Abstract:
The post-contract phase of the construction process remains critical to cost management.
Several techniques have been used to facilitate e ective 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 di erent 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.
