7.2 Ingenieurbau
Filtern
Dokumenttyp
- Beitrag zu einem Tagungsband (20)
- Zeitschriftenartikel (7)
- Vortrag (2)
- Buchkapitel (1)
- Posterpräsentation (1)
Sprache
- Englisch (31) (entfernen)
Schlagworte
- Impact (5)
- Damage characterization (4)
- Damage localization (4)
- Model interpolation (4)
- Planar tomography (4)
- Structural health monitoring (4)
- Compressive strength (3)
- DUCON® (3)
- Damage detection (3)
- Ductility (3)
- E-modulus (3)
- Load vector (3)
- Micro-reinforcement (3)
- Mobile elements (3)
- Numerical modeling (3)
- Quasi-static and dynamic tests (3)
- Reinforced concrete structure (3)
- Statistical evaluation (3)
- Statistical tests (3)
- Stereo photogrammetry (3)
- Temperature rejection (3)
- UHPC (3)
- Ansys Autodyn (2)
- Cable failure (2)
- Cable-stayed bridge (2)
- Crack detection (2)
- Drucker-Prager (2)
- Fatigue (2)
- Fault detection (2)
- Linear parameter varying systems (2)
- Numeric simulation (2)
- Post-impact evaluation (2)
- SDDLV (2)
- Structural Health Monitoring (2)
- Subspace methods (2)
- Subspace-based method (2)
- Subspace-based residual (2)
- Tomographic damage evaluation (2)
- 3D imaging (1)
- Ambient excitation (1)
- Artificial Intelligence (1)
- Artificial intelligence (1)
- Asymptotic local approach (1)
- BIM (1)
- Changing process noise (1)
- Climate chamber (1)
- Coating (1)
- Computer Vision (1)
- Crack Luminescence (1)
- Crack damage detection (1)
- Digital Models (1)
- Drop Tests (1)
- Drop tower (1)
- Environmental changes (1)
- Fault detectionchanging (1)
- Global ambient vibrations (1)
- Hard impact (1)
- Impact damage of reinforced concrete (1)
- Impact damage on reinforced concrete (1)
- Laboratory beam structure (1)
- Laminography (1)
- Luminescence (1)
- Multiple impact (1)
- Nelson’s method (1)
- Non-Destructive Evaluation (1)
- Numerical damage simulation (1)
- Numerical simulation of impact damage (1)
- Probability of Detection (1)
- Probability of detection (1)
- Process noise (1)
- Reinforced concrete (1)
- Reliability (1)
- Research data management (1)
- Residual evaluation (1)
- SHM (1)
- SHM environmental bridges (1)
- Sensitivity vectors (1)
- Soft impact (1)
- Statistical method (1)
- Steel structures (1)
- Stress stiffening (1)
- System Identification (1)
- Temperature effect rejection (1)
- Temperature effects (1)
- Temperature modeling (1)
- Uncertainty (1)
- Uncertainty in reference (1)
- Uncertainty quantification (1)
- Vibration measurement (1)
- Vibration measurements (1)
- Vibrations (1)
- Wind (1)
- temperature (1)
Organisationseinheit der BAM
- 7 Bauwerkssicherheit (31)
- 7.2 Ingenieurbau (31)
- 8 Zerstörungsfreie Prüfung (10)
- 8.5 Röntgenbildgebung (5)
- 8.1 Sensorik, mess- und prüftechnische Verfahren (3)
- 8.0 Abteilungsleitung und andere (2)
- 3 Gefahrgutumschließungen; Energiespeicher (1)
- 3.3 Sicherheit von Transportbehältern (1)
- 7.4 Baustofftechnologie (1)
- VP Vizepräsident (1)
Eingeladener Vortrag
- nein (2)
Damage detection and localization in civil or mechanical structures is a subject of active development and research. A few vibration‐based methods have been developed so far, requiring, for example, modal parameter estimates in the reference and damaged states of the investigated structure, and for localization in addition a finite element model. For structures in operation, temperature has been shown to be a major nuisance to the efficiency of such methods because the modal parameters are varying not only with damage but also due to temperature variations. For detection, a few rejection approaches have been developed. Besides the increased complexity, environmental variation is hardly taken into account in localization approaches. In this paper, we propose a sensitivity‐based correction of the identified modal parameters in the damaged state with respect to the temperature field in the reference state, on the basis of a sensitivity analysis with respect to temperature dependent Parameters of the finite element model in the reference state. The approach is then applied to the stochastic dynamic damage locating vector method, where its improved performance under nonuniform temperature variations is shown in a numerical application on a beam.
Automatic vibration-based structural health monitoring has been recognized as a useful alternative or addition to visual inspections or local non-destructive testing performed manually. It is, in particular, suitable for mechanical and aeronautical structures as well as on civil structures, including cultural heritage sites. The main challenge is to provide a robust damage diagnosis from the recorded vibration measurements, for which statistical signal processing methods are required. In this chapter, a damage detection method is presented that compares vibration measurements from the current system to a reference state in a hypothesis test, where data9
related uncertainties are taken into account. The computation of the test statistic on new measurements is straightforward and does not require a separate modal identification. The performance of the method is firstly shown on a steel frame structure in a laboratory experiment. Secondly, the application on real measurements on S101 Bridge is shown during a progressive damage test, where damage was successfully detected for different damage scenarios.
Temperature variation can be a nuisance that perturbs vibration based structural health monitoring (SHM) approaches for civil engineering structures. In this paper, temperature affected vibration data is evaluated within a stochastic damage detection framework, which relies on a null space based residual. Besides two existing temperature rejection approaches – building a reference state from an averaging method or a piecewise method – a new approach is proposed, using model interpolation. In this approach, a general reference model is obtained from data in the reference state at several known reference temperatures. Then, for a particular tested temperature, a local reference model is derived from the general reference model. Thus, a well fitting reference null space for the formulation of a residual is available when new data is tested for damage detection at an arbitrary temperature. Particular attention is paid to the computation of the residual covariance, taking into account the uncertainty related to the null space matrix estimate. This improves the test performance, contrary to prior methods, for local and global damages, resulting in a higher probability of detection (PoD) for the new interpolation approach compared to previous approaches.
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 subject of damage localization is an important issue for Structural Health Monitoring (SHM) particularly in mechanical or civil structures under ambient excitation. In this paper, the statistical subspacebased damage localization method has been applied on a benchmark application, namely a 1/200 scale model of the Saint-Nazaire Bridge, which is a cable-stayed bridge located on the Loire River near the river’s mouth. The employed damage localization method combines data-driven features with physical parameter information from a finite element model in statistical tests, avoiding typical ill-conditioning problems of FE model updating. Damage is introduced in the mockup for cable failures on some of the 72 cables. The purpose of the experiment is to assess the capability of damage assessment methods to find a cable failure.
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.
The subject of damage localization is an important issue for Structural Health Monitoring (SHM) particularly in mechanical or civil structures under ambient excitation. In this paper, the statistical subspacebased damage localization method has been applied on a benchmark application, namely a 1/200 scale model of the Saint-Nazaire Bridge, which is a cable-stayed bridge located on the Loire River near the river’s mouth. The employed damage localization method combines data-driven features with physical parameter information from a finite element model in statistical tests, avoiding typical ill-conditioning problems of FE model updating. Damage is introduced in the mockup for cable failures on some of the 72 cables. The purpose of the experiment is to assess the capability of damage assessment methods to find a cable failure.
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.
Due to the wide range of applications, the easy production and the large field of use, reinforced concrete (RC) is a widespread building material. This variety of applications is reflected in a wide range of physical material properties. Not only therefor it still is a technical challenge to provide all necessary test conditions for experimentally reproducing dynamic effects under impact loading of RC structures. In this paper we present investigations on the thicknesses of RC plates under low and medium high velocity impact loading by a flat-tipped impactor. The planar tomography setup at BAM is used to visualize the impact damage and to characterize the damage features such as cracks, scabbing and spalling. Further, the comparison of tomography results with those of an applied numeric simulation analysis is used to verify the numeric models for future damage prognosis under impact loading. Using the results of both, the tomographic as well as the FE analysis, different damage features were investigated and compared regarding their validity. Crack damage plays a leading part and the significance of summarized crack values as well as their distribution is analyzed. The total damage value but also the determined damage distribution both provide an input for describing damage as a function of the impactor velocity and plate thickness.
Planar tomography and numerical analysis for damage characterization of impact loaded RC plates
(2020)
The damage analysis of reinforced concrete (RC) is of high interest for reasons of effective maintenance and structural safe-ty of buildings. The damage structures of RC plates loaded by an impact were investigated, applying X-ray planar tomogra-phy and finite element method (FEM). Planar tomography allows getting three-dimensional information of the RC elements and the damage including crack, spalling and scabbing. The FEM model validated on the tomography data justifies the appli-cation for further predictions of the damage description. In this study, we investigated concrete plates of three different thick-ness subjected to impacts at different low- and medium-velocity, whereby the used impactor had a flat tip, which resulted in small penetrations on the front side and scabbing on the rear side. In order to quantify the damage, the damage volume and its distribution through the plate were computed and the correlations between degree of damage and impact velocity were found out.