Automated progress monitoring technological model for construction projects

Construction industry professionals admire the evolution of digital data-acquisition technologies in mon-itoring processes due to efficient and efficacious outcomes. However, due to a lack of theoretical insight towards the effective application of these technologies, hesitation has been observed for its adoption, which added a challenge to attaining the Industry 4.0 spirit. This study aims to improve a theoretical, sta-tistically validated model, underlining the operational factors that enhance the performance of the dig-itized monitoring process. The study has followed the structural equation modeling (SEM) approach on identified 36 factors, which were colligated under four categories for developing the technological -based model. The attained model defines effective technological-based factors for implementing auto-mated monitoring under 'tracking & sensing', 'site video', '3D scanner', and 'site images'. The significance of this model is a provision of a theoretical base to researchers and construction industry professionals in digitized progress monitoring technological operations and technical aspects towards enriched outcomes.(c) 2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams Uni-versity. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

Construction industry professionals appreciate the advancements in digital data-acquisition technologies for monitoring processes because of their efficiency and effectiveness. However, a lack of theoretical insight hampers the widespread adoption of these technologies, posing a challenge to achieving the goals of Industry 4.0. In this study, a statistically validated model is developed to identify the operational factors that enhance the performance of digital monitoring processes. This model provides theoretical guidance for researchers and professionals in the construction industry, highlighting the technological factors that contribute to successful implementation of automated monitoring.