Machine-Learning-Based Consumption Estimation of Prestressed Steel for Prestressed Concrete Bridge Construction
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{kovacevic:2023:Buildings,
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author = "Miljan Kovacevic and Fani Antoniou",
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title = "{Machine-Learning-Based} Consumption Estimation of
Prestressed Steel for Prestressed Concrete Bridge
Construction",
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journal = "Buildings",
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year = "2023",
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volume = "13",
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number = "5",
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pages = "Article No. 1187",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "2075-5309",
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URL = "https://www.mdpi.com/2075-5309/13/5/1187",
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DOI = "doi:10.3390/buildings13051187",
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abstract = "Accurate prediction of the prestressed steel amount is
essential for a concrete-road bridge's successful
design, construction, and long-term performance.
Predicting the amount of steel required can help
optimise the design and construction process, and also
help project managers and engineers estimate the
overall cost of the project more accurately. The
prediction model was developed using data from 74
constructed bridges along Serbia's Corridor X. The
study examined operationally applicable models that do
not require indepth modelling expertise to be used in
practice. Neural networks (NN) models based on
regression trees (RT) and genetic programming (GP)
models were analysed. In this work, for the first time,
the method of multicriteria compromise ranking was
applied to find the optimal model for the prediction of
prestressed steel in prestressed concrete bridges. The
optival model based on GP was determined using the
VIKOR method of multicriteria optimisation; the
accuracy of which is expressed through the MAPE
criterion is 9.16percent. A significant average share
of 46.11percent of the costs related to steelworks, in
relation to the total costs, indicates that the model
developed in the paper can also be used for the
implicit estimation of construction costs.",
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notes = "also known as \cite{buildings13051187}",
- }
Genetic Programming entries for
Miljan Kovacevic
Fani Antoniou
Citations