Michael Do¨hler, Falk Hille, Xuan-Binh Lam, Laurent Mevel and Werner Ru¨cker Fig. 3 Destructive damaging; a) cutting through one of the columns, b) and c) successive intersecting of prestressing tendons [10]. A First cut through column G Uplifting column B Second cut through column H Exposing cables and cut through first cable C Lowering column (first step) I Cut through second cable D Lowering column (second step) J Cut through third cable E Lowering column (third step) K Cut through fourth cable F Inserting steel plates Table 1 Damage scenarios during progressive damage test of S101 bridge. lower end. With this action a change in the global structural system was to be implemented. After a second cut a 5 cm slice of the column was removed and the column was lowered for altogether 3 cm until the elastic ductility of the bridge structure was depleted (Figure 3). Afterwards the column was uplifted again to its original position and secured there by steel plates. In a second damage scenario prestressing tendons of one of the beams were to be cut successively (Figure 3). Since the loss of prestressing by deterioration processes is a typical risk for existing RC bridges it was of specific interest to examine the sensitivity of damage identification routines to that kind of structural degradation. All in all three and a quarter of a wire bundle were cut through. Between each intersection pauses of several hours were kept to let the structural system change into a new state of equilibrium. For safety reasons the damaging process was stopped after 3.25 tendons were intersected. An overview of all introduced damages is given in Table 1. 4.4 System Identification Results before Destruction In this paper, primary interest is in the identification of the first five modes in the frequency range [0–18 Hz]. For this, the data was downsampled from sampling rate 500 Hz by factor 8 and after a first examination only the sensors in vertical direction were chosen, as in this frequency range only vertical bending and torsional modes were present. System identification and confidence interval computation was done with the covariance and data driven SSI methods from Section 2.2 with parameters 244
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