Project Details
Projekt
Quality assured flow production of light UHPC bar elements using artificial neural networks
Subject Area
Structural Engineering, Building Informatics and Construction Operation
Applied Mechanics, Statics and Dynamics
Construction Material Sciences, Chemistry, Building Physics
Applied Mechanics, Statics and Dynamics
Construction Material Sciences, Chemistry, Building Physics
Term
from 2019 to 2024
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 423958617
Construction is still strongly influenced by workmanship. The value of a construction project is mainly taking place at the construction. Due to external conditions such as weather, the construction process is prone to error. Furthermore, the dependence on the manual skill of construction workers leads in many cases to unsatisfactory results in terms of quality of the structures. Long construction times are yet another consequence of in-situ manufacturing. Precast concrete elements can solve a part of the production problems on site. However, similar complex production steps as with in-situ concrete construction (preparing reinforcement and formwork, stripping, cleaning formwork) lead to similar problems.A modular design should lead to faster and cheaper construction. By reducing the possible components to a catalog of elements, higher similarities can be possible, allowing an economic flow production process.Trusses with their nodes and bars should allow the erection of supporting structures with high precision in a short time. The focus of the proposed research is the production of bar elements by extrusion. Hollow cylinders made of ultra-high-strength concrete (UHPC) with carbon reinforcement, developed at the Institute for Building Materials (IfB), are extruded. This element is characterized by a particularly low weight, a high buckling stability and a ductile behavior. Extruding UHPC requires a high level of quality assurance. In the case of the proposed research project, a continuous, self-learning quality assurance in the form of an artificial neural network (ANN), which is to be developed by the Institute of Statics and Dynamics (ISD).The aim is to develop an extrusion process for UHPC that incorporates a sensor concept "from day one", i.e. from the first steps of manufacturing. The measured data is used by the ANN to monitor the production process. For this purpose, the ANN is trained by measured data obtained from “healthy” extrusion processes. Furthermore, errors are deliberately introduced in some processes to “train" the ANN to recognize such and recommend countermeasures. Finally, in a statistical analysis, the safety gain is quantified by the proposed quality assurance framework. The work program provides for the following work packages:-Development of a method of extruding UHPC-Development of a sensor concept- Experiments to UHFB extrusion- Training of the ANN- Simulation of erroneous processes.-Quantitative assessment of the safety gain
DFG Programme
Priority Programmes
