## 4 Implementation in Seismic Monitoring of a Large-Scale Building Using Two Vision-Based Systems
### Seismic Behavior and System Identification of the Three-Story Building  
Several ground motion excitations ranging from low to high wave amplitude were applied to the RC building. White noise excitations in terms of low amplitude vibration were applied to the building between the seismic tests with a loading duration of 180 s. A sample of the displacement history from high amplitude (150% scale of a synthetic ground motion seismic excitation [79]) and white noise are given in Figures 9 and 10 for the measured template marked in the figures. The HS system is selected as the reference sensor and the relative difference between each system measurement is presented in detail. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **High-Speed (HS)** & **Standalone DSLR (SD)** \\ \hline Color & Monochrome & Monochrome \\ Format &.tiff & jpg \\ Input image size \(w\times h\) (pixel) & 2560 \(\times\) 2048 & 1920 \(\times\) 1080 \\ Sampling rates, \(f\)s & 32 & 30 \\ (frame-per-second) & 120 & 120 \\ Seismic record duration (s) & 180 & 180 \\ White noise record duration (s) & 180 & 180 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Vision-based system configuration using two sets of cameras for the seismic shake table test. Figure 8: Input and output image with gray level intensity from CLAHE methods. Figure 10: White noise response as measured by high-speed and commercial DSLR system measurements in three principal axes as measured from the marked template. Figure 9: Comparison between high-speed and commercial DSLR system measurements of high-amplitude seismic excitation as measured from the marked template. The seismic response of the building under high-amplitude excitation is shown in Figure 9 based on the measurement of the two vision systems, together with their relative difference. More details are presented in Table 8, which provides a summary of the peak displacement values from both monitoring systems. The peaks observed from the displacement measurement of HS systems were computed as 1118.3 and \(-\)787.4 mm, respectively, whereas the SD system shows the peaks at a maximum of 1113.3 and \(-\)779.2 mm. The relative maximum error in the SD system measurement relative to the HS system was computed as \(-\)28.57 mm (3.63%), which shows that both consumer-grade and high-end high-speed sensor systems are comparable. The building response in three principal axes based on low-amplitude white noise excitation is given in Figure 10 for the two vision systems. These data were analyzed using the SSI-COV algorithm to enable frequency and modal identifications of the building system. The HS sampling rate was 32 Hz, so the SD system acceleration signal was resampled to increase the computational efficiency of the identification algorithm. The signals from both systems were filtered using a 4 th order Butterworth bandpass filter with cutoff frequencies of 3 and 13 Hz. A model order of six was selected for all signals to enable the identification of the first three fundamental modes, with the fitting computed up to the 30 th order to show the stability of the poles at a higher level. The frequency response function of each signal is plotted in Figure 11 together with the stability of the poles. With a model order of six, only the transverse and longitudinal response data are able to extract three stable poles in frequency and damping. Higher modes and different filtering can also be selected to extract more modes in the vertical direction. For the uniformity in signal processing, the filtering and model order selection were set to be similar in this case study. Figure 11: Stabilization plots from output-only SSI-COV method measured for building dynamic modal properties in three directions.