@ARTICLE{Jasim_Nidal_A._Predicting,
 author={Jasim, Nidal A. and Maruf, Shelan M. and Aljumaily, Hadi S.M. and Al-Zwainy, Faiq M.S.},
 volume={Vol. 66},
 number={No 3},
 journal={Archives of Civil Engineering},
 pages={541-554},
 howpublished={online},
 publisher={WARSAW UNIVERSITY OF TECHNOLOGY FACULTY OF CIVIL ENGINEERING and COMMITTEE FOR CIVIL ENGINEERING POLISH ACADEMY OF SCIENCES},
 abstract={Inaccurate estimation in highway projects represents a major problem facing planners and estimators, especially when data and information about the projects are not available, and therefore the need to use modern technologies that addresses the problem of inaccuracy of estimation arises. The current methods and techniques used to estimate earned value indexes in Iraq are weak and inefficient. In addition, there is a need to adopt new and advanced technologies to estimate earned value indexes that are fast, accurate and flexible to use. The main objective of this research is to use an advanced method known as artificial neural networks to estimate the TSPI of highway buildings. The application of artificial neural networks as a new digital technology in the construction industrial in Republic of Iraq is absolutely necessary to ensure successful project management. One model built to predict the TCSPI of highway projects. In this current study, artificial neural network model were used to model the process of estimating earned value indexes, and several cases related to the construction of artificial neural networks have been studied, including network architecture and internal factors and the extent of their impact on the performance of artificial neural network models. Easy equation was developed to calculate that TSPI. It was found that these networks have the ability to predict the TSPI of highway projects with a very outstanding saucepan of reliability (97.00%), and the accounting coefficients (R) (95.43%).},
 type={Article},
 title={Predicting Index to Complete Schedule Performance Indicator in Highway Projects Using Artificial Neural Network Model},
 URL={http://ochroma.man.poznan.pl/Content/117479/PDF/30.ACE00045%20do%20druku_B5.pdf},
 doi={10.24425/ace.2020.134412},
 keywords={Highway Project, Artificial Neural Network, To Complete Schedule Performance Indicator (TCSPI), GMDH Shell Software, Budget at Completion (BAC), Earned Value (EV), Planning Value (PV)},
}