Filtern
Erscheinungsjahr
- 2021 (7) (entfernen)
Dokumenttyp
- Vortrag (3)
- Beitrag zu einem Tagungsband (2)
- Buchkapitel (1)
- Posterpräsentation (1)
Sprache
- Englisch (7)
Referierte Publikation
- nein (7) (entfernen)
Schlagworte
- Structural health monitoring (7) (entfernen)
Organisationseinheit der BAM
Eingeladener Vortrag
- nein (3)
Gradual or sudden changes in the state of structural systems caused, for example, by deterioration or accidental load events can influence their load-bearing capacity. Structural changes can be inferred from static and/or dynamic response data measured by structural health monitoring systems. However, they may be masked by variations in the structural response due to varying environmental conditions. Particularly, the interaction of nominally load-bearing components with nominally non-load bearing components exhibiting characteristics that vary as a function of the environmental conditions can significantly affect the monitored structural response. Ignoring these effects may hamper an inference of structural changes from the monitoring data. To address this issue, we adopt a probabilistic model-based framework as a basis for developing digital twins of structural systems that enable a prediction of the structural behavior under varying ambient condition. Within this framework, different types of data obtained from real the structural system can be applied to update the digital twin of the structural system using Bayesian methods and thus enhance predictions of the structural behavior. In this contribution, we implement the framework to develop a digital twin of a simply supported steel beam with an asphalt layer. It is formulated such that it can predict the static response of the beam in function of its temperature. In a climate chamber, the beam was subject to varying temperatures and its static response wass monitored. In addition, tests are performed to determine the temperature-dependent properties of the asphalt material. Bayesian system identification is applied to enhance the predictive capabilities of the digital twin based on the observed data.
Gradual or sudden changes in the state of structural systems caused, for example, by deterioration or accidental load
events can influence their load-bearing capacity. Structural changes can be inferred from static and/or dynamic response data
measured by structural health monitoring systems. However, they may be masked by variations in the structural response due to
varying environmental conditions. Particularly, the interaction of nominally load-bearing components with nominally non-load
bearing components exhibiting characteristics that vary as a function of the environmental conditions can significantly affect the
monitored structural response. Ignoring these effects may hamper an inference of structural changes from the monitoring data. To
address this issue, we adopt a probabilistic model-based framework as a basis for developing digital twins of structural systems
that enable a prediction of the structural behavior under varying ambient condition. Within this framework, different types of data
obtained from real the structural system can be applied to update the digital twin of the structural system using Bayesian methods
and thus enhance predictions of the structural behavior. In this contribution, we implement the framework to develop a digital
twin of a simply supported steel beam with an asphalt layer. It is formulated such that it can predict the static response of the beam
in function of its temperature. In a climate chamber, the beam was subject to varying temperatures and its static response wass
monitored. In addition, tests are performed to determine the temperature-dependent properties of the asphalt material. Bayesian
system identification is applied to enhance the predictive capabilities of the digital twin based on the observed data.
The aim of this work is to improve the current structural health monitoring (SHM) methods for civil structures. A field experiment was carried out on a two-span bridge with a built-in un-bonded prestressing system. The bridge is a 24-metre long concrete beam resting on three bear-ings. Cracks were formed subsequently when a prestressing force of 350 kN was changed to 200 kN, so that different structural states could be demonstrated. The structural assessment of this reference bridge was accomplished by the non-destructive testing using ultrasonic devices and vibration measurements. The ultrasonic velocity variations were investigated by using the coda wave interferometry method. The seismic interferometry technique was applied to the vi-bration recordings to reconstruct the wave propagation field in the bridge. This investigation shows that the wave velocity is sensitive to the current structural state and can be considered as the damage indicator. Overall, the implementation of coda cave interferometry and seismic interferometry technique facilitates structural health monitoring (SHM) in civil engineering.
Changes in the measured response of structural systems can be an indication of structural damages. However, such changes can also be caused by the effect of varying environmental conditions. To detect, localize and quantify changes or damages in structural systems subject to varying environmental conditions, physics-based models of the structural systems have to be applied which explicitly account for the influence of ambient conditions on the structural behavior. Data obtained from the structural systems should be used to calibrate the models and update predictions. Bayesian system identification is an effective framework for this task. In this paper, we apply this framework to learn the parameters of two competing structural models of a reinforced concrete beam subject to varying temperatures based on static response data. The models describe the behavior of the beam in the uncracked and cracked condition. The data is collected in a series of load tests in a climate chamber. Bayesian model class selection is then applied to infer the most plausible condition of the beam conditional on the available data.
Monitoring of reinforced concrete structures to ensure their stability and increase their service-life is a crucial element of a modern infrastructural concept. With classical methods of non-destructive testing and inspection, repeated measurements under comparable conditions are difficult to conduct. Therefore, DFG research unit FOR 2825 CoDA researches the assessment of concrete damage using ultrasound coda wave interferometry and embedded sensors. Embedding the sensors into the monitoring target reduces human and non-human factors influencing repeatability. Using Coda Wave Interferometry (CWI), small velocity changes in the material can be detected by comparison of repeated measurements. The technique is sensitive to damaging changes like cracking as well as to reversible influences like material temperature. The understanding of these different influences on the signal is crucial for the analysis of long-term monitoring data to make an educated assessment of the structure and its integrity. With several laboratory experiments in a climate chamber and a long-term experiment recording an annual cycle in a large model on an outdoor test site in Horstwalde close to Berlin, we try to understand the influence of temperature on the CWI results. The results show that the velocity change calculated by CWI does closely follow the trend of concrete temperature. After one year of data recording with the large model being exposed to environmental variations only, the calculated velocity change resembles the annual temperature curve. The data shows a linear dependency between velocity and temperature change in a range of -0.03 percent per °K to -0.06 percent per °K - regardless of specimen size. An approach to remove temperature influence from the yearly cycle recorded in the large-scale experiment using this linear relation is unable to remove high-frequency variations - especially daily influences. Low-pass filtering the data can eliminate these variations while preserving permanent shifts caused by damages. Although we have shown that the influence of temperature on long term monitoring can be removed to a significant extent, there is still an influence of environmental changes remaining in the data. Possible nonlinear effects and influences not related to temperature need to be investigated in the future.
Due to the importance of reinforced concrete structures for modern society, damage assessment during the entire life-cycle of such structures has become a special interest in non-destructive testing. Using embedded ultrasound sensors in combination with other measurement methods, numerical modeling and self-made data collectors, tailored specifically for monitoring tasks, the German research group DFG FOR CoDA aims to investigate and develop novel methods for damage detection and rapid model updating in reinforced concrete structures. In the first stage of the project, besides the development of custom-built, low-cost data collectors, ultrasonic transducers are embedded in a large, reinforced concrete specimen on a BAM test site near Berlin. In this experiment, the influence of changing environmental conditions (mainly temperature) on the ultrasound signal is investigated using coda-wave interferometry. The results show a correlation between changes in temperature and ultrasonic velocity. Such changes must be taken into consideration in a long-term monitoring setup to distinguish between reversible and permanent changes. By correcting the data using a linear relation between concrete temperature and velocity change to remove the seasonal trends and by low-pass filtering the data to remove daily variations can remove most of the temperature influence on the ultrasound measurements.