Probabilistic model development for estimating construction labor productivity optimization integrating with fuzzy logic approach systems

Construction labour productivity is a foremost tool used for data assessing, planning, budgeting and establishment of construction project. Influence of multi variation factors results in decrease of labour productivity in construction field. Still we are dependent on traditional technique comprises with reference published data or on the experience of estimators so as to estimate the construction labour productivity. The objective of this research is to recognize and map the association between identified factors affecting the construction labour productivity and individual productive rates through a systematic engineering model. This process comprises with calculation of productivity, collection of productivity information and using that information for designing construction model. Also, it intends to build up an optimized probabilistic model for construction industry. Initially, the raw data for instance labour cost, capital cost, and energy consumption has been considered as input so as to compute objective function and total productive factor. The membership function is developed and employed in fuzzy optimization algorithm to optimize the productivity rate in construction. The results through anticipated model prove to be more effective model with reasonable generalization capabilities compared to existing traditional work. Furthermore, this paper provides an insight of probabilistic model comprising internal in addition to external variable factors such as supervision, work rules, government rules and public labour unions.

Keywords:

Fuzzy optimization algorithm , labour productivity , membership function , objective function , total productivity factor