Productivity modeling of precast concrete installation using multiple regression analysis

• Main Authors: Najafi, Ali, Kong, Robert Tiong Lee
• Other Authors: School of Civil and Environmental Engineering
• Format: Article
• Language: English
• Published: 2016
• Subjects: construction productivity; precast concrete erection; productivity estimation; multiple regression analysis
• Online Access: https://hdl.handle.net/10356/81565; http://hdl.handle.net/10220/39579; http://www.arpnjournals.com/jeas/volume_06_2015.htm
• Institution: Nanyang Technological University

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.