AREA BASE OF COST ESTIMATION FOR BUILDING CONSTRUCTION PROJECTS USING AN ARTIFICIAL NEURAL NETWORK

Abstract

Managers of projects monitor the project schedule and compare planning values with the actual cost into the project and how much of it is earned value. One of success methods for managing the construction projects is to find the effective cost factors and investigate the correlations between them. In order to determine the discrepancies, the Execution Phase performance measures are compared to the baseline metrics decided upon in the Planning Phase. The significance of these deviations is assessed by factoring them into the control methods at every stage. For that, the present paper developed an ANN technique for project management to monitor the cost of project based on the correlation between the project size and the project cite area within the implementation process. The contribution in this paper is to present a project planning cost mimic the real actual cost. Modifications to the project can be monitored using this method, which takes into account both the nature of the work being done and the time frame during which it is being performed. To gauge the system's efficacy, the ANN system was applied to structural concrete and building walls. The system's final output demonstrated a straightforward and reliable method of tracking and observing progress.