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Digitisation in the construction industry continues to advance and, together with the increasing dissemination and further development of hardware and software, is steadily opening up further opportunities for innovative ways of working. Building Information Modelling (BIM) is currently becoming the standard for new construction but has not yet been optimised for use in existing buildings. Therefore, the Institute of Building Materials Research (ibac) is researching new methods and possibilities for BIM-based building preservation. In this paper, the automated creation and analysis of point clouds as well as the implementation of further information from in situ diagnosis and monitoring systems in BIM-Models are presented. On a practical example, the different steps of a subsequent digitisation of an existing building are demonstrated considering new possibilities as autonomous robots and the intelligent utilisation of sensors and diagnostics tools. The goal is a decision support tool, which is independent from proprietary software, adaptive to different types of buildings and open for various interfaces. Current results show that quantifying point clouds and making BIM-models usable beyond the planning and execution phase for new buildings are essential steps for the digitisation of building maintenance. The proposed digital workflow holds great potential for effective building diagnoses and efficient service life management.

Hymenoptera has some of the highest diversity and number of individuals among insects. Many of these species potentially play key roles as food sources, pest controllers and pollinators. However, little is known about the diversity and biology and ~80% of the species have not yet been described. Classical taxonomy based on morphology is a rather slow process but DNA barcoding has already brought considerable progress in identification. Innovative methods such as image-based identification and automation can further speed up the process. We present a proof of concept for image data recognition of a parasitic wasp family, the Diapriidae (Hymenoptera), obtained as part of the GBOL III project. These tiny (1.2-4.5mm) wasps were photographed and identified using DNA barcoding to provide a solid ground truth for training a neural network. Taxonomic identification was used down to the genus level. Subsequently, three different neural network architectures were trained, evaluated and optimised. As a result, 11 different genera of diaprids and one mixed group of 'other Hymenoptera' can be classified with an average accuracy of 96%. Additionally, the sex of the specimen can be classified automatically with an accuracy of >97%.

A promising process for the automatization of concrete structures is extrusion or extrusion molding. An innovative approach is the extrusion of concrete with imbedded technical textiles as reinforcement. For a successful extrusion, the rheological properties of the fresh concrete have to be optimized, as it must be extrudable and have sufficient early strength after leaving the mouthpiece. Within the scope of this paper, a process was developed which allows the integration of flexible as well as stiff impregnated textiles into the extrusion process. For this purpose, different textile-reinforced mortars (TRM) were extruded and their material characteristics were investigated. The results show that the mortar cross-section is considerably strengthened, especially when using carbon textiles, and that extrusion has considerable potential to produce high-performance TRM composites. In uniaxial tension tests with TRM, as well as in the pure roving tensile strength tests, textile stresses of approx. 1200 MPa were achieved for the glass textile and approx. 2250 MPa for the carbon textile. The position of the textile layer deviated a maximal 0.4 mm from its predesigned position, which shows its potential for producing tailor-made TRM elements. In addition, by adjusting the mortar mix design, it was possible to reduce the global warming potential (GWP) of the extrusion compound by up to 49.3% compared to the initial composition from preliminary studies.