Version 1
: Received: 23 March 2020 / Approved: 24 March 2020 / Online: 24 March 2020 (14:49:20 CET)
How to cite:
Aidan, I.; Jaber, F.; Al-Jeznawi, D.; Al-Zwainy, F. Predicting Earned Value Indexes in Residential complexes’ Construction Projects Using Artificial Neural Network Model. Preprints2020, 2020030363. https://doi.org/10.20944/preprints202003.0363.v1
Aidan, I.; Jaber, F.; Al-Jeznawi, D.; Al-Zwainy, F. Predicting Earned Value Indexes in Residential complexes’ Construction Projects Using Artificial Neural Network Model. Preprints 2020, 2020030363. https://doi.org/10.20944/preprints202003.0363.v1
Aidan, I.; Jaber, F.; Al-Jeznawi, D.; Al-Zwainy, F. Predicting Earned Value Indexes in Residential complexes’ Construction Projects Using Artificial Neural Network Model. Preprints2020, 2020030363. https://doi.org/10.20944/preprints202003.0363.v1
APA Style
Aidan, I., Jaber, F., Al-Jeznawi, D., & Al-Zwainy, F. (2020). Predicting Earned Value Indexes in Residential complexes’ Construction Projects Using Artificial Neural Network Model. Preprints. https://doi.org/10.20944/preprints202003.0363.v1
Chicago/Turabian Style
Aidan, I., Duaa Al-Jeznawi and Faiq Al-Zwainy. 2020 "Predicting Earned Value Indexes in Residential complexes’ Construction Projects Using Artificial Neural Network Model" Preprints. https://doi.org/10.20944/preprints202003.0363.v1
Abstract
The importance of this study may be defined by using the smart techniques to earned value indicators of residential buildings projects in Republic of Iraq, only one development intelligent forecasting model was presented to predict Schedule Performance Index (SPI), Cost Performance Index (CPI), and To Complete Cost Performance Indicator (TCPI) are defined as the dependent. The approach is principally influenced by the determining numerous factors which effect on the earned value management, that involves Iraqi historical data. In addition, six independent variables (F1: BAC, Budget at Completion., F2: AC, Actual Cost., F3, A%, Actual Percentage., F4: EV, Earned Value. F5: P%, Planning Percentage., and F6: PV, Planning Value) were arbitrarily designated and satisfactorily described for per construction project. It was found that ANN has the capability to envisage the dust storm with a great accuracy. The correlation coefficient (R) has been 90.00%, and typical accuracy percentage has been 89.00%.
Keywords
Artificial Neural Network; Schedule Performance Index (SPI); Cost Performance Index (CPI); To Complete Cost Performance Indicator (TCPI); Predicting; Models
Subject
Engineering, Civil Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
