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See detailCondition assessment of bridge structures by damage localisation based on the DAD-method and close-range UAV photogrammetry
Erdenebat, Dolgion UL

Doctoral thesis (2020)

The provided dissertation presents a so-called “Deformation Area Difference (DAD)” method for condition assessment of existing bridges, especially for the detection of stiffness-reducing damages. The ... [more ▼]

The provided dissertation presents a so-called “Deformation Area Difference (DAD)” method for condition assessment of existing bridges, especially for the detection of stiffness-reducing damages. The method is based on the one hand on conventional static load deflection experiments and on the other hand on a high-precision measurement of the structural deflection. The experimental load on the bridge should be generated within the serviceability limit state in order to enable a non-destructive inspection. In the course of the laboratory tests, the most innovative measuring techniques were applied, whereby the photogrammetry has delivered promising results. With the help of additional studies on the influences of camera quality and calibration, the measuring precision of photogrammetry could be brought to its limits. Both the theoretical investigations and the laboratory tests showed the successful use of the DAD method for the identification of local damages. Therefore, the first in-situ experiment was carried out on a single-span, prestressed bridge in Luxembourg. The knowledge gained from this was combined with statistical investigations based on finite element calculations and artificially generated measurement noise effect in order to determine the application limits, such as the achievable measurement precision, identifiable degree of damage, required number of measurement repetitions, influence of the damage position, optimal size of the structural deformation, etc. The development of the DAD method ready for application usefully supplements the state of the art and contributes to the reliable assessment of the bridge condition. [less ▲]

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