Toward Automated Earned Value Tracking Using 3D Imaging Tools

Abstract

Accurate and frequent construction progress tracking provides critical input data for project systems such as cost, schedule control, and billing. Unfortunately, conventional progress tracking is labor intensive, sometimes subject to negotiation, and often driven by arcane rules. Attempts to improve progress tracking have recently focused on automation, using technologies such as three-dimensional imaging, global positioning systems, ultra wide band (UWB) indoor locating, handheld computers, voice recognition, wireless networks, and other technologies in various combinations. However, one limit of these approaches is their focus on counting objects or milestones rather than value. In this paper, a four-dimensional model recognition-driven automated progress tracking system that transforms objects to their earned values is examined via the analysis of data from the construction of a steel reinforced concrete structure and a steel structure. It is concluded that automated, object oriented recognition systems that convert each object to its earned value can substantially improve the accuracy of progress tracking, and thus, better support project systems like billing. The contribution of this study is an argument based on scientific results for refocusing future research onto automated earned value tracking, which is ultimately what is needed in practice.