SPP 2388:  Hundred plus - Extending the Lifetime of Complex Engineering Structures through Intelligent Digitalization

• Subject Area: Construction Engineering and Architecture ; Geosciences ; Computer Science, Systems and Electrical Engineering ; Mathematics
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 461030501

The condition of a structure – similar to that of people – is characterised by an ever more rapid degradation as it ages. Preventive measures against ageing are all the more successful the earlier they are taken. This Priority Programme “Hundred plus” aims to extend the service life of complex structures through intelligent digitalisation. Three core research areas are launched in the proposal for the entire program: (1) digital models, (2) digital linkage, and (3) condition indicators. Digital models and digital linkage are the focuses of funding period 1. The successful development of methods in the SPP is supported by two essential elements. The first element is the central coordination of the activities, the promotion of a mutual, fruitful exchange of knowledge and experience as well as comprehensively coordinated support for young scientists. Various activities are planned for this purpose, e.g. organisation and implementation of annual key meetings of all researchers in the SPP, interdisciplinary cluster workshops, events for doctoral training and the involvement of guests and associates. Specific public relations work will promote the visibility of the SPP. The second element is the research on the validation structure within the scope of an accompanying scientific project. The aim is to test and validate the methods, which are developed in this SPP, regarding model generation, digital linking and the derivation of condition indicators on a real demonstrator structure. The Weserstrombrücke (Weser River Bridge), which is an overpass of the BAB A2 over the Weser near Bad Oeynhausen, has been selected as the validation structure. In funding phase 1, digital models will be created, tested and improved using various resources such as existing plans, laser scans and photogrammetry measurements as well as non-destructive methods. The results form the basis for a digital twin. This and all other data will be stored, processed and sustainably saved on the platform, which is to be concurrently set up, for data management and exchange. Besides, an initial structural health monitoring system for the validation object is to be developed and installed. This will create a real data basis. In addition, all these heterogeneous models and data, which come from different sources, will be linked together. This whole system can be used to realise a comprehensive scientific exchange across all research projects in phase 1. Furthermore, sub-projects can be developed in phase 2 on the basis of this existing system.

• DFG Programme: Priority Programmes
• International Connection: China, USA

Projects

• Automated digital building modelling from heterogeneous as-built data taking into account their quality characteristics - ADIBAMOD-Q (Applicant Neitzel, Frank )
• Automatic data-driven modeling and H2/H-infinity- norm-based dimension reduction of process-oriented and cooperative systems for SHM condition analysis with methods of system identification and machine learning on exposed structures (Applicant Lenzen, Armin )
• Coordination Funds (Applicant Marx, Steffen )
• Data driven model adaptation for identifying stochastic digital twins of bridges (Applicants Unger, Jörg F. ;  Weiser, Martin )
• Design methodology for cross-life structural health monitoring with unknown damage process (Applicant Marx, Steffen )
• Digital coupling of multiscale analyses in modelling and monitoring (Applicants Könke, Carsten ;  Zabel, Volkmar )
• Digital twin as an intermediary between in-situ damage detection and global structural analysis (Applicant Unglaub, Julian )
• Intelligent resilience analysis for infrastructure considering uncertain real-time data (Applicants Beer, Michael ;  Broggi, Matteo )
• Lifetime monitoring of structures by means of data assimilation in digital twin with artificial intelligence (Applicant Petryna, Yuri S. )
• Measurement-based condition assessment of prestressed concrete bridges with low shear reinforcement ratios under fatigue loading for service life prediction on a digital twin (Applicants Claßen, Martin ;  Hegger, Josef )
• Method development and evaluation scheme for cross-life linkage of structural health monitoring data and exiting knowledge via deep transfer learning (Applicant Herrmann, Ralf )
• Modeling of civil engineering structures with particular attention to incomplete and uncertain measurement data by using explainable machine learning (MoCES) (Applicant Reiterer, Alexander )
• Monitoring data driven life cycle management with AR based on adaptive, AI-supported corrosion prediction for reinforced concrete structures under combined impacts (Applicants Lowke, Dirk ;  Wessels, Henning )
• Optical 3D-bridge-inspect: Innovative inspection of complex infrastructure combining very high-resolution UAV-borne imagery and structured-light scanning (Applicants Bestmann, Ulf ;  Gerke, Markus )
• Optical 3D measurement techniques for generation, revision and monitoring of digital twins of complex building structures (Applicant Maas, Hans-Gerd )
• Pattern detection of internal tendon rupture on concrete surfaces (Applicant Sanio, David )
• Quality assurance of digital twins based on mathematical abstraction and tangle-based blockchain architectures (Applicant Smarsly, Kay )
• Semantic segmentation of laser scanning point clouds for digital building modeling using Bayesian neural networks for uncertainty quantification (PointSemSeg+) (Applicant Blankenbach, Jörg )
• SpatioLink: Spatio-temporal links between heterogenous Information Resources in infrastructure (Applicant Beetz, Jakob )
• Structural Health Monitoring with model based damage detection using nonlinear model adaption and Artificial Intelligence methods (Applicant Schnellenbach-Held, Martina )

• Spokesperson: Professor Dr.-Ing. Steffen Marx