Despite using advanced design, construction and facility management tools, the construction sector contributes to about 7% of the global GDP. The average cost ratio between OPEX and CAPEX is distributed at 1:6.6. The factor varies depending on the location, energy performance, quality, energy cost, and BMS (Building Management System) performance. Despite its low contribution to GDP, the AECO(Architecture, Engineering, Construction and Operation) sector is responsible for 40% of energy construction in the European Union. Such demand is a crucial aspect of nowadays energy crisis which Europe is currently facing. The high demand leads multiple researchers to work on new ways and frameworks for energy optimization. One of the concepts the industry has embraced with great enthusiasm was the term proposed by Michael Grieves in the early 2000s. The DT (digital twin) definition distinguishes the data flow between an object and its' virtual instance in three levels, depending upon the data flow direction and communication method.
In the beginning, the term was not so popular. It resurfaced in the middle of the 2010s, gaining popularity and attractiveness among researchers because of its connection with IoT (Internet of Things). IoT, together with BIM (Building Information Modeling) objects, created a well-structured informational and geometrical input to various systems, being a basis for platforms providing communication between physical and digital assets. Therefore the increasing popularity of the Digital Twin concept among researchers and the involvement of commercial companies lean toward the fact that we are standing at the edge of the next revolution, which is the transition of BIM models into Digital Twins.
The thesis aims to close this research gap by proposing a Federated Semantic Digital Twin (FSDT) Framework. The FSDT will merge the most popular ontologies in the construction sector and provide a standardized method for an extension by other ontologies. The main challenge will be suitable conversion and selection of ontologies to represent all systems, asset management information, sensors data, MEP (Mechanical, Electrical, Plumbing) systems and many others. The research will demonstrate that multiple interoperability issues are still a valid and reliable problem among AECO professionals and propose a set of tools, methods and frameworks which might be used to close this gap.