7.2 Ingenieurbau
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- 2021 (31) (entfernen)
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- Structural systems (6)
- Deterioration (5)
- Bayesian updating (4)
- Inspection (4)
- Structural health monitoring (4)
- Damage detection (3)
- Environmental effects (3)
- Offshore wind energy (3)
- Subspace methods (3)
- Apartment building (2)
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- 7.2 Ingenieurbau (31) (entfernen)
Eingeladener Vortrag
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Automated vibration-based damage detection is of increasing interest for structural health monitoring of engineering structures. In this context, stochastic subspace-based damage detection (SSDD) compares measurements from a testing state to a data-driven reference model in a statistical framework. In this thesis theoretical developments have been proposed to improve the robustness of SSDD for realistic applications conditions. First, a statistical test has been proposed considering the statistical uncertainties about the model obtained from the reference data. This leads to a precise description of the test’s distribution properties and damage detection thresholds. Second, an approach has been developed to account for environmental effects in SSDD. Based on reference measurements at few different environmental conditions, a test is derived with respect to an adequate interpolated reference.
The proposed methods are validated in numerical simulations and applied to experimental data from the laboratory and outdoor structures.
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.
In this article, the passage of different trains over different bridges will be studied for resonant excitation. The intensity of the resonance will be estimated in frequency domain by using three separated spectra. At first, the excitation spectrum of the modal forces is built by the mode shape and the passage time of the train over the bridge. The second spectrum is the frequency response function of the bridge which include the modal frequency, damping and mass. The third part is the spectrum of the axle sequence of the train. The influences of train speed, bridge length, bridge support, track irregularities, and train type on the resonance amplitudes will be analysed for each of these spectra separately for getting a better insight. A variety of axle-sequence spectra and corresponding rules will be presented for different vehicles and trains. As examples, the passage of a slow freight train over a long-span bridge, a normal passenger train over a medium-span bridge, and a high-speed train over a short bridge will be analysed. Corresponding measurements show the amplification, but also the cancellation of the subsequent axle responses. Namely in one of the measurement examples, the first mode of the bridge was amplified and the second mode was cancelled at a low speed of the train and vice versa at a higher speed.
In this presentation, a framework for integrating vibration-based structural health monitoring data into the optimization of inspection and maintenance of deteriorating structural systems is presented. The framework is demonstrated in an illustrative example considering a steel frame subject to fatigue.
In this presentation, we discuss the potential of probabilistic approaches to the design and assessment of offshore foundations. The potential is demonstrated in a numerical example considering a laterally loaded monopile. As an outlook, we present a concept for managing the risk associated with installing large monopiles.
This article presents a solid cohesion model for the simulation of bonded granular assemblies in the frame of 3D discrete element approaches (DEM). A simple viscoplastic cohesion model for 2D geometries is extended to 3D conditions, while its yield criterion is generalized as a hyper-surface in the space of bond solicitations to include torsional moments. The model is then calibrated using experimental results of uniaxial traction at both the microscopic and macroscopic scales with an artificial granular cohesive soil. The paper finally presents some simulated results on the macromechanical sample traction application and briefly discusses the model's current limitations and promising prospects for subsequent works.
This article presents a solid cohesion model for the simulation of bonded granular assemblies in the frame of 3D discrete element approaches (DEM). A simple viscoplastic cohesion model for 2D geometries is extended to 3D conditions, while its yield criterion is generalized as a hyper-surface in the space of bond solicitations to include torsional moments. The model is then calibrated using experimental results of uniaxial traction at both the microscopic and macroscopic scales with an artificial granular cohesive soil. The paper finally presents some simulated results on the macromechanical sample traction application and briefly discusses the model's current limitations and promising prospects for subsequent works.
Prediction of building noise and vibration – 3D finite element and 1D wave propagation models
(2021)
Construction work or traffic excite nearby buildings, and the perceptible or audible vibration can be a nuisance for the inhabitants. The transfer of the vibration from the free field to the building has been calculated by the finite element method for many models in consultancy and research work. The analysis for all storeys of certain building points such as walls, columns and floors unveiled some rules, some typical modes, and some wavetype responses. A simplified building-soil model has been created, which includes well these effects of building-soil resonance, wall/column resonance, floor resonances, and the high-frequency reduction. The model consists of one wall for a wall-type apartment building or a column for each specific part (mid, side or corner) of a column-type office building. The building response in the high-frequency (acoustic) region is calculated as mean values over all storeys and over wider frequency bands, by wave-type asymptotes of an infinitely tall building, and by the soil to wall ratio of impedances. The secondary noise is predicted by Transfer values between the building vibration (center of floors, walls at a room corner) and the sound pressure.
Prediction of building noise and vibration – 3D finite element and 1D wave propagation models
(2021)
Construction work or traffic excite nearby buildings, and the perceptible or audible vibration can be a nuisance for the inhabitants. The transfer of the vibration from the free field to the building has been calculated by the finite element method for many models in consultancy and research work. The analysis for all storeys of certain building points such as walls, columns and floors unveiled some rules, some typical modes, and some wavetype responses. A simplified building-soil model has been created, which includes well these effects of building-soil resonance, wall/column resonance, floor resonances, and the high-frequency reduction. The model consists of one wall for a wall-type apartment building or a column for each specific part (mid, side or corner) of a column-type office building. The building response in the high-frequency (acoustic) region is calculated as mean values over all storeys and over wider frequency bands, by wave-type asymptotes of an infinitely tall building, and by the soil to wall ratio of impedances. The secondary noise is predicted by Transfer values between the building vibration (center of floors, walls at a room corner) and the sound pressure.
Measurements at the foundation, the surrounding soil and nearby buildings have been done during several drop tests of different containers on different foundations. The first measurements have been done on a big foundation where it should be guaranteed that the foundation is rigid and the container is tested properly. It was controlled that the foundation does not absorb more than 2 percent of the energy of the container. Most of the drop energy is lost in shock absorbers. Later on, a smaller drop test facility has been built on the ground but inside an existing building. It had to be controlled by prediction and measurements that the drop test will not damage the building. Tests from different heights on soft, medium, and stiff targets have been done to find out rules which allow to identify acceptable and unacceptable drop tests. Later on, the biggest drop test facility has been built for masses up to 200 t. It was necessary for the design of the foundation to estimate the forces which occur during the drop tests. On the other hand, the acceptable tests should be selected and controlled by measurements where the impact duration is important. Different sensors, accelerometers, accelerometers with mechanical filters, geophones (velocity transducers), strain gauges, and pressure cells have been applied for these tasks. The signals have been transformed to displacements which proved to be best suited for the interpretation of the impact mechanism. Modell calculations have been used to check and understand the dynamic measurements. The simplest law is the conservation of the momentum which is a good approximation if the impact is short. If the soil under the foundation has an influence on the deceleration of the container, the maximum foundation velocity is lower than the simple estimation. The amplitudes of the foundation could also be estimated from the ground vibrations and their amplitude-distance law.
Measurements at the foundation, the surrounding soil and nearby buildings have been done during several drop tests of different containers on different foundations. The first measurements have been done on a big foundation where it should be guaranteed that the foundation is rigid and the container is tested properly. It was controlled that the foundation does not absorb more than 2 percent of the energy of the container. Most of the drop energy is lost in shock absorbers. Later on, a smaller drop test facility has been built on the ground but inside an existing building. It had to be controlled by prediction and measurements that the drop test will not damage the building. Tests from different heights on soft, medium, and stiff targets have been done to find out rules which allow to identify acceptable and unacceptable drop tests. Later on, the biggest drop test facility has been built for masses up to 200 t. It was necessary for the design of the foundation to estimate the forces which occur during the drop tests. On the other hand, the acceptable tests should be selected and controlled by measurements where the impact duration is important. Different sensors, accelerometers, accelerometers with mechanical filters, geophones (velocity transducers), strain gauges, and pressure cells have been applied for these tasks. The signals have been transformed to displacements which proved to be best suited for the interpretation of the impact mechanism. Modell calculations have been used to check and understand the dynamic measurements. The simplest law is the conservation of the momentum which is a good approximation if the impact is short. If the soil under the foundation has an influence on the deceleration of the container, the maximum foundation velocity is lower than the simple estimation. The amplitudes of the foundation could also be estimated from the ground vibrations and their amplitude-distance law.
Structural health monitoring (SHM) of civil structures often is limited due to changing environmental conditions, as those changes affect the structural dynamical properties in a similar way like damages can do. In this article, an approach for damage detection under changing temperatures is presentedand applied to a beam structure. The used stochastic subspace-based algorithm relies on a reference null space estimate, which is confronted to data from the testing state in a residual function. For damage detection the residual is evaluated by means of statistical hypothesis tests. Changes of the system due to temperature effects are handled with a model interpolation approach from linear parameter varying system theory. From vibration data measured in the undamaged state at some few reference temperatures, a model of the dynamic system valid for the current testing temperature is interpolated. The reference null space and the covariance matrix for the hypothesis test is computed from this interpolated model. This approach has been developed recently and was validated in an academic test case on simulations of a mass-spring-damper. In this paper, the approach is validated experimentally on a beam structure under varying temperature conditions in a climate chamber. Compared to other approaches, the interpolation approach leads to significantly less false positive alarms in the reference state when the structure is exposed to different temperatures, while faults can still be detected reliably.
Structural health monitoring (SHM) of civil structures often is limited due to changing environmental conditions, as those changes affect the structural dynamical properties in a similar way like damages can do. In this article, an approach for damage detection under changing temperatures is presentedand applied to a beam structure. The used stochastic subspace-based algorithm relies on a reference null space estimate, which is confronted to data from the testing state in a residual function. For damage detection the residual is evaluated by means of statistical hypothesis tests. Changes of the system due to temperature effects are handled with a model interpolation approach from linear parameter varying system theory. From vibration data measured in the undamaged state at some few reference temperatures, a model of the dynamic system valid for the current testing temperature is interpolated. The reference null space and the covariance matrix for the hypothesis test is computed from this interpolated model. This approach has been developed recently and was validated in an academic test case on simulations of a mass-spring-damper. In this paper, the approach is validated experimentally on a beam structure under varying temperature conditions in a climate chamber. Compared to other approaches, the interpolation approach leads to significantly less false positive alarms in the reference state when the structure is exposed to different temperatures, while faults can still be detected reliably
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.
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.
The reliability of deteriorating structures at time t is quantified by the probability that failure occurs within the period leading up to time t. This probability is often referred to as cumulative failure probability and is equal to the cumulative distribution function of the time to failure. In structural reliability, an estimate of the cumulative failure probability is obtained based on probabilistic engineering models of the deterioration processes and structural performance. Information on the condition and the loading contained in inspection and monitoring data can be included in the probability estimate through Bayesian updating. Conditioning the probability of failure on the inspection or monitoring outcomes available at time t (e.g. detections or no detection of damages) can lead to a reduction in that probability.
Such a drop in the cumulative failure probability might seem counterintuitive since the cumulative failure probability is a non-decreasing function of time. In this paper, we illustrate—with the help of a numerical example—that such a drop is possible because the cumulative probability before and after the updating is not based on the same information, hence not on the same probabilistic model.
The reliability of deteriorating structures at time t is quantified by the probability that failure occurs within the period leading up to time t. This probability is often referred to as cumulative failure probability and is equal to the cumulative distribution function of the time to failure. In structural reliability, an estimate of the cumulative failure probability is obtained based on probabilistic Engineering models of the deterioration processes and structural performance. Information on the condition and the loading contained in inspection and monitoring data can be included in the probability estimate through Bayesian updating. Conditioning the
probability of failure on the inspection or monitoring outcomes available at time t (e.g. detections or no detection of damages) can lead to a reduction in that probability.
Such a drop in the cumulative failure probability might seem counterintuitive since the cumulative failure probability is a non-decreasing function of time. In this paper, we illustrate—with the help of a numerical example—that such a drop is possible because the cumulative probability before and after the updating is not based on the same information, hence not on the same probabilistic model.