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Please use this identifier to cite or link to this item: http://dspace.vgtu.lt/handle/1/4057

Title: Improving the Results of the Earned Value Management Technique Using Artificial Neural Networks in Construction Projects
Authors: Balali, Amirhossein
Valipour, Alireza
Antuchevičienė, Jurgita
Šaparauskas, Jonas
Keywords: symmetry
earned value management (EVM)
artificial neural networks (ANNs)
multiple regression analysis
road industry
Issue Date: 2020
Publisher: MDPI
Citation: Balali, A.; Valipour, A.; Antucheviciene, J.; Šaparauskas, J. Improving the Results of the Earned Value Management Technique Using Artificial Neural Networks in Construction Projects. Symmetry 2020, 12, 1745.
Series/Report no.: 12;10
Abstract: The cost, time and scope of a construction project are key parameters for its success. Thus, predicting these indices is indispensable. Correct and accurate prediction of cost throughout the progress of a project gives project managers the chance to identify projects that need revision in their schedules in order to result in the maximum benefit. The aim of this study is to minimize the shortcomings of the Earned Value Management (EVM) method using an Artificial Neural Network (ANN) and multiple regression analysis in order to predict project cost indices more precisely. A total of 50 road construction projects in Fars Province, Iran, were selected for analysis in this research. An ANN model was used to predict the projects’ cost performance indices, thereby creating a more accurate symmetry between the predicted and actual cost by considering factors that influence project success. The input data of the ANN model were analysed in MATLAB software. A multiple regression model was also used as another analytical tool to validate the outcome of the ANN. The results showed that the ANN model resulted in a lower Mean Squared Error (MSE) and a greater correlation coefficient than both the traditional EVM model and the multiple regression model.
Description: This article belongs to the Special Issue Symmetric and Asymmetric Data in Solution Models
URI: http://dspace.vgtu.lt/handle/1/4057
ISSN: 2073-8994
Appears in Collections:Moksliniai straipsniai / Research articles

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