Predicting Earned Value Indexes in Residential Complexes’ Construction Projects Using Artificial Neural Network Model

Background: The back propagation neural network model as a smart technique can be used in this work proved to be very successful in modelling nonlinear at the same time and the interrelationships between them, and, it have the ability to predict the earned value indicators for residential Complexes buildings projects in Republic of Iraq, Objective: 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. Methodology: The approach is principally influenced by the determining numerous factors which effect on the earned value management, which 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. Results: 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%. Novelty: this study had found that the neural network models outperformed traditional linear methods and therefore they leave the great potential for replacing traditional methods in the area of earned value estimating and forecasting.