Although novel construction tech systems have been developed to monitor construction sites, each collects data in isolation. We present an event processing model (EPM) that applies multiple monitoring technologies in parallel and a set of algorithms to interpret data from the multiple streams for operations control. In a full-scale lab experiment, multiple monitoring systems were applied to monitor workers as they built drywall partitions with a variety of systems and finishes. Data from the different streams were merged to establish what work was done, by whom, where, and for how long. The results suggest that collecting, merging, and interpreting diverse data streams using a multiple monitoring technologies system on-site can provide more complete and more accurate project status information than can be obtained from the use of any individual technology. This is essential for the automation of project progress monitoring, which is important in automating production planning and control tasks.
Practical Applications

Modern construction management approaches need more accurate, more reliable, and automated construction-progress monitoring. Manual monitoring is complicated and expensive in large, complex projects. It is difficult to perform the production control process successfully for whole projects with many related trades and resources. Automating data monitoring, data collection, and computing can reduce errors and increase efficiency compared to manual approaches. This paper presents a fully integrated system to manage production information through the construction life cycle. Different types of construction technologies could be integrated, along with a building information modeling (BIM) environment, into one common data-management system to report actual work progress and events completed on-site. The proposed system would support the decision-making process as part of construction planning in large and complex projects by providing accurate real-time information defining what work was done, by whom, in which location, and for how long. The information can be tailored to suit planners’ needs at four different levels: (1) work package, (2) task, (3) daily recording, and (4) building element. Broadly speaking, this paper presents a digital twin construction (DTC) framework to integrate data transfer and monitoring from field-to-BIM and from BIM-to-field for automated, accurate, and intelligent production control. This system can help planners improve look-ahead planning (LAP) and decision-making processes.