An analysis of factors affecting labor productivity in masonry construction

Existing forecasting models for labor-intensive construction activities are inadequate because they fail to consider the project-related factors that directly affect daily productivity. The primary objective of this study is to identify the factors that significantly affect the daily productivity of masonry crews and to quantify the effects of the identified factors. Data were collected from 11 masonry projects using standardized data collection forms and procedures developed for this study. The factors of type of building, physical element of the building, method of construction, design requirements, weather, and crew size were identified as the significant factors. Each day was classified for each of the factors, and disrupted days were removed from the data set. An additive regression model was developed using the identified factors as the independent variables and the daily crew productivity as the dependent variable. The model coefficient calculated for each factor level indicates how the presence of that condition causes the productivity to deviate from the expected productivity for a set of standard conditions. The model was evaluated based on its statistical significance and usability. The model not only proved to account for 37 per cent of the variability in the data but also to be a useful and accurate predictor of masonry productivity. The disruptions were classified and analyzed to determined their effects. This analysis indicated that the disruptions increased the work-hours needed to complete the projects by 16 per cent. This study proves that the factors that significantly affect masonry productivity can be identified and quantified. The identified factors can then be used to create a valid, useful forecasting model.