Framework for the validation of simulation-based productivity analysis: focused on curtain wall construction process. Issue 2 (17th February 2017)

• Record Type: Journal Article
• Authors: Han, Seungwoo; Ko, Yong-Ho; Hong, Taehoon; Koo, Choongwan; Lee, Sangyoub
• Is Part Of: Journal of civil engineering and management. Volume 23:Issue 2(2017)
• Page Start: 163
• Page End: 172
• Publication Date: 2017-02-17
• Subjects: simulation-based productivity -- WebCYCLONE -- curtain wall construction -- statistical analysis -- T-test; Civil engineering -- Periodicals; Civil engineering; Periodicals; 624.05
• DOI: 10.3846/13923730.2014.992468
• Languages: English
• ISSNs: 1822-3605

Abstract

Abstract: As construction projects have become more complicated in design and construction, it is necessary to establish the construction operational plans in advance. However, there were some limitations in analyzing construction productivity due to the difficulty of collecting accurate data. To address this challenge, this study aimed to develop the framework for the validation of simulation-based productivity analysis, which consisted of three measures: (i) validation of the measured productivity data as target variable; (ii) validation of the measured duration data as input variable; and (iii) validation of the simulation model compared to the actual construction process. To verify the feasibility of the proposed framework, this study focused on the curtain wall construction project of "S" office building as a case study. The T-test was applied to investigate the statistical difference between the measure and simulated productivity. It was determined that the significance level α in the T-test for the unloading process was 0.136 with 95% confidence interval; the lifting process, 0.106; and the installing process, 0.311. As a result, there was no significant difference between the measured and simulated productivity. The proposed framework could enable executives and managers in charge of project planning and scheduling to accurately predict the simulation-based productivity.