Productivity modeling of precast concrete installation using multiple regression analysis
Kong, Robert Tiong Lee
Date of Issue2015
School of Civil and Environmental Engineering
Precast concrete products are generally used to shorten project duration and provide higher quality and more sustainable construction projects. There are many factors affecting productivity in precast concrete construction sites and there is a lack of research in terms of estimation tools for prediction of precast installation times for different components that are widely used in precast projects (walls, columns, beams, and slabs). Therefore, this study was designed to study the erection of different precast panels and develop a regression model to estimate the installation times based on the selected factors (extracted from literature, interviews, and site visits) involved in different stages of installation process namely preparation, lift, and fixing activities. The results showed the appropriateness of the model to be used by site managers and general estimators for their planning purposes. This study contributes to the construction management knowledge by providing simple but effective models to predict the installation times of precast elements. Significant factors involved in each stage of precast installation were discussed and limitations and recommendations for future research were presented.
construction productivity; precast concrete erection; productivity estimation; multiple regression analysis
ARPN Journal of Engineering and Applied Sciences
© 2015 Asian Research Publishing Network. This paper was published in ARPN Journal of Engineering and Applied Sciences and is made available as an electronic reprint (preprint) with permission of Asian Research Publishing Network. The published version is available at: [http://www.arpnjournals.com/jeas/volume_06_2015.htm]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.