Forecasting the Final Contract Cost on the Basis of the Owner’s Cost Estimation Using an Artificial Neural Network
Author(s): |
Abdulah M. Alsugair
Naif M. Alsanabani Khalid S. Al-Gahtani |
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Medium: | journal article |
Language(s): | English |
Published in: | Buildings, 26 February 2023, n. 3, v. 13 |
Page(s): | 786 |
DOI: | 10.3390/buildings13030786 |
Abstract: |
Raising the final contract cost (FCC) is a significant risk for project owners. This study hypothesizes that the factors that cause owner’s cost estimation (OCE) accuracy and FCC changes share the same causes, and a case study confirmed that the two variables (OCE and FCC) could be correlated. Accordingly, this study aims to develop a forecast model to predict FCC on the basis of the initial OCE, which has not been studied previously. This study utilized data from 34 Saudi Arabian projects. Two linear regression models developed the data, and the square root function transformed the data. Moreover, the artificial neural network (ANN) model was developed after data standardization using Zavadskas and Turskis’ logarithmic method. The results showed that the ANN model had a MAPE smaller than the two linear regression models. Using Zavadskas and Turskis’ logarithmic standardization method and elimination of data that had an absolute percentage error (APE) of more than 35% led to an increase in ANN model accuracy and provided a MAPE value of less than 8.5%. |
Copyright: | © 2023 by the authors; licensee MDPI, Basel, Switzerland. |
License: | This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met. |
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data sheet - Reference-ID
10712790 - Published on:
21/03/2023 - Last updated on:
10/05/2023