7.2 Ingenieurbau
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- 7.2 Ingenieurbau (169) (entfernen)
Usually, geometric irregularities are considered as the main cause of ground vibrations from trains. A varying stiffness of the track, the track support and the soil can also generate ground vibrations. The regular stiffness variation of the track on and between the sleepers results in a deterministic dynamic axle load. The random stiffness variation of the track support yields also dynamic axle loads. The dynamic axle loads are generated by the varying wheel displacements under the static axle load by the acceleration of the unsprung mass of the rail vehicle. The random stiffness variation has a second effect. The pulses from the passage of the static axle loads are superposed regularly to the quasi-static response, but also irregularly to yield a “scattered” part of the axle pulses. The same holds for a random variation of the soil stiffness. All these effects of stiffness variations have been calculated by wavenumber-domain multi-beam track models, a random finite-element soil model and the superposition of axle impulses in a stochastic simulation. The results are confronted with many measurements at different sites. It is concluded that the stiffness variation of the track and the soil generate an important ground vibration component near railway lines.
Vibration measurements have many causes and many technical and natural sources. Problems can sometimes be solved by short-term measurements, but in many cases, a long-term measurement is necessary. In long-term measurements of days, weeks, months and even years, it is easy to collect a huge quantity of raw data, but at the end, the post-processing of these data can be exhausting (for example one-year vibration data of a wind energy tower). A software has been developed which con-sists of measuring and evaluation routines where the measuring routines can operate different meas-uring systems and different measuring cards. The main advantage of this software is the fact that the interesting evaluations can be integrated in the measuring process so that the characteristics of the vibration can be extracted without storing all the raw data. Only important time segments are stored, for example train passages. The overall concept of the software and the main evaluation routines will be described in some details. Examples of our measurement experience will illustrate the capabilities of the software. 1) Surveying construction work in nearby sensitive buildings (for example an old wind tunnel), including a stable alarm system and meaningful vibration limits. 2) Prediction of train-induced vibration for a planned building to prevent annoyance and to improve the building design. 3) Modal analysis and long term measurements of several single- or multi-span, concrete or steel bridges 4) Modal and wave analysis of coupled floors in a historical building (“Neues Palais” at Potsdam). 5) Soil properties of various measurement sites (different routines to evaluate the dispersion). Moreover, from many projects, amplitudes, frequencies, and attenuation laws have been collected and analysed for the different sources such as vibratory or impact pile driving and ground compaction, demolition work with different machines, blasting in quarries and in tunnel works, bomb and mine clearing.
Brücken werden durch Inspektionen und teilweise durch Monitoring überwacht, um Daten und Informationen über den Zustand und die Einwirkungen zu sammeln. Auf deren Grundlage werden Entscheidungen hinsichtlich Maßnahmen zur Gewährleistung der Sicherheit und Verfügbarkeit getroffen. In diesem Vortrag werden die Potentiale der Digitalisierung zur Unterstützung und Verbesserung der Brückenüberwachung diskutiert. Insbesondere werden die Bereiche Datenmanagement, Brückenmodellierung und Entscheidungsfindung betrachtet.
Support structures of wind turbines in German offshore wind farms are regularly inspected. Currently, inspection outcomes are generally not systematically recorded. This prevents their consistent handling and processing, which is a key requirement to enable an efficient structural integrity management. As part of the DiMoWind-Inspect project, a data model and reference designation system for such inspection results is developed to facilitate their identification, localization, quantification, tracing and linking throughout the lifetime of a wind farm and beyond. The inspection results together with structural health monitoring results and information on repairs form the basis for assessing and predicting the structural condition, estimating the remaining lifetime, and planning of future inspections, structural health monitoring, and repairs. As a basis for developing a digital structural integrity management, a generic framework is proposed in this paper, which describes the stakeholders, data, models, processes and workflows of the integrity management of support structures in offshore wind farms and their interrelations. The framework adopts a building information modelling approach to describe the information relevant to the structural integrity management. The central digital space of the framework is a common data environment. An implementation of the framework will enable a digital structural integrity management in which inspection, structural health monitoring, repair, assessment and planning outcomes generated throughout the life cycle of a wind farm can be consistently collected, managed, shared and processed by the relevant shareholders.
Die B1 Brücke am Altstädter Bahnhof in Brandenburg an der Havel wurde im Dezember 2019 aufgrund von während einer turnusmäßigen Inspektion entdeckten Rissen und Hohlstellen entlang der vorgespannten Längsträger gesperrt und im Mai 2021 abgebrochen. In der Zwischenzeit wurde die Brücke detailliert überwacht. Vor dem Abriss wurden zudem umfangreiche Untersuchungen zur Bestandsaufnahme und Schadensanalyse sowie Tests moderner Sensorik vorgenommen. Dabei konnte sowohl die richtige, zuvor nicht sicher bekannte Anzahl von Spanngliedern in den Querträgern sicher ermittelt werden als auch durch moderne Varianten der Schwingungsmessungen und der faseroptischen Sensorik zusätzliche Kenntnisse püber das Bauwerksverhalten ermittelt werden . In dem Beitrag werden die Verfahren mit ihren Möglichkeiten und Grenzen vorgestellt, die Ergebnissee an der Brücke in Brandenburg erläutert und zukünftige Einsatzmöglichkeiten diskutiert.
Die aktuelle Instandhaltungsstrategien von Ingenieurbauwerken arbeiten zustandsbasiert und stützen sich auf visuelle Inspektionen in kurzen, starren Intervallen. Beim Übergang zu Predicitive-Maintenance-Strategien können Sensordaten eigesetzt werden um Prognosemodelle der Bauwerke zu aktualisieren. Ein erster Schritt hierzu ist die sensorbasierte Systemidentifikation.
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.
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.