Masonry Productivity Forecasting Model

Abstract

For the most part, existing productivity forecasting models are based on extrapolating from historical data instead of considering the effects of project‐related factors. These factors can change daily and can significantly affect the productivity of a labor‐intensive activity. This paper describes a statistical model developed to forecast the productivity of masonry activities. The model is an additive regression model and is based on data collected from 11 masonry projects. The model was tested by predicting the productivity of the 11 projects, with seven of the 11 being predicted within 10% of the actual productivity. This is noteworthy, given that the projects included a number of different masonry activities and types of facilities. Other analyses of the model indicate that the model is statistically valid and reflects what would be expected. Construction managers could easily use the model to estimate the labor requirements for a project and then to better manage the project as it progresses. Other labor‐intensive activities could be modeled using the same methodology.