2019 CSCE Annual Conference - Laval (Greater Montreal) Conference
Dr. Hani Alzraiee, California Polytechnic State University
This paper proposes a framework to conduct a quantity take-off (QTO) and cost estimate using a Building Information Model (BIM). The method addresses the cost uncertainty associated with the detailed information that defines the design geometry and properties (BIM objects). For example, estimating a concrete wall is impacted by parameters subjectively defined and quantified by construction estimators. Wall height, reinforcing, and block-outs for openings all affect the price per unit area of a concrete wall, these factors are not captured by current BIM estimating practices. In addition, cost estimators have little experience in utilizing and leveraging information within semantic-rich information models. This is due to the lack of available tools that address detailed QTO and cost estimation using a BIM platform. Parameters impacting BIM objects are considered a source of uncertainty in the cost estimate, therefore they should be identified and quantified.
A system which assists the estimators to conduct a QTO and cost estimate within the BIM environment is developed. This system addresses the BIM model generated quantities, BIM object parameters, and cost of scope not captured in BIM. The system consists of five modules 1) BIM representing design objects; 2) project costs classification module; 3) object parameters library; 4) computation platform; and 5) updating the estimate model and parameters during construction. The BIM represents the design objects with their properties. For each object, the parametric factors impacting cost are identified, quantified and mapped into their objects. Since not all project costs are represented within the BIM model, project cost drivers are classified as internal and external to the BIM. The computation platform consisted of Autodesk Revit to document quantities, Autodesk Dynamo to associates parameters with quantities, and excel to assign external costs. The last step involved a real-time update of the object cost parameters to calibrate the system for project controls and future use.
The developed method was tested using real residential project data from the construction sector. The early results showed using BIM to conduct project cost estimating without considering the object’s parameters that impact cost is a real source of risk and can lead to inaccurate project cost estimate, and hence misleading project controls metrics.