A Conceptual Study on the Monte Carlo Simulation for Cost Forecasting in the Green Building Project
Keywords:Monte Carlo, Risk Analysis, Cost Prediction, Qualitative approach, Quality of life
Monte Carlo Simulation is a mathematical technique that generates random variables for modelling risk. This technique is suitable and benefits to the various client such as public and private sector to evaluate the realistic costing proposed by the Quantity Surveyor. Through this approach, quality of life received by the client in investing budget without waste of propose funding in the construction project. The methodology used is a qualitative approach consist of case study and document analysis. The result shows through Monte Carlo simulation, can predict the worst return from the accuracy of the estimation and given absolute confidence for project development. Keywords: Monte Carlo, Risk Analysis, Cost Prediction, Qualitative Approach eISSN: 2398-4287 © 2020. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/e-bpj.v5i13.2101
Ali Touran, R. L. (2006). Modelling Cost Escalation in Large Infrastructure Projects. Journal of Construction Engineering and Management, 853-860.
An, S. H., & Kang, K. I. (2005). A study on predicting construction cost of apartment housing using experts' knowledge at the early stage of projects. Journal of Architectural Institute of Korea, 21(6), 81–88.
Attalla, M., & Hegazy, T. (2003). Prediction cost deviation in reconstruction project: Artificial neural networks versus regression. Journal of Construction Engineering and Management, ASCE, 129(4), 405–411.
Balcombe, K.G. & Smith, L.E. (1999), “Refining the use of Monte Carlo techniques for risk analysis in project planning”, The Journal of Development Studies, 36(2), 113-135.
Bennett, J. & Ormerod, R.N. (1984), “Simulation applied to construction projects”, Construction Management and Economics, 2(3), 225-263
Chen, G., 2013. Monte Carlo simulation of π and the discussion of variance reduction techniques. J. Convergence Inform. Technol., 8, 850-859.
Choi, J., & Ryu, H.-G (2015). Statistical analysis of construction productivity for highway pavement operation. KSCE Journal of Civil Engineering. 19(5), 1193-1202.
Faris, K. R., & D. Patterson. (2007). Managing Risk in the Project Portfolio. Conference Paper, Newtown Square: Project Management Institute.
El-Sadek, A. (2010) Monte Carlo Approach to Developing a Water Quality Process- Factor. International Journal of Water Resources and Environmental Management, 1, 97-104.
Grinstead, C.M. & Snell, J.L. (2012), Introduction to Probability, American Mathematical Society, Providence.
Hertz, D. B. (1964). Risk Analysis in Capital Investment.
Hongxiang, C., & Wei, C. (2013). Uncertainty Analysis by Monte Carlo Simulation in a Life Cycle Assessment of Water-Saving Project in Green Buildings. Information Technology Journal, 12(13), 2593-2598.
Jahangirian, M., Eldabi, T., Naseer, A., Stergioulas, L.K. & Young, T. (2010), “Simulation in manufacturing and business: a review”, European Journal of Operational Research, 203(1), 1-13.
Khedr, M.K. (2006), “Project risk management using Monte Carlo simulation”, 50th Annual Meeting, AACE International, Las Vegas, NV.
Kwak, Y.H. & Ingall, L. (2007), “Exploring Monte Carlo simulation applications for project management”, Risk Management, 9(1), 44-57.
Liu, N. & Q. Zhang. (2012). Asymmetric stochastic volatility model estimation using improved markov chain Monte Carlo method. Journal Convergence Information Technology., 7, 179-186.
Lorance, R.B. & Robert, V.W. (1999), “Basic techniques for analyzing and presentation of cost risk analysis”, AACE International Transactions, Association for the Advancement of Cost Engineering.
Palisade Corporation. (2010). Palisade.
Marseguerra, M. & Zio, E. (2000), “Optimizing maintenance and repair policies via combination of genetic algorithms and Monte Carlo simulation”, Reliability Engineering and System Safety, 68, 69-83.
Mun, J. (2015). Risk Simulator. Dublin,California,U.S.A: Real Options Valuation.
Marquez, C., Heguedas, S., & Iung, B. (2005). Monte Carlo-based assessment of system availability. A case study for cogeneration plants. Reliability Engineering & System Safety, 88(3), 273-289.
Shukur, F. M. H. N. M. N. A. A. H. a. A. S. (2016). Feasibility Study and Economic Assessment in Green Building Projects. Procedia Social and Behavioral Science, 56-64.
Wilson, H. (2015). Estimate Project Cost Contingency Required using Monte Carlo Simulation. https://www.katmarsoftware.com/articles/project-cost-contingency.htm.
Bishop, K., & Said, I., (2017). Challenges of Participatory Qualitative Research in a Malaysian and Australian Hospital. Asian Journal of Environment-Behaviour Studies, 2(4), 1-11.
Collado, S., & Corraliza, J., A. (2017). Children’s Perceived Restoration and Pro-Environmental Beliefs. Journal of ASIAN Behavioural Studies, 2(2), 1-12.
Fachinger, J., den Exter, M., Grambow, B., Holgerson, S., Landesmann, C., Titov, M., et al. (2004). Behavior of spent HTR fuel elements in aquatic phases of repository host rock formations, 2nd International Topical Meeting on High Temperature Reactor Technology. Beijing, China, paper #B08.
Mehdi K., & Koorosh, A. (2015). Achievement to Environmental Components of Educational Spaces for Iranian Trainable Children with Intellectual Disability. Procedia - Social and Behavioral Sciences, 201, 9-18.
Mettam, G. R., & Adams, L. B. (1999). How to prepare an electronic version of your article. In B. S. Jones & R. Z. Smith (Eds.), Introduction to the electronic age (pp. 281–304). New York: E-Publishing Inc.
How to Cite
eISSN: 2398-4287 © Year. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia.