Quantifying the Impact of Technology Utilization on Schedule and Cost Performance in Construction Projects

Abstract

There has been a plethora of research on the performance of schedule and cost in construction projects. However, current research falls short in investigating the combined contribution of different construction technologies on project performance metrics. Specifically, none of the previous efforts have used integrated earned value management (EVM) parameters and utilized construction technologies to assess project schedule and cost performance. This paper fills this knowledge gap. To this end, a multistep research methodology was employed to (1) explore the utilization ratings of 13 construction technologies based on data collected from 52 industry experts; (2) rank these technologies in terms of their contribution to schedule and cost savings; (3) map the utilization ratings of construction technologies to schedule and cost variances by leveraging data from 48 construction projects with varying levels and scopes of technology integration to gauge the impact of using these technologies; and (4) develop an associated decision-support model that underwent rigorous verification under extreme conditions and sensitivity analysis. This study makes a noteworthy contribution to the existing body knowledge, revealing that the major drivers for schedule savings are building information modeling (BIM), unmanned aerial vehicles (UAVs), and augmented/virtual reality (AR/VR), while additive manufacturing (AM), digital twin (DT), and Internet of Things (IoT) are the most significant contributors to cost savings. Moreover, the study reveals strong correlations between various technologies, such as UAVs and laser scanning, geographic information system (GIS) and GPS, AR/VR and digital twins, as well as robotics/automation and AR/VR. Furthermore, the study introduces a novel model that enhances the prediction of project performance, specifically in terms of schedule and cost variations resulting from the adoption of various construction technologies. Eventually, it improves the decision-making process related to the deployment of the investigated construction technologies.