الفهرس 
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Abstract
This study aims to enhance the process of selecting and hiring sub-contractors in the Egyptian construction industry. Throughout two phases, first, studying the factors that are highly considered by the procurement personals when hiring a sub-contractor. Accordingly, a survey approach was introduced to grasp the opinions of a random sample with an adequate size from the Egyptian construction industry about the importance of the selection attributes, which were collected from the well-documented previous studies. A set of statistical analysis tests were carried out to explore the trends and insights, then Exploratory factor analysis was implemented to discover the latent constructs that explains the highest possible variance through all the attributes. Nine factors were extracted and explained 67.29% of the total variance. Second, an Artificial neural network (ANN) that has the ability to predict the project performance indicator as a result of hiring a nominated sub-contractor shall be implemented. Six factors out of the nine which can be predicted and evaluated for a nominated sub-contractor in the selection process were selected as an input layer for the ANN, four models were introduced, each for a project performance indicators (time, cost, quality, and scope). The data set required to feed and train the ANN models were collected through a second survey that grasped the previous experience of personals related to procurement process of hiring a sub-contractor in a previous project. The appropriate architecture for each model was studied based on a sufficient number of iterations considering every possible parameter. Finally, four ANN models were introduced and validated to have the required predicting capabilities which is user-friendly and does not require any previous background about the hustle of ANN’s implementation.