Automatic Parametric Generation of Simulation Models from Project Information in Digital Twin Construction

In construction, simulation can provide production planners with forward-looking or predictive situational awareness of the potential impact of proposed changes before implementation. Planners can experiment extensively with various alternative production plans and systems without suffering real-world consequences of failure. Addressing the need to have proper control of the jobsite, DTC is a model for managing production in construction that leverages data streaming from different monitoring technologies and artificially intelligent functions. Overall, DTC offers accurate project status information (PSI) and proactive analysis and optimization of ongoing design, planning, and production processes. The integration of automated monitoring and information integration algorithms contemplated within the DTC framework may be able to provide the kind of information needed for practical simulation at short intervals, thus offering construction planners a powerful tool to optimize the decision-making process regarding any necessary changes to designs or plans, by automatically generating accurate and reliable simulation models based on the current jobsite progress, resource information, and safety conditions. This paper describes an automated system for parametric generation of simulation models for this purpose from project intent and status information stored in a DTC database. This is one aspect of broader research that involves design, development and testing of a DTC simulation and optimization system. A construction case study is provided to demonstrate the technical feasibility of automatically and parametrically producing simulation models based on data from a digital twin.