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- Beitrag zu einem Tagungsband (5)
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#### Schlagworte

- Finite element method (6)
- Least-squares adjustment (4)
- Inverse analysis (3)
- Adjustment calculation (2)
- Damage detection and localisation (2)
- Photogrammetry (2)
- Structural analysis (2)
- Compressive strength (1)
- DUCON® (1)
- Ductility (1)

#### Organisationseinheit der BAM

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.

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.

Within the presented research project, experimental and numerical investigations were performed to develop a thin-shelled, modular, mobile element system made of a micro-reinforced ultra-high-performance ductile concrete (DUCON®). Material parameters were experimentally determined to adapt the material model within the numerical analysis applying the Drucker-Prager relationship. Afterwards, for validation of the numerical models, quasi-static and high-velocity impact tests were performed on plate-like structures. Finally, a suitable geometry of transportable barrier elements will be designed, which provides a maximum of resistance against impact by a minimum of weight and a maximum of mobility.

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 integration of finite element method (FEM) into the least-squares adjustment presented in [1] 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. In many literature, see for example [2], parameters are iteratively tuned until the computed FEM results are in accordance with the measurements. In contrast to these debatable approaches, we follow a rigorous and direct method. The “classical” FEM procedure starts with known material constants and ends up with computed fields such as dis-placement or temperature field. We present a method to invert the FEM procedure using the most general least-squares adjustment – the GAUSS-HELMERT Model (GHM). From given fields, the material parameters are directly calculated.

One major ambition in Structural Health Monitoring (SHM) is to develop the ability to detect, identify and localize damage as well as to predict the lifespan of civil structures (Worden et al. 2007). This would allow well-informed decision on whether to repair or to demolish these structures. The word monitoring in SHM brings up several frequently ignored questions: What type of sensors and accuracies are needed to monitor a given structure? Where are the optimal sensor placements? How many sensors are necessary? How to analyse spatially distributed hybrid measurements? Or, in short: What is the sensor configuration best suited for structural health monitoring? If these questions are not explicitly addressed, the usefulness of the measurement data for an evaluation is left to coincidence.