Productivity plays a key role in the successful delivery of construction projects, and it has long been a major research interest within the construction engineering domain. Previous research on the identification of factors influencing productivity often focused on labour intensive activities while ignoring equipment intensive activities, for which equipment is the driver of productivity. Therefore, there is a gap in the research on the identification of factors that affect the productivity of equipment intensive activities. Existing predictive models of activity level productivity often predict construction labour productivity (CLP), which is a single-factor productivity measure for construction activities. However, CLP is not an appropriate measure of productivity for equipment intensive activities because it does not provide any information regarding the resource input that is the driver of productivity for these activities. Determining multi-factor productivity (MFP) using labour, equipment, and material as the three model inputs results in a more comprehensive prediction of productivity than CLP. However, there is a gap in the research on developing a predictive model of productivity for equipment intensive activities that will determine the MFP measure of these activities. Existing construction productivity models are either static in nature or not capable of capturing the subjective uncertainty of some of the factors that influence construction productivity (e.g., crew motivation). Fuzzy system dynamics (FSD) is an appropriate technique for modeling construction productivity since it captures the dynamism of construction projects while simultaneously addressing the subjective and probabilistic uncertainty of the factors that influence construction productivity. However, there is a gap in the research on developing computational methods for the implementation of fuzzy arithmetic operations in FSD models. The main contributions of this research are threefold. It identifies the factors that affect the productivity of equipment intensive activities; it enhances the FSD technique by developing computational methods for the implementation of fuzzy arithmetic in these models; and it develops a predictive model of construction productivity for determining the MFP measure of equipment intensive activities using the FSD technique.
|Qualification||Doctor of Philosophy|
|Award date||22 Dec 2017|
|Publication status||Published - 22 Dec 2017|