Construction cost estimation of reinforced and prestressed concrete bridges using machine learning

Miljan Kovacevic*, Nenad Ivaniševic, Predrag Petronijevic, Vladimir Despotovic

*Corresponding author for this work

Research output: Contribution to specialist publicationArticle

6 Citations (Scopus)

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.

Original languageEnglish
Pages1-13
Number of pages13
Volume73
No.1
Specialist publicationGradevinar
DOIs
Publication statusPublished - 2021
Externally publishedYes

Keywords

  • Construction costs
  • Machine learning
  • Prestressed concrete bridges
  • Reinforced concrete bridges

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