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By means of a small-scale truss bridge, the ability of the Measurement- and Model-based Structural Analysis to detect and localize damage was examined. Although there was no noteworthy difficulty in detecting damage, it turned out that damage localization responds sensitively to systematic influences, i.e. non-modelled properties of the mechanical model. Therefore, another experiment is being conducted to re-examine the Measurement- and Model-based Structural Analysis. For this purpose, the bending test is carried out as it has been already theoretically respectively numerically discussed. In this attempt, the systematic influences such as residual stress are kept as low as possible.
By means of a small-scale truss bridge, the ability of the Measurement- and Model-based Structural Analysis to detect and localize damage was examined. Although there was no noteworthy difficulty in detecting damage, it turned out that damage localization responds sensitively to systematic influences, i.e. non-modelled properties of the mechanical model. Therefore, another experiment is being conducted to re-examine the Measurement- and Model-based Structural Analysis. For this purpose, the bending test is carried out as it has been already theoretically respectively numerically discussed. In this attempt, the systematic influences such as residual stress are kept as low as possible.
By means of a small-scale truss bridge, the ability of the Measurement- and Model-based Structural Analysis to detect and localise damage was examined in. Although there was no noteworthy difficulty in detecting damage, it turned out that damage localisation responds sensitively to systematic influences, i.e. non-modelled properties of the mechanical model. Therefore, another experiment is being conducted to re-examine the Measurement- and Model-based Structural Analysis. For this purpose, the bending test is carried out as it has been already theoretically respectively numerically discussed in. In this attempt, the systematic influences such as residual stress are kept as low as possible.
The Measurement- and Model-based Structural Analysis (MeMoS) integrates a finite element model into least squares adjustment and thus allows to evaluate a mechanical model and measurements in a combined analysis. To examine the capability to detect and localise damage using this integrated analysis MeMoS, a small-scale truss bridge made of aluminium profiles is built as a test specimen for this purpose.
To examine the capability to detect and localise damage using the Measurement- and Model-based Structural Analysis (MeMoS), a small-scale truss bridge (1520 mm × 720 mm × 720 mm) made of aluminium profiles is built as a test specimen for this purpose. The truss frame of the test bridge is made of aluminium profiles with a sophisticated design of the cross-sectional area. In comparison, with solid profiles, only a fraction of the material is needed to produce the profiles, while their bending resistance decreases slightly. The profiles are built into a truss frame by connecting them by means of fastening sets made of steel. The bridge model is mounted on four steel bearings which each of them consist of a cylinder arranged between two plates. Fixed bearings are made by holding onto one end of the bridge. The bridge is subjected by an external load by placing a heavy object beneath it. At the same time, measurements can be conducted below the bridge. Therefore, the bridge specimen is elevated by attaching it on a pedestal with four columns. Damages can be induced by loosening the
fastening pieces.
To examine the capability to detect and localise damage using the Measurement- and Model-based Structural Analysis (MeMoS), a small-scale truss bridge (1520 mm × 720 mm × 720 mm) made of aluminium profiles is built as a test specimen for this purpose. The truss frame of the test bridge is made of aluminium profiles with a sophisticated design of the cross-sectional area. In comparison, with solid profiles, only a fraction of the material is needed to produce the profiles, while their bending resistance decreases slightly. The profiles are built into a truss frame by connecting them by means of fastening sets made of steel. The bridge model is mounted on four steel bearings which each of them consist of a cylinder arranged between two plates. Fixed bearings are made by holding onto one end of the bridge. The bridge is subjected by an external load by placing a heavy object beneath it. At the same time, measurements can be conducted below the bridge. Therefore, the bridge specimen is elevated by attaching it on a pedestal with four columns. Damages can be induced by loosening the fastening pieces.
The use of open GIS standards offers a broad variety of potential, particularly in the field of data exchange, data storage, and interoperability. GML and CityGML are excellent examples for the ontological description of real world objects by means of an open standard whereas SensorML serves to describe measurements, sensors and measuring platforms. The use of such standards offers not only the possibility of using a common standardised language, but also the use of open service standards. The combination of spatial data and sensor standards in services and service-oriented architectures goes far beyond previous existing solutions on the market and provides a novel platform for monitoring structures. That in fact is far more than a simple data storage model. The methods and models presented in this contribution allow a direct integration of sensor data and its provision through an open standard language. In this case, all the intermediate steps at any time through an open service interface are addressed and may be made available and provided to different actors and stakeholders participating in a construction scenario. The great potential and the added value of such an information system is the permanent availability of measurement and object data and an associated integrated analysis of sensor data in combination with a finite element model (FEM). The automatic derivation of a finite element model from the 3D structure model, the visualisation of FEM, the provision of raw (measurement) data and sensor information for each time of measurement transform the platform into a universal tool in the field of structural monitoring. This contribution introduces the individual components, the standards used and the interaction between the components to an overall system.
Many engineering structures are made of composite materials or metal foam. To simulate the deformational behaviour of these structures often requires a high number of discretisation elements. This in turn yields a very large system of linear
equations that are extremely time and memory consuming or practically impossible to solve. It is therefore desirable to find an approach to overcome this obstacle.
Many engineering structures are made of composite materials or metal foam. To simulate the deformational behaviour of these structures often requires a high number of discretisation elements. This in turn yields a very large system of linear equations that are extremely time and memory consuming or practically impossible to solve. It is therefore desirable to find an approach to overcome this obstacle.
Many engineering structures are made of composite materials or metal foam. To simulate the deformational behaviour of these structures often requires a high number of discretisation elements. This in turn yields a very large system of linear equations that are extremely time and memory consuming or practically impossible to solve. It is therefore desirable to find an approach to overcome this obstacle.
Many engineering structures are nowadays made of composite materials or metal foam. These modern engineering materials contain very complex inner geometry. To simulate the deformational behaviour of these structures often requires a high number of discretisation elements. This in turn yields a very large system of linear equations that are extremely time and memory consuming or practically impossible to solve. It is therefore desirable to find an approach to overcome this obstacle. In this paper a numerical method is proposed to find an approximate substitute model for geometrical complex structures.
The determination of material parameters from displacement field measurement is being examined for linear elastic solid. A frequently used approach to compute material constants can be found in many studies. Even though they presented the approach in many different variations, but in the end they are essentially based on the same algorithm: Parameters are iteratively tuned until the computed results are in accordance with the measurements. The main drawback of this approach is that mainly commercial software is used that hinders us to investigate its inner evaluation process. This leads to the question, how the results from this commercial software can be trusted. On the contrary to these debatable approaches, we present a method that inverts the procedure of finite element method by using the most general model for a least-squares adjustment – the GAUSS-HELMERT Model.
The integration of finite element method (FEM) into the least-squares adjustment presented in is further extended for a joint evaluation of an elastostatic model and displacement field measurement. For linear solids which obey the Hooke's law, the material parameters determination from measurements is being examined.
The best-known discretization methods for solving engineering problems formulated as partial differential equations are finite difference method (FDM), finite element method (FEM) and finite volume method (FVM). While the finite volume method is used in fluid mechanics, the finite element method is predominant in solid state mechanics. At first glance, FVM and FEM are two highly specialized methods. However, both methods can solve problems of both solid mechanics and fluid mechanics well. Since experimental mechanics deals not only with solid state physics but also with fluid mechanics problems, we want to understand FVM in the sense of FEM in this work. In the long term, we want to use the variational calculus to unify many important numerical methods in engineering science into a common framework. In this way, we expect that experiences can be better exchanged between different engineering sciences and thus innovations in the field of experimental mechanics can be advanced. But in this work, we limit ourselves to the understanding of the FVM with the help of the variational calculus already known in FEM. We use a simple 1D Poisson equation to clarify the point. First, we briefly summarize the FVM and FEM. Then we will deal with the actual topic of this paper, as we establish the FEM and the FVM on a common basis by variation formulation. It is shown here that the FVM can be understood in terms of the finite element method with the so-called Galerkin-Petrov approach.
The best-known discretization methods for solving engineering problems formulated as partial differential equations are finite difference method (FDM), finite element method (FEM) and finite volume method (FVM). While the finite volume method is used in fluid mechanics, the finite element method is predominant in solid state mechanics. At first glance, FVM and FEM are two highly specialized methods. However, both methods can solve problems of both solid mechanics and fluid mechanics well. Since experimental mechanics deals not only with solid state physics but also with fluid mechanics problems, we want to understand FVM in the sense of FEM in this work. In the long term, we want to use the variational calculus to unify many important numerical methods in engineering science into a common framework. In this way, we expect that experiences can be better exchanged between different engineering sciences and thus innovations in the field of experimental mechanics can be advanced. But in this work, we limit ourselves to the understanding of the FVM with the help of the variational calculus already known in FEM. We use a simple 1D Poisson equation to clarify the point. First, we briefly summarize the FVM and FEM. Then we will deal with the actual topic of this paper, as we establish the FEM and the FVM on a common basis by variation formulation. It is shown here that the FVM can be understood in terms of the finite element method with the so-called Galerkin-Petrov approach.
The present work is intended to make a contribution to the monitoring of civil engineering structures. The detection of damage to structures is based on the evaluation of spatially and temporally distributed hybrid measurements. The acquired data can be evaluated purely geometrically or physically. It is preferable to do the latter, since the cause of damage can be determined by means of geometrical-physical laws in order to be able to intervene in time and ensure the further use of the structures. For this reason, the continuum mechanical field equations in conjunction with the finite element method and hybrid measurements are combined into a single evaluation method by the adjustment calculation. This results in two challenges.
The first task deals with the relationship between the finite element method and the method of least squares. The finite element method solves certain problem classes, which are described by a system of elliptical partial differential equations. Whereas the method of least squares solves another class of problems, which is formulated as an overdetermined system of equations. The striking similarity between both methods is known since many decades. However, it remains unresolved why this resemblance exists. The contribution is to clarify this by examining the variational calculus, especially with regard to its methodological procedure. Although the well-known Gauss-Markov model within the method of least squares and the finite element method solve inherently different problem classes, it is shown that both methods can be derived by following the same methodological steps of the variational calculus. From a methodical viewpoint, this implies that both methods are not only similar, but actually the same. In addition, it is pointed out where a possible cross-connection to other methods exists.
The second task introduces a Measurement- and Model-based Structural Analysis (MeMoS) by integrating the finite element method into the adjustment calculation. It is shown in numerical examinations how this integrated analysis can be used for parameter identification of simple as well as arbitrarily shaped structural components. Based on this, it is examined with which observation types, with which precision and at which location of the structure these measurements must be carried out in order to determine the material parameters as precisely as possible. This serves to determine an optimal and economic measurement set-up. With this integrated analysis, a substitute model of a geometrically complex structure can also be determined. The issue of the detection and localisation of damage within a structure is studied by means of this structural analysis. The Measurement and Model-based Structural Analysis is validated using two different test setups, an aluminum model bridge and a bending beam.
The present work is intended to make a contribution to the monitoring of civil engineering structures. The detection of damage to structures is based on the evaluation of spatially and temporally distributed hybrid measurements. The acquired data can be evaluated purely geometrically or physically. It is preferable to do the latter, since the cause of damage can be determined by means of geometrical-physical laws in order to be able to intervene in time and ensure the further use of the structures. For this reason, the continuum mechanical field equations in conjunction with the finite element method and hybrid measurements are combined into a single evaluation method by the adjustment calculation. This results in two challenges.
The first task deals with the relationship between the finite element method and the method of least squares. The finite element method solves certain problem classes, which are described by a system of elliptical partial differential equations. Whereas the method of least squares solves another class of problems, which is formulated as an overdetermined system of equations. The striking similarity between both methods is known since many decades. However, it remains unresolved why this resemblance exists. The contribution is to clarify this by examining the variational calculus, especially with regard to its methodological procedure. Although the well-known Gauss-Markov model within the method of least squares and the finite element method solve inherently different problem classes, it is shown that both methods can be derived by following the same methodological steps of the variational calculus. From a methodical viewpoint, this implies that both methods are not only similar, but actually the same. In addition, it is pointed out where a possible cross-connection to other methods exists.
The second task introduces a Measurement- and Model-based Structural Analysis (MeMoS) by integrating the finite element method into the adjustment calculation. It is shown in numerical examinations how this integrated analysis can be used for parameter identification of simple as well as arbitrarily shaped structural components. Based on this, it is examined with which observation types, with which precision and at which location of the structure these measurements must be carried out in order to determine the material parameters as precisely as possible. This serves to determine an optimal and economic measurement set-up. With this integrated analysis, a substitute model of a geometrically complex structure can also be determined. The issue of the detection and localisation of damage within a structure is studied by means of this structural analysis. The Measurement and Model-based Structural Analysis is validated using two different test setups, an aluminum model bridge and a bending beam.