Early Highway Construction Cost Estimation: Selection of Key Cost Drivers

Abstract

Cost estimates in the early stages of project development are essential for making the right deci-sions, but they are a huge challenge and risk for owners and potential contractors due to limited information about the characteristics of the future highway project. While previous studies were mainly focused on achieving the highest possible estimation accuracy, this paper aims to propose cost estimation models that can provide satisfactory accuracy with the least possible effort, and to compare the perspectives of owners and contractors, as the key stakeholders on projects. To de-termine cost drivers (CDs) which have a high influence on highway construction costs and re-quire low effort for their establishment, a questionnaire survey was conducted. Based on the key stakeholders’ perceptions and collected data set, cost estimation models were developed using multiple regression analysis, artificial neural networks, and XGBoost. The results show that rea-sonable cost estimation accuracy can be achieved with a relatively low effort for 3 CDs for the owners' perspective and 5 CDs for the contractors' perspective. Additional inclusion of input CDs in models does not necessarily imply an increase in accuracy. Also, the questionnaire results show that owners are more concerned about environmental issues, while contractors are more con-cerned about the possible changes in resource prices (especially after recent high increases caused by COVID-19 and the Russia-Ukraine war). These findings can help owners and potential con-tractors in intelligent decision-making in the early stages of future highway construction projects.