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Published: Građevinar 73 (2021) 1
Paper type: Original scientific paper
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Construction cost estimation of reinforced and prestressed concrete bridges using machine learning

Miljan Kovačević, Nenad Ivanišević, Predrag Petronijević, Vladimir Despotović

Abstract

Seven state-of-the-art machine learning techniques for estimation of construction costs of reinforced-concrete and prestressed concrete bridges are investigated in this paper, including artificial neural networks (ANN) and ensembles of ANNs, regression tree ensembles (random forests, boosted and bagged regression trees), support vector regression (SVR) method, and Gaussian process regression (GPR). A database of construction costs and design characteristics for 181 reinforced-concrete and prestressed-concrete bridges is created for model training and evaluation.

Keywords
reinforced concrete bridges, machine learning, prestressed concrete bridges, construction cost

HOW TO CITE THIS ARTICLE:

Kovačević, M., Ivanišević, N., Petronijević, P., Despotović, V.: Construction cost estimation of reinforced and prestressed concrete bridges using machine learning, GRAĐEVINAR, 73 (2021) 1, pp. 1-13, doi: https://doi.org/10.14256/JCE.2738.2019

OR:

Kovačević, M., Ivanišević, N., Petronijević, P., Despotović, V. (2021). Construction cost estimation of reinforced and prestressed concrete bridges using machine learning, GRAĐEVINAR, 73 (1), 1-13, doi: https://doi.org/10.14256/JCE.2738.2019

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This paper is licensed under a Creative Commons Attribution 4.0 International License.
Authors:
Miljan Kovacevic
Miljan Kovačević
University of Prishtina, Kosovska Mitrovica
Faculty of Technical Sciences
Ivanisevic Nenad WEB
Nenad Ivanišević
University of Belgrade
Faculty of Civil Engineering
Predrag Petronijevic WEB
Predrag Petronijević
University of Belgrade
Faculty of Civil Engineering
VladimirD WEB
Vladimir Despotović
University of Luxembourg
Department of Computing