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
TY - JOUR
AU - Kovačević, Miljan
AU - Despotović, Vladimir
AU - Petronijević, Predrag
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 Despotović, Vladimir and Petronijević, Predrag",
year = "2021",
url = "https://grafar.grf.bg.ac.rs/handle/123456789/2336",
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., Despotović, V.,& Petronijević, P. (2021). Construction cost estimation of reinforced and prestressed concrete bridges using machine learning.
Časopis Građevinar
Građevinar., 73.
https://doi.org/10.14256/JCE.2738.2019
Kovačević M, Despotović V, Petronijević P. Construction cost estimation of reinforced and prestressed concrete bridges using machine learning. Časopis Građevinar. 2021;73
Kovačević Miljan, Despotović Vladimir, Petronijević Predrag, "Construction cost estimation of reinforced and prestressed concrete bridges using machine learning" Časopis Građevinar, 73 (2021),
https://doi.org/10.14256/JCE.2738.2019 .
