The strong population growth and urbanization are increasing the global resources and energy consumption. The AEC (architecture, engineering and construction) industry is responsible for 60% of the extracted raw materials and generates 40% of the energy-related CO2 emissions. In Austria, the AEC sector is responsible for 70% of total annual waste – facts that are underlining the importance of implementing recycling strategies. The building stock has great potential to serve as raw material reservoir, however currently there is a lack of comprehensive knowledge about the actual building stock, which is the largest obstacle for reusing and recycling of materials and elements.
The main goal of BIMstocks is to develop a method for a consistent digital documentation of the material composition of the existing building stock for modeling the secondary raw materials cadaster and prediction of the recycling potential, by creating a catalogue of BIM-Objects of typically Viennese buildings and follow-up generation of as-built BIM-Models, thus enabling an upscaling to city level. Analyzing and scanning of 10 different use cases, which will represent the variety of typical Viennese buildings, will enable the upscaling to city level. The final aim is to generate a GIS-based Urban Mining Platform, which embeds the obtained information of the use cases and predicts the recycling potential, the material flow and waste mass. Furthermore, a framework will be developed in order to enable the application of urban mining strategies. The framework should describe all individual steps as well as the applied methods.
Thus, the project represents the continuation of the framework developed in the research project SCI_BIM, which investigated an integrated determination of geometry and material by coupling laser scanning and GPR technology for the semi-automated BIM-Model generation. SCI_BIM demonstrated that GPR technology needs further testing to a) apply it to different building structures and b) build-up a material database, which would significantly increase the efficiency of material determination.
The innovation of the project is the coupling of different technologies, which enable upscaling from component-level to city-level: scanning technology using GPR, application of machine learning for the automated determination of material compositions, and predictive modelling at city-level in the digital urban mining platform. For the first time the uncertainties resulting from the use case samples, the measured values and the extrapolation are estimated. The intended result is to generate a building catalogue for typical Viennese buildings, which enables upscaling on city-level as well as embedding the components and buildings into the GIS-based Urban Mining Platform, based on GPR scans and subsequent machine learning algorithms.
The main use of the obtained results from BIMstocks is the increase of recycling rates by applying urban mining strategies, for which the generated public urban mining platform serves as a basis.