Neural Network-Based Prediction Model for Sites' Overhead in Commercial Projects

Abstract

Construction companies need to improve the accuracy of their projects' budgeting to achieve the targeted profit. Site overheads are the expenses related to a project but are not allocated to a specific work package. The main objective of this research is to develop a neural network model for commercial projects to predict and estimate project site overhead costs as a percentage of the direct cost. The focal point of the research is focused on the main factors affecting site overhead costs for commercial projects in Egypt. These factors and their weights were identified by experts through the collected structured data. Cost data for 55 projects in the past seven years was collected with various conditions of company rank, direct cost, project duration, project location, contract type, and type of company ownership. The results have shown that the best model developed consists of six input neurons; two hidden layers with six and five neurons respectively, and one output layer representing the percentage of project site overhead. The model was tested on six projects with accuracy of 84%.