In view of the growing demand for sustainable construction methods, building materials with high reusability and deconstructable connection techniques are increasingly coming to the fore. In this particular context, steel has been identified as having the potential to positively contribute to the reduction of emissions and waste. One of the key challenges for the widespread implementation of steel reuse is the lack of a consistent, digital and interoperable data basis. This not only affects the deconstruction phase, but is also important along the entire value chain in the construction industry. The upstream production of steel requires both horizontal digitalisation strategies that enable standardised data exchange between different actors and vertical strategies are important to connect internal processes, systems and stakeholders. However, the presence of inconsistent data formats and proprietary software solutions increase the complexity of such data exchange. While the DSTV-NC format has become an established standard for representing geometric data and enabling conventional steel fabrication, it no longer meets the requirements for modern interoperability or supports emerging fabrication methods such as robotic processing. Advancing digital fabrication and facilitating material reuse in line with circular construction principles therefore requires a more flexible and semantically enriched data model. This dissertation proposes a multi-modal approach to data representation in steel pre-fabrication. Both the Industry Foundation Classes (IFC) schema and ontology-based frameworks are investigated as potential successors to the DSTV-NC format. A detailed analysis of the data structures required for novel fabrication and reuse processes form the basis for the development of both models. Their practical applicability is evaluated through prototype implementations in robotic steel fabrication. Based on the findings, a combined approach is recommended. The IFC schema serves as a standardised data model that allows cross-project information to be stored within a Common Data Environment (CDE), ensuring accessibility for all relevant parties. Ontology-based models can complement this by enabling flexible, domain-specific modelling of detailed data. This interoperable data architecture supports both horizontal and vertical digitalisation. The dissertation also examines the practical challenges of integrating the proposed data model into existing planning and fabrication workflows, and identifies specific opportunities for standardisation. The outcomes contribute to the advancement of digital transformation in steel construction and simultaneously promote circular economy principles through enhanced data interoperability and process automation.
Victoria Jung (Wed,) studied this question.