7.2 Ingenieurbau
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Eingeladener Vortrag
- nein (24)
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 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.
Die Linienlastgesetzmäßigkeit gilt nicht für Zuganregung. Die Punktlastgesetzmäßigkeit wird bei kurzen Zügen in größeren Entfernungen erreicht. Bei langen Zügen reduziert sich die Abnahme um r-0,3 für die theoretische exponentielle Dämpfungsabnahme, um r-0,5 für die vereinfachte potentielle Dämpfungsabnahme. Die gemessenen Abnahmereduktionen liegen in diesem Bereich.