Engineering Commons^™

Predicting Construction Labor Productivity With Bayesian Belief Networks, Ayoub Hazrati

Department of Construction Engineering and Management: Dissertations, Theses, and Student Research

Construction labor productivity plays an important role in labor intensive projects. Therefore, increasing construction labor productivity is a vital task to decrease a project’s cost (time). The primary goal of this research is to investigate the feasibility of developing a comprehensive causal model that can predict construction labor productivity for various project’s situations, such as existence of “Adverse Weather,” “Changes,” “Working Overtime,” etc., while considering uncertainty. It is found that Bayesian Belief Networks (BBNs) is the best approach that can model causal relationships among different factors while considering uncertainty, simultaneously.

Developing a BBNs model requires to extract its structure and, …