Forecasting Models for Time and Cost Performance Predicting of Infrastructural Projects

Abstract Developing artificial forecasting models to predict the cost index and time index is the primary objective of this research. The efficacy of residential property investment projects will be assessed through the implementation of models that incorporate Artificial Neural Networks and Multiple Linear Regression. Historical information of thirteen boundaries for twenty finished Private Property Venture Tasks were separated from the records of the Directorate of Lodging, then four models were created by utilizing Multiple Linear Regression strategy and Artificial Neural Networks method. The main model was Time Index model with test information that gave average accuracy equivalent to 78.48 % and delivered a high connection coefficient (R) equivalent to 95.3 % utilizing (MLR), the subsequent model was (CI) model with test information gave (AA) equivalents to 66.6 % and created (R) equivalents to 85.4 % utilizing (MLR), and the third model was (TI) model with test information gave (AA) equivalents to 90.31 %, and delivered a high (R) equivalents to 95.4 % utilizing (ANN), the fourth model was (CI) model with test information gave (AA) equivalents to 90.21 % and delivered a high (R) equivalents to 99.6 % utilizing (ANN). It was found that the NN model showed more exact assessment results than the MLR models. Thus, it was resolved that the NN model is generally appropriate for anticipating the time execution and cost execution of Residential Property Investment Projects.