Reconstruction of the Building Infrastructure: Two Performance Prediction Models

Abstract

The total value of civil infrastructure (roads, buildings, and underground services) in North America is estimated at $20 trillion. Due to its large size and the need for minimal interruption to services, maintaining the infrastructure becomes a huge challenge. Motivated by the large cost overruns and delays in most infrastructure reconstruction work, this research sheds some light on the performance of such projects and their risky environment. The paper presents two predictive models of the overall performance of reconstruction projects using a simple measure, project performance factor to combine cost performance, schedule performance, and quality performance. Using 54 case studies of past reconstruction projects, a micromodel as well as a macromodel were developed through experimentation with statistical analysis and artificial neural networks. Using the developed models, a Monte Carlo-based sensitivity analysis was performed to assess the impact of uncertainty in project conditions on performance predictions. Guidelines for improving reconstruction of building infrastructure for owner organizations are then provided.