Returning Green Through Engineering: Utilization of Monte Carlo Simulation in Assessing Potential Lifetime Value of Conventional and Sustainable Building
S Mark Kenneth M. Lindo1, Dante L. Silva2

1S Mark Kenneth M. Lindo, Graduate, School of Studies, Mapua University.
2Dante L. Silva, Department of Mathematics, Mapua University.
Manuscript received on 02 June 2019 | Revised Manuscript received on 27 June 2019 | Manuscript Published on 04 July 2019 | PP: 130-138 | Volume-8 Issue-1S4 June 2019 | Retrieval Number: A10260681S419/2019©BEIESP
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Abstract: Entering into the age of information where you can quickly access almost anything that happens globally has proven itself beneficial. This study was conducted to assess the performance of conventional buildings and sustainable or green buildings in terms of their potential lifetime value. By analyzing the standard specification of green buildings, people can have a better understanding of this innovation. Data gathered through statistics, literature review, and evaluation from companies, will determine the quantitative information for the creation of the Mathematical Model by the use of Monte Carlo simulation. The Monte Carlo simulation was executed using a software to provide histograms. In order to create scenarios to produce various results, a What-If analysis was also conducted. Consequently, evaluation and analysis of the costs of conventional and green building, gave way for the researcher to make a conclusion with regards to their efficiency. The validity of the results was determined based on anecdotal information by professionals who practiced building sustainable infrastructure. Nevertheless, this research serves as a pilot study to identify the breakeven points between conventional and green buildings. Furthermore, the researcher believes that a deeper enhancement of the data might yield to more accurate results.
Keywords: Conventional Building, Green Building, Mathematical Model, Monte Carlo Simulation.
Scope of the Article: Simulation Optimization and Risk Management