Machine-Learning-Based Consumption Estimation of Prestressed Steel for Prestressed Concrete Bridge Construction

Created by W.Langdon from gp-bibliography.bib Revision:1.7810

@Article{kovacevic:2023:Buildings,

• author = "Miljan Kovacevic and Fani Antoniou",
• title = "{Machine-Learning-Based} Consumption Estimation of Prestressed Steel for Prestressed Concrete Bridge Construction",
• journal = "Buildings",
• year = "2023",
• volume = "13",
• number = "5",
• pages = "Article No. 1187",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2075-5309",
• URL = "https://www.mdpi.com/2075-5309/13/5/1187",
• DOI = "doi:10.3390/buildings13051187",
• 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.",
• notes = "also known as \cite{buildings13051187}",

}

Genetic Programming entries for Miljan Kovacevic Fani Antoniou

Citations