Early Highway Construction Cost Estimation: Selection of Key Cost Drivers

Nevena Simić (), Nenad Ivanišević, Đorđe Nedeljković, Aleksandar Senić, Zoran Stojadinović and Marija Ivanović
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Nevena Simić: Faculty of Civil Engineering, University of Belgrade, 11000 Belgrade, Serbia
Nenad Ivanišević: Faculty of Civil Engineering, University of Belgrade, 11000 Belgrade, Serbia
Đorđe Nedeljković: Faculty of Civil Engineering, University of Belgrade, 11000 Belgrade, Serbia
Aleksandar Senić: Faculty of Civil Engineering, University of Belgrade, 11000 Belgrade, Serbia
Zoran Stojadinović: Faculty of Civil Engineering, University of Belgrade, 11000 Belgrade, Serbia
Marija Ivanović: Faculty of Civil Engineering, University of Belgrade, 11000 Belgrade, Serbia

Sustainability, 2023, vol. 15, issue 6, 1-20

Abstract: Cost estimates in the early stages of project development are essential for making the right decisions, but they are a huge challenge and risk for owners and potential contractors due to limited information about the characteristics of a future highway project. Whereas 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 determine cost drivers (CDs) that have a high influence on highway-construction costs and require 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 reasonable cost-estimation accuracy can be achieved with relatively low effort for three CDs for the owners’ perspective and five 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, whereas contractors are more concerned 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 contractors in intelligent decision-making in the early stages of future highway-construction projects.

Keywords: cost estimation; cost driver; influence; effort; questionnaire; extreme gradient boosting; artificial neural networks; highway projects (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:6:p:5584-:d:1104249

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