7.2 Ingenieurbau
Filtern
Erscheinungsjahr
- 2022 (2) (entfernen)
Dokumenttyp
- Beitrag zu einem Tagungsband (2) (entfernen)
Sprache
- Englisch (2) (entfernen)
Referierte Publikation
- nein (2)
Schlagworte
Organisationseinheit der BAM
- 7 Bauwerkssicherheit (2)
- 7.2 Ingenieurbau (2)
- 8 Zerstörungsfreie Prüfung (1)
- 8.5 Röntgenbildgebung (1)
- VP Vizepräsident (1)
- VP.1 eScience (1)
The contribution discusses the processing and analysis of data generated on two different ways of investigations for impact damage in reinforced concrete structures. Damage investigations are essential to determine type and characteristics of damage and thus the residual capacity. Damage describing data is generated using two different types of investigation, a non-destructive tomographic as well as numerical examination. Subsequently, data of both sources was merged and analysed. Within the research project “Behaviour of structural components during impact load conditions caused by aircraft fuel tank collision” reinforces concrete plates were damaged by impact loading, see Hering (2020). Afterwards the damaged specimens were investigated tomographically as well as numerically using several methods and models. Aim of the presented research work was to specify an objective comparability of numerical data with experimentally determined damage patterns and based on this, to establish a quantitative damage evaluation.
Structural health monitoring (SHM) intends to improve the management of engineering structures. The number of successful SHM projects – especially SHM research projects – is ever growing, yielding added value and more scientific insight into the management of infrastructure asset. With the advent of the data age, the value of accessible data becomes increasingly evident. In SHM, many new data-centric methods are currently being developed at a high pace. A consequent application of research data management (RDM) concepts in SHM projects enables a systematic management of raw and processed data, and thus facilitates the development and application of artificial intelligence (AI) and machine learning (ML) methods to the SHM data. In this contribution, a case study based on an institutional RDM framework is presented. Data and metadata from monitoring the structural health of the Maintalbrücke Gemünden for a period of 16 months are managed with the RDM system BAM Data Store, which makes use of the openBIS data management software. An ML procedure is used to classify the data. Feature engineering, feature training and resulting data are performed and modelled in the RDM system.