Influencing Cost Factors in Road Projects in Gaza Strip Using ANN

Abstract

Conceptual cost estimate can serve the owners’ feasibility estimate and assists in the establishment of the owner's funding which aids the engineers in designing to a specific budget. Conceptual estimating exhibits low accuracy level due to the lack of project information and the high level of uncertainty at early stage of project development. The purpose of this paper is to determine the most influencing cost factors in road projects using Delphi technique and Artificial Neural Networks. These factors were employed in a neural network (NN) for building a multi-layer perceptron (MLP) model to estimate the road project cost. Historical data of Gaza strip road projects were used to train and test the MLP model. The model developed showed a reduced error rate of 6.3% which demonstrates the ability to estimate the cost of road projects at early stage with higher accuracy.