Reducing Data-Collection Efforts for Conceptual Cost Estimating at a Highway Agency

Abstract

Data-driven models using historical project attributes to estimate future construction costs, such as multiple-regression analysis and artificial neural networks are both proven techniques that highway agencies could adopt for conceptual cost estimating. This research found literature using those techniques has been solely focused on estimating model performance with little to no attention to the level of effort required to conduct the conceptual estimate. It is commonly believed using more input data enhances estimate accuracy. However, this paper finds for the highway agency studied that using more input variables than necessary in the conceptual estimate does not improve estimate accuracy. Conceptual estimates using the minimum amount of input data to produce an estimate with a reasonable level of confidence is more cost effective. This paper quantifies the effort expended to undertake conceptual estimates using data from a highway agency and concludes that input variables that have a large influence on the final predicted cost and require a low amount of effort are desired in data-driven conceptual cost-estimating models for the agency studied.