Productivity is one of the most important elements to manage construction projects especially with regards to the prediction of the activities’ durations. Uncertainty is an entrenched characteristic of most construction projects. Most research works in simulating construction productivity have focused predominantly on modeling and have neglected to study the effect of subjective variables on productivity of construction process. The unique nature of construction projects and uncertainty of the construction processes lead to a need of new generation of models that utilizes the historical data. The presented research develops, using Fuzzy approach, a model to utilize, analyze, extract and find the hidden patterns of the project data sets to predict the construction process productivity. The engine depends on finding the relation between quantitative and qualitative variables, which affect the construction processes, and productivity. The methodology of this research consists of six steps: (1) Investigate the factors affecting the productivity (2) select the critical factors that affect the productivity; (3) build Fuzzy sets; (4) generate Fuzzy rules and models; (5) build Fuzzy knowledge base; and (6) validate the effectiveness of the built model to predict the construction process productivity. The developed model is validated and verified using case study with sound and satisfactory results, 90.65 % average validity percent. The developed research/engine benefits both researchers and practitioners because it provides robust model for construction processes and a tool to predict the productivity of construction processes.Non UBCUnreviewedFacultyOthe
