Test Data Interpretation

induced accelerations at TAY-R1 are presented in Figure 6.25. The first two transverse modes are successfully identified under the white noise excitations. Prior to the 0.8𝑔 earthquake input, the identified frequency (2.5 Hz) of the first mode changes little. At 0.9𝑔 PGA, the fundamental frequency starts to drop, corresponding to the cover concrete spalling at the lower end of the towers, and the exposure of transverse and longitudinal reinforcements. For the second mode, the frequency drop happens much earlier at approximately 0.4𝑔 in PGA, corresponding to concrete cracks. When the cracks are fully developed after a PGA of 0.8𝑔, the frequency value of the second mode becomes stable.

Although effective in identifying the change of the first two natural frequencies over time, white noise excitations are difficult to generate for large-scale civil infrastructures. Therefore, the dynamic responses of the bridge model under the earthquake excitations were analyzed to identify the system parameters or detect damage by means of AWT and AMD.

WN01 WN03

WN05 WN06

WN07 WN08

WN10 WN11

WN12 WN13

Figure 6.25. White noise induced accelerations at different input steps

Figure 6.26 shows the acceleration time history at the top of the tower (TAY-R1) and its time-frequency representation under the 0.6𝑔 earthquake excitation. The peak

Time (s) Time (s)

Time (s) Time (s)

Time (s) Time (s)

Time (s) Time (s)

Time (s) Time (s)

acceleration occurs at approximately 4.1 s. Between 1 s and 4.1 s, both the first and second modes of vibration are significant due to the strong earthquake motion, as shown in Figure 6.24, and their frequencies are unchanged over time. At 4.1 s when the PGA occurs, the frequency of the first mode is reduced from 3 Hz to 2.5 Hz, indicating initial cracking at the base of the tower as observed during the test. After 4.1 s, the second mode of vibration becomes less significant likely due to the decreasing earthquake input energy at high frequencies.

(a) Time history – signal in time domain (b) Time-frequency representation by AWT

Figure 6.26. TAY-R1 acceleration under the 0.6 𝑔 earthquake excitation

Based on the AWT of the acceleration response, as depicted in Figure 6.26(b), a bisecting frequency can be determined and AMD is applied to ensure success of the response decomposition. As demonstrated in Figures 6.27 and 6.28, the first and second vibration modes of the tower under the earthquake excitation are separated successfully. It is worth to note that the AMD also filters out the low-amplitude noise that is present in the AWT in Figure 6.26(b)). That is, Figures 6.27(b) and 6.28(b) show much cleaner time-frequency representations of the dynamic response for the approximate first and second modes of vibration. The M-AMD algorithm can thus be performed with better accuracy with the help of AWT and the denoising from AMD.

0 2 4 6 8 10

(a) Approximate modal acceleration (b) CWT

Figure 6.27. Decomposed acceleration for the first mode of vibration under the 0.6 𝑔 earthquake excitation

(a) Approximate modal acceleration (b) CWT

Figure 6.28. Decomposed acceleration for the second mode of vibration under the 0.6 𝑔 earthquake excitation

Figure 6.29 shows the acceleration response at the top of tower (TAY-R1) under the 1.2 𝑔 earthquake input. In comparison with the response under the 0.6 𝑔 earthquake excitation, as shown in Figure 6.26(a), the peak acceleration response in Figure 6.29(a) is more than twice, occurring at approximately 2.7 s, and the frequencies of the first and second modes are significantly lower. As shown in Figure 6.29(b), the frequency of the first mode changes little over time, while the frequency of the second mode drops from 3 Hz before 2.7 s to 2.7 Hz after approximately 4.1 s, which can also be observed from Figure 6.25 from WN12 to WN13. The frequency drop corresponds to the cover concrete spalling around the mid-height and bottom of the tower and partial separation of tower columns and beams due to core concrete crush and rebar fracture at each end of the tower beams.

Furthermore, the second mode of vibration appears more significant than that of the first mode, in terms of signal amplitude and energy.

(a) Time history (b) Time-frequency representation by AWT

Figure 6.29. TAY-R1 acceleration under the 1.2 𝑔 earthquake excitation

Based on Figure 6.29(a), a bisecting frequency of 2 Hz between the first and second modes of vibration is selected in the AMD analysis. The decomposed and filtered signals are presented in Figures 6.30 and 6.31 for the first and second modes of vibration, respectively. With little or no mix of the vibration modes, the AMD successfully separates the two modes of vibration. By comparing Figure 6.31(a) with Figure 6.30(a), it can be seen that the second mode of vibration is significantly stronger than that of the first mode.

(a) Approximate modal acceleration (b) CWT

Figure 6.30. Decomposed acceleration for the first mode of vibration under the 1.2 𝑔 earthquake excitation

0 2 4 6 8 10

(a) Approximate modal acceleration response (b) CWT

Figure 6.31. Decomposed acceleration for the second mode of vibration under the 1.2 𝑔 earthquake excitation

M-AMD is also used to identify dynamic properties of the bridge model by considering the first mode of vibration of the tower to be a SDOF system. Following the same procedure as used in Section 5, the identified natural frequency and damping ratio under the 0.6g and 1.2g earthquake excitations are respectively presented in Figures 6.32 and 6.33.

(a) 0.6 𝑔 earthquake input (b) 1.2 𝑔 earthquake input

Figure 6.32. M-AMD identified natural frequency of the first mode

The general trends of the frequency variations under the 0.6g and 1.2g earthquake excitations are similar to those seen from the AWT in Figures 6.27(b) and 6.30(b), as the frequency change over time does not contain fast-varying components. The damping ratio increases over time as the structural damage, such as concrete spalling and rebar yielding, is accumulated.

(a) 0.6 𝑔 earthquake input (b) 1.2 𝑔 earthquake input

Figure 6.33. M-AMD identified damping ratio of the first mode

6.4. SUMMARY

In this section, the optimized AMD has been developed to solve the mode mix issue when the original AMD is applied to a sampled/discretized data series. Its effectiveness has been demonstrated with sinusoidal, chirp and Duffing signals. By comparing the decomposed and theoretical signals, the errors in all three analytical examples are well below 10%, indicating satisfactory robustness and accuracy in the decomposition of frequency components.

Together with the AWT, the optimized AMD is applied for the damage detection and system identification of the 1/20-scale bridge model under earthquake excitations of various intensities. Although pre- and post-earthquake white noise excitations can be successfully used to identify the change in dynamic properties such as natural frequency and damping ration, the white noise test process is difficult, if not impossible, to execute in applications. Thus, this study is focused on the processing of earthquake responses.

During each application, AWT is first applied to specific seismic responses of the bridge tower in order to identify each dominant mode of vibration. The time-varying or constant bisecting frequency can thus be determined from the time-frequency scalogram given by AWT. The optimized AMD is then performed to separate different modes of vibration. For

0 2 4 6 8 10

the first mode, M-AMD is implemented to further identify the changes of natural frequency and damping ratio more accurately. For the 1/20-scale bridge model, both the optimized AMD and M-AMD can detect damage from the reduction of natural frequencies of the first and second modes since the natural frequencies fluctuate slowly.