Although Building Information Modeling (BIM) is increasingly used in construction, integrating sustainability criteria into early design decisions remains limited. Current practices often rely solely on ecological factors from individual Environmental Product Declarations (EPDs), overlooking the multidimensional nature of sustainability, which includes ecological, economic, and social aspects. Therefore, automated decision-making requires comprehensive sustainability assessments throughout the building's lifecycle.
This paper presents a BIM-based framework for embedding holistic sustainability attributes into digital building models, with a focus on wall constructions treated as a representative part of the building. A key focus is the integration of End-of-Life (EoL) scenarios with EPD data to enable more comprehensive ecological assessments. The results were developed through a mixed-method approach. The methodology combines 16 expert interviews and a qualitative content analysis according to Mayring to identify common modeling practices in BIM-based assessments. A systematic literature analysis complemented the interviews by identifying relevant sustainability attributes for wall constructions and exploring how EPD data and EoL scenarios can be integrated into assessments. Based on these insights, a prototype model was developed and validated using four representative masonry walls, evaluating its accuracy and robustness through comparison with reference data.
The results reveal that, for walls, thermal transmittance, sound insulation, and production costs were identified as particularly relevant attributes alongside ecological factors. For the ecological assessment, five EoL scenario categories integrate circular information across life cycle stages. Within this framework, construction elements are evaluated not only at the material level but also regarding connections, accessibility, and deconstruction potential, enabling a more comprehensive sustainability assessment.
Validation shows that current data gaps, particularly regarding EoL processes and other sustainability attributes, can skew evaluations, often favoring destructive strategies over circular ones. This underscores the importance of consistent, holistic, sustainable data sets and EoL modeling for data-driven, automated decisions in the early stages of design.