An artificial neural network approach to predicting most applicable post-contract cost controlling techniques in construction projects

Abstract

The post-contract phase of the construction process remains critical to cost management. Several techniques have been used to facilitate effective cost management in this phase. However, the deployment of these techniques has not caused a reduction in the incidence of cost overruns hence casting doubts on their utility. The seeming underwhelming performance posted by these post-contract cost control techniques (PCCTs), has been traced to improper deployment by construction project managers (CPM) and quantity surveyors (QS). Utilizing the perspectives of CPM and QS professionals, as elicited through a survey, produced 135 samples. The instrumentality of the artificial neural networks (ANN) in this study enabled the development of a structured decision-support methodology for analysing the most appropriate PCCTs to be deployed to different construction process phases. Besides showcasing the utility of the emergent ANN-based decision support methodology, the study's theoretical findings indicate that CPM and QS professionals influence decisions pertaining to PCCTs choice in distinct phases of the construction process. Whereas QS professionals were particularly responsible for the choice of PCCTs during the initial and mid-level phases, CPM professionals assumed responsibility for PCCTs selection during the construction process close-out phase. In construction cost management practice, the crucial PCCTs identifies more with the application of historical data and all cost monitoring approaches.