Cost simulation in an item-based project involving construction engineering and management |
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Authors: | Jui-Sheng Chou |
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Affiliation: | Department of Construction Engineering, National Taiwan University of Science and Technology, 43 Sec.4, Keelung Rd., Taipei, 106, Taiwan |
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Abstract: | Despite the extensive use of simulation in management, the continuous simulation model for cost estimation remains unexploited, especially for construction engineering and management. This study introduces streamlining Monte Carlo simulation procedures with evaluation of stochastic processes and input probability distribution selection via hypothesis testing, and specification of correlations between simulated variates. By using self-developed algorithms and a spreadsheet-add-on program, this investigation uses historical construction projects as case study data to create an early-stage cost distribution for budget allocation. While establishing the applicability of the proposed simulation procedures, this study demonstrates that the simulated cost results present superior simulation accuracy in addition to separating the principal work items and unit price component model. Generally, the precision and absolute error rates fall into acceptable ranges when the proposed systematic simulation procedures are adopted. The cost simulation approach offers a simplified decision tool for fairly assessing construction cost and uncertainties based on the experienced judgment of project managers. |
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Keywords: | BCIS, Building Cost Information Service CDF, cumulative distribution function K-S, Kolmogorov-Smirnov LCGs, linear congruential generators MAPE, mean absolute percentage error MCS, Monte Carlo simulation MLNRS, multivariate lognormal random simulation MNRS, multivariate normal random simulation MPE, mean percentage error NORTA, NORmal To Anything NTD, New Taiwan Dollar PC, Pearson's Chi-square PDF, probability density function PEM, probabilistic estimation method PWI, principal work items RICS, Royal Institute of Chartered Surveyors SBS, stochastic budget simulation SD, standard deviation SDPE, standard deviation percentage error TPC, total project cost derived by summing work items costs TPCS, total project cost derived from sum of item quantity multiplied by unit price |
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