Site Overhead Cost Index Prediction Using RBF Neural Networks

Abstract

Cost estimation is one of the key tasks in the process of construction project management. Total costs incurred during the construction stage of the project by a contractor include direct costs that are related to works execution and indirect costs that accompany the delivery. This paper presents Artificial Neural Network (ANN) approach for prediction index of site overhead cost which is significant part of indirect cost. Applicability of Radial Basic Function (RBF) networks was investigated. A quantitative study on the factors conditioning site overhead costs of polish construction projects was completed. Moreover actual site overhead costs incurred by enterprises during project implementation were investigated. This research phase resulted in completion of a data set which covered 143 real-life cases of building projects. On the basis of the neural modelling the authors stated that the RBF networks can be a promising solution in the regression problem of site overhead cost index prediction.