). “Dynamic regression models for prediction of construction costs.” J. Constr. Eng. Manage., 360–367.
Jafarzadeh, R. (2012). “Seismic retrofit cost modelling of existing structures.” Ph.D. dissertation, Univ. of Auckland, Auckland, New Zealand.
Lowe, D. J., Emsley, M. W., and Harding, A. (2006). “Predicting construction cost using multiple regression techniques.” J. Constr. Eng. Manage., 750–758.
Maqsood, S. T., and Schwarz, J. (2010). “Building vulnerability and damage during the 2008 Baluchistan earthquake in Pakistan and past experiences.” Seismol. Res. Lett., 81(3), 514–525.
Meduri, S. S., and Annamalai, T. R. (2013). “Unit costs of public and PPP road projects: Evidence from India.” J. Constr. Eng. Manage., 35–43.
Ogunlana, S., and Thorpe, A. (1987). “Design phase cost estimating: The state of the art.” Int. J. Constr. Manage. Technol., 2(4), 34–47.
Oppenheim, A. N. (1992). Questionnaire design, interviewing, and attitude measurement, Pinter, London.
Potangaroa, R. (1985). “The seismic strengthening of existing buildings for earthquakes.” Master of Architecture Dissertation, Victoria Univ., Wellington, New Zealand.
Shehab, T., Farooq, M., Sandhu, S., Nguyen, T.-H., and Nasr, E. (2010). “Cost estimating models for utility rehabilitation projects: Neural networks versus regression.” J. Pipeline Syst. Eng. Pract., 104–110.
Shrestha, P. P., and Mani, N. (2012). “Impact of design cost on design bid build project performance.” Proc., Construction Research Congress, ASCE, Reston, VA, 1570–1579.
Skitmore, R. M., and Ng, S. T. (2003). “Forecast models for actual construction time and cost.” Build. Environ., 38(8), 1075–1083.
Soutos, M., and Lowe, D. J. (2005). “ProCost—Towards a powerful early stage cost estimating tool.” Paper presented at the ASCE Int. Conf. on Computing in Civil Engineering, ASCE, Reston, VA.
Stoy, C., and Schalcher, H. R. (2007). “Residential building projects: Building cost indicators and drivers.” J. Constr. Eng. Manage., 139–145.
Studenmund, A. H. (2011). Using econometrics: A practical guide, 6th Ed., Pearson Education, Upper Saddle River, NJ.
Technical Criteria Codification, and Earthquake Risk Reduction Affairs Bureau. (2007). Instruction for seismic rehabilitation of existing buildings, Code No. 360, Vice Presidency for Strategic Planning and Supervision, Tehran, Iran.
U.S. Congressional Budget Office. (2007). “Potential cost savings from the pre-disaster mitigation program.”, Congressional Budget Office, Washington, DC. 〈http://www.cbo.gov/sites/default/files/cbofiles/ftpdocs/86xx/doc8653/09-28-disaster.pdf〉.
Williams, T. P. (1994). “Predicting changes in construction cost indexes using neural networks.” J. Constr. Eng. Manage., 306–320.
Williams, T. P. (2003). “Predicting final cost for competitively bid construction projects using regression models.” Int. J. Proj. Manage., 21(8), 593–599.
Williams, T. P., Lakshminarayanan, S., and Sackrowitz, H. (2005). “Analyzing bidding statistics to predict completed project cost.” Paper presented at the ASCE Int. Conf. on Computing in Civil Engineering, ASCE, Reston, VA.
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© 2013 American Society of Civil Engineers.
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Received: Jul 18, 2012
Accepted: Jun 5, 2013
Published online: Jun 7, 2013
Published in print: Mar 1, 2014
Discussion open until: May 12, 2014
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