Construction cost estimation of reinforced and prestressed concrete bridges using machine learning
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 costsSource:
Časopis Građevinar, 2021, 73Publisher:
- Građevinar
Funding / projects:
- Bezbednost hrane, hemijski kontaminanti i integrativna procena rizika (RS-MESTD-MPN2006-2010-20212)
Collections
Institution/Community
GraFarTY - JOUR AU - Kovačević, Miljan AU - Ivanišević, Nenad AU - Petronijević, Predrag AU - Despotović, Vladimir PY - 2021 UR - https://grafar.grf.bg.ac.rs/handle/123456789/2336 AB - 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 PB - Građevinar T2 - Časopis Građevinar T1 - Construction cost estimation of reinforced and prestressed concrete bridges using machine learning VL - 73 DO - 10.14256/JCE.2738.2019 ER -
@article{ author = "Kovačević, Miljan and Ivanišević, Nenad and Petronijević, Predrag and Despotović, Vladimir", year = "2021", 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", publisher = "Građevinar", journal = "Časopis Građevinar", title = "Construction cost estimation of reinforced and prestressed concrete bridges using machine learning", volume = "73", doi = "10.14256/JCE.2738.2019" }
Kovačević, M., Ivanišević, N., Petronijević, P.,& Despotović, V.. (2021). Construction cost estimation of reinforced and prestressed concrete bridges using machine learning. in Časopis Građevinar Građevinar., 73. https://doi.org/10.14256/JCE.2738.2019
Kovačević M, Ivanišević N, Petronijević P, Despotović V. Construction cost estimation of reinforced and prestressed concrete bridges using machine learning. in Časopis Građevinar. 2021;73. doi:10.14256/JCE.2738.2019 .
Kovačević, Miljan, Ivanišević, Nenad, Petronijević, Predrag, Despotović, Vladimir, "Construction cost estimation of reinforced and prestressed concrete bridges using machine learning" in Časopis Građevinar, 73 (2021), https://doi.org/10.14256/JCE.2738.2019 . .