Project Details
Projekt
Modeling of civil engineering structures with particular attention to incomplete and uncertain measurement data by using explainable machine learning (MoCES)
Applicant
Professor Dr.-Ing. Alexander Reiterer
Subject Area
Structural Engineering, Building Informatics and Construction Operation
Geodesy, Photogrammetry, Remote Sensing, Geoinformatics, Cartography
Geodesy, Photogrammetry, Remote Sensing, Geoinformatics, Cartography
Term
since 2022
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 501457924
The condition of a civil engineering structure is characterised by increasingly rapid degradation as it ages. A preventive action against aging is more successful the earlier it is taken. To prolong the usability of complex structures, much more information is required at a much earlier stage than is common today. To move toward predictive maintenance, fundamental research is needed on the methods of collecting, fusing, and evaluating all geometry, material, stress, and aging data. Digitisation, regarding the generation of a digital twin, is taking on a completely new significance in this context. It enables the combination and real-time evaluation of all data required for operation and maintenance. The main goal of our proposal is to research and develop new methods and processes for the automated modeling of complex building structures. The aim is to fuse a wide variety of data streams and to take into account their uncertainty and incompleteness. The modeling, which will be realised on the basis of machine learning methods, will be extended by an explanatory component so that conclusions in the sense of object modeling and reconstruction are reproducible.The object reconstruction and modeling has the focus to form the basis for object monitoring - here we fully address the goal of the DFG Priority Programme "Hundert plus". The global aim of "Hundert plus" is the methodical development of an adaptive, intelligent, and digital representation (digital twin) of real, physical objects (buildings). In the end the model will be linked with measurement data from building monitoring over the entire service life and centrally provides compressed information for predictive, digital building management.An automated semantic object reconstruction and modeling for monitoring tasks as proposed does not exist in the literature or in practical use today and will therefore provide a significant and important contribution to the rapid and efficient monitoring of large-scale civil structures.
DFG Programme
Priority Programmes
