ANN Model for Assessment of Design Changes in Gas-Oil and Petrochemical Projects

Abstract

The gas-oil and petrochemical industry plays one of the most powerful roles in the growth of the worldwide industrial and economic sectors. Huge amounts of investment are focused on these projects through various delivery methods. The majority of these projects are facing time delay challenges, cost overruns and quality weaknesses worldwide. Generally, the influence factors on project productivity performance would be subjected to changes in projects. In this research, the contribution of design change effects that imposed by frequency and severity on cost overrun and schedule delay was studied through a case study. The aim is to provide and introduce an artificial neural network through radial basis function (RBF) model to increase the level of perception and overall project performance during an early stages of project (design phase). The purpose is to create an insight evaluation on the level of impact of changes to the project during the design phase as an advanced supporting tool for decision makers. The output of RBF neural network will present the quantified measurement of all interacted change causes by means of cost and time expenditures. Experimental results show that the proposed prediction neural network is in good agreement with case observations. Consequently, this model of predominantly influencing factors will lead decision makers to deliver more optimized decisions with less complexity through wider insight on design change events.