7.2 Ingenieurbau
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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.
The statistical subspace-based damage detection technique has shown promising theoretical and practical results for vibration-based structural health monitoring. It evaluates a subspacebased residual function with efficient hypothesis testing tools, and has the ability of detecting small changes in chosen system parameters. In the residual function, a Hankel matrix of Output covariances estimated from test data is confronted to its left null space associated to a reference model. The hypothesis test takes into account the covariance of the residual for decision making. Ideally, the reference model is assumed to be perfectly known without any uncertainty, which is not a realistic assumption. In practice, the left null space is usually estimated from a reference data set to avoid model errors in the residual computation. Then, the associated uncertainties may be non-negligible, in particular when the available reference data is of limited length. In this paper, it is investigated how the statistical distribution of the residual is affected when the reference null space is estimated. The asymptotic residual distribution is derived, where its refined covariance term considers also the uncertainty related to the reference null space estimate. The associated damage detection test closes a theoretical gap for real-world applications and leads to increased robustness of the method in practice. The importance of including the estimation uncertainty of the reference null space is shown in a numerical study
and on experimental data of a progressively damaged steel frame.
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
Im AISTEC Projekt erforscht der FB 7.2 Verfahren zur Bewertung von Verkehrsbrücken auf der Gruandlage von sensorbasierten Bauwerksmessungen. In diesem Vortrag wird ein Überlick über die Forschungsarbeiten des FB 7.2 präsentiert. Des Weiteren wird ein Ausblick zur quantitativen Integration von sensorbasierten Bauwerksmessungen in die risiko-basierte prädiktive Planung von Inspektionen und Reparaturen von Ingenieurbauwerken gegeben.
Der Vortrag stellt die Durchführung und die ersten Ergebnisse der Belastungsfahrten der BAM im Rahmen des AISTEC Projekts an der Maintalbrücke am 19.05. und 20.05.2021 vor. Es wurden Tragwerksreaktionen des Bauwerks mit dem installierten Dauermonitoringsystem und insbesondere dem Betongelenk mit zusätzlich installierter Sensorik aufgezeichnet. Für die Ermittlung der Lastposition wurden mehrere Verfahren eingesetzt und verglichen.
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.
Nach den bisherigen Regeln werden Bauwerksprüfungen von Brücken in starr definierten Intervallen durchgeführt. Diese starre, periodische, zustandsbasierte Instandhaltungsstrategie soll zukünftig durch eine flexible und prädiktive Instandhaltung ersetzt werden. Hierbei sollen Inspektionen und Instandhaltungsmaßnahmen unterstützt durch autonome Systeme auf der Grundlage von Monitoringdaten geplant und zusätzlich bei unvorhergesehenen Ereignissen ausgelöst werden. Im Rahmen des Verbundforschungsvorhabens AISTEC wird ein Vorgehen für Großbauwerke und kleinere Regelbauwerke zur Umsetzung eines kombinierten globalen, schwingungsbasierten und quasistatischen Monitorings entworfen. An der Maintalbrücke Gemünden – einer semiintegralen Rahmenbrücke aus Spannbeton auf der Schnellfahrstrecke Hannover–Würzburg – wurde als Demonstrator ein Dauermonitoring realisiert.
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.
Fatigue is one of the main deterioration processes affecting the performance of welded steel support structures of wind turbines in offshore wind farms. In this contribution, we present a probabilistic physics-based fatigue deterioration model of a wind farm that accounts for the stochastic dependence among the fatigue behavior of different hotspots at turbine and wind farm level. The dependence exists because of uncertain common influencing factors such as similar material properties, fabrication qualities and load conditions. These system effects signify that an inspection of one hotspot provides indirect information of the condition of the remaining hotspots and thus enable an optimization of the inspection effort for a wind farm.
The wind farm model consists of two levels: A turbine support structure (level 1 system model) is represented by its fatigue hotspots and their stochastic dependence. The fatigue performance of a hotspot is described by a probabilistic fracture mechanics (FM) fatigue model. The probabilistic model of the parameters of the FM fatigue model is calibrated based on design data and is thus directly linked to the design of the turbine support structures. Dependence among the fatigue performances of different hotspots in a turbine support structure is modeled by introducing correlations between the stochastic parameters of the FM fatigue models. A wind farm (level 2 system model) consists of different turbine support structures (level 1 system models). Additional correlations are introduced at wind farm level to account for the dependence among the fatigue behavior of hotspots belonging to different turbine support structures. The wind farm model enables the computation of (marginal) hotspot fatigue reliabilities, system fatigue reliabilities of individual turbine support structures and the system fatigue reliability of an entire wind farm. The probabilistic model of the parameters of the two-level system model can be consistently updated with inspection outcomes using Bayesian methods. The updated probabilistic model of the model parameters then forms the basis for updating the estimates of the fatigue reliabilities.
We implement the wind farm model in a software tool named SysPark. The tool provides the means for planning inspections at wind farm level using an adaptive reliability-based threshold approach. In this approach, the first inspection campaign is planned in the year before the fatigue failure rates of the hotspots with the lowest fatigue reliabilities exceed a threshold failure rate. Once inspection results become available, the probabilistic model of the parameters of the wind farm model is updated. If repairs are performed, the wind farm model is additionally modified to describe the behavior of the repaired hotspots. The updated and modified model then enables the planning of the next inspection campaign and so on. The software tool is demonstrated in a case study considering a generic wind farm consisting of turbines with jacket support structures.
There is a strong need for cost-effective mitigation measures for
turnouts. SBB has initiated a series of examinations using different methodologies to gain a deeper understanding of the excitation mechanisms at low frequencies, in addition to that obtained in the RIVAS project. To date it is not yet clear what constitutes a complete measurement data set that would enable understanding most of the vibration excitation mechanisms in turnouts. Increasing vibration at turnouts in comparison to normal track is observed for all measured frequencies.
The different methodologies are presented in the paper. Under-sleeper pads (USP) are a cost-effective method to reduce vibration at frequencies above 63 Hz (1/3 octave), but there is probably no improvement for frequencies below 63 Hz. A first test of new frog geometry did not show relevant improvements in Vibration emission in comparison to a reference frog geometry. Axle box acceleration measurements are an interesting method to identify defects in a turnout. A specialized measurement system of rail roughness could identify certain geometry Problem areas for some frogs. Noise increases also are observed at turnouts for frequencies ranging between 80 to 1000 Hz. The use of railway source models to calculate
contact forces for ballasted track and turnouts seems promising, in particular for understanding the influence of ground.
The present contribution evaluates four measuring series made by the Federal Institute of Material Research and Testing for the relations between train speed and ground vibration amplitudes. This experimental evaluation is supported by the simulation of the train passages at the different sites by using appropriate excitation mechanisms and forces as well as layered soil models which have been derived from impact measurements at each site.
This document presents the specification for the execution and evaluation of high-speed tensile tests on reinforcement bar coupler systems. This specification was developed at BAM - Bundesanstalt für Materialforschung und -prüfung (Federal Institute for Materials Research and Testing, Germany) - following test principles from related international standards.
The present document represents the latest status of the test specification. It is noted that until 2010 the test procedure was characterised on a test velocity based on L0. In an improved test conception and after intensive investigations in cooperation with industry partners, this procedure has been updated to consider instead a test velocity based on Lr, since this warrants more comparable and meaningful results.
Fault detection for linear parameter varying systems under changes in the process noise covariance
(2020)
Detecting changes in the eigenstructure of linear systems is a comprehensively investigated subject. In particular, change detection methods based on hypothesis testing using Gaussian residuals have been developed previously. In such residuals, a reference model is confronted to data from the current system. In this paper, linear output-only systems depending on a varying external physical parameter are considered. These systems are driven by process noise, whose covariance may also vary between measurements. To deal with the varying parameter, an interpolation approach is pursued, where a limited number of reference models – each estimated from data measured in a reference state – are interpolated to approximate an adequate reference model for the current parameter. The problem becomes more complex when the different points of interpolation correspond to different noise conditions. Then conflicts may arise between the detection of changes in the eigenstructure due to a fault and the detection of changes due to different noise conditions. For this case, a new change detection approach is developed based on the interpolation of the eigenstructure at the reference points. The resulting approach is capable of change detection when both the external physical parameter and the process noise conditions are varying. This approach is validated on a numerical simulation of a mechanical system.
Fault detection for linear parameter varying systems under changes in the process noise covariance
(2020)
Detecting changes in the eigenstructure of linear systems is a comprehensively investigated subject. In particular, change detection methods based on hypothesis testing using Gaussian residuals have been developed previously. In such residuals, a reference model is confronted to data from the current system. In this paper, linear output-only Systems depending on a varying external physical parameter are considered. These systems are driven by process noise, whose covariance may also vary between measurements. To deal with the varying parameter, an interpolation approach is pursued, where a limited number of reference models { each estimated from data measured in a reference state { are interpolated to approximate an adequate reference model for the current parameter. The problem becomes more complex when the different points of interpolation correspond to different noise conditions. Then con icts May arise between the detection of changes in the eigenstructure due to a fault and the detection of changes due to different noise conditions. For this case, a new change detection approach is developed based on the interpolation of the eigenstructure at the reference points. The resulting approach is capable of change detection when both the external physical parameter and the process noise conditions are varying. This approach is validated on a numerical simulation of a mechanical system.