AI and Imaging Methods

AI AND IMAGING METHODS TO CHARACTERISE THE PERFORMANCE OF BIOFIBRE SYSTEMS

The proliferation of natural fibre-reinforced polymer (NFRP) systems for construction has underscored the need for higher-fidelity structural characterisation. Bio-based materials exhibit inherent variability and uncertainties in properties, which is further amplified by complex component geometries and the non-deterministic nature of robotic coreless filament winding (CFW). As a yet-to-be-standardised construction system, this has led to the enforcement of large safety factors and extensive full-scale destructive tests. To reduce effort for these wasteful measures, RP43-1 aims to provide methods for sensing, integrating and predicting the mechanical performance of bio-fibre systems using AI. Specific objectives of the project include enabling robust and precise scanning of both static and dynamically loaded structures, while using machine intelligence and prior data for local material property learning, for performance prediction and anomaly detection. Furthermore, these methods and results will be connected to anatomy-specific computational design, engineering, fabrication and construction methods within the cluster, namely the developments of integrated design methods for bio-based CFW systems in RP12.

PRINCIPAL INVESTIGATOR

Prof. Dr.-Ing. Jan Knippers
Deputy Spokesperson
Institute of Building Structures and Structural Design (ITKE)
Cluster of Excellence IntCDC

PARTICIPATING RESEARCHER

Dr. Shohei Mori
Participating Researcher
Visualization Research Center (VISUS)
Cluster of Excellence IntCDC

RESEARCHERS

M.Sc. Otto Lindstam
Doctoral Researcher
Institute of Building Structures and Structural Design (ITKE)
Cluster of Excellence IntCDC

M.Sc. Haifan Zhang
Doctoral Researcher
Visualization Research Center (VISUS)
Cluster of Excellence IntCDC