Description

The ecological consideration of life cycle analysis is more topical than ever. Concrete construction in particular emits large quantities of CO2 equivalents, which need to be reduced using new intelligent networked methods. The aim of the KIT in the AIBetOn3D project is to expand the ecological aspect of product life cycle analysis. KIT aims to integrate methods of artificial intelligence and knowledge management for this purpose. The aim is to design and develop a data-based AI-supported semantic middleware. This is to be understood as a learning system that links all data, information and knowledge elements relevant to materials, production and the product life cycle and, in conjunction with the simulation tools developed in the consortium, enables forecasting models. An important component of this is the joint development of a heterogeneous material database in the form of a semantically networked causally correlated knowledge graph. It should then be possible to use this knowledge graph to carry out multidimensional optimization with regard to the input variables of component dimensions, functional relationships, material composition, parameter settings of the additive manufacturing process and the output variables and time expenditure and costs, functional fulfillment, CO2 life cycle analysis (LCA) and resource conservation. In the long term, the aim is to develop a model that can be transferred to other areas of application, which can make robust predictions about behavior depending on the setting variables, as well as identify potential CO2 savings and analyze the potential of new building materials based on their material composition and link them directly to the relevant manufacturing parameters.