Project runtime: 01.02.2021 - 31.01.2024
Concrete is one of the most important construction materials worldwide and is characterised by an enormous adaptability to changing requirements. This comes with a high, and continuously increasing, complexity in terms of raw materials, formulations and the manufacturing process. In order to fully exploit the technical and environmental potential of concrete construction requires the highest level of expertise among the individual players in the construction industry. Currently, there is an unsatisfactory knowledge transfer both from research into industry and experience from successful special applications into broad practice which has hindered progress. Solving this problem is the goal of the joint project "LeBeDigital". It sets itself the challenging task of developing a generally available data and knowledge management system tailored to the process of concrete production on the basis of generally applicable ontologies and workflows. The project puts particular emphasis on the manufacturing process under the easily controllable conditions in the precast industry. The aim is to link the experimental and simulation-based material data relevant for concrete production across scales in a knowledge-based manner, to validate it and to make it usable for future applications in precast production. In developing this knowledge-based database, the joint project "LeBeDigital" has set itself the goal of making experience and research data permanently usable and accessible to the general public at a high level and with deep information content. This paradigm shift to the design of concrete components, from predefined in extensive codes and leaflets to knowledge-based, performance-oriented material design and takes into consideration the whole product life cycle. The close integration into the current nationwide initiative for the digitalisation of materials’ research will amplify the potential for impact on the domestic construction industry. For example, a suitable database structure can provide groundbreaking impulses for the construction industry, which is strongly fragmented into individual stakeholders. The desired effects for the industry, which go hand in hand with an increase in efficiency in terms of sustainability and costs, are, for example, the evaluation of data employing machine learning methods as a basis for numerical simulations for material optimisation or for the control and automation of the production of precast concrete parts.
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