Original scientific paper

https://doi.org/10.14256/JCE.2738.2019

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; prestressed concrete bridges; machine learning; construction costs

Hrčak ID:

252427

URI

https://hrcak.srce.hr/252427

Publication date:

10.2.2021.

Article data in other languages: croatian

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