# Structural Health Monitoring for Prefabricated Building Envelope under Stress Tests
## 3 Results and Test Analysis
### Accelerometers  
In this section, the most relevant results are reported. As mentioned above, the accelerometer results could be comparable, with no significant differences among them. Therefore, in this article, the reported results consist of the data collected and analyzed from the external accelerometer's 'z' direction called FAcc--Z. The data analysis reveals that relevant and significant results for the facade were obtained. The SHM can be performed considering the fixed installation of accelerometers on a wall curtain facade, as explained in Section 2.5. At first, the analysis of the signal over time was performed to understand the global response of the accelerometers subjected to the compliance tests. In fact, a Root Mean Square (RMS) analysis of the signal over time was performed for each test day, as reported in Figure 19. In the following figure (Figure 20), the error bar is shown on RMS data for each day. This value was calculated by applying the accelerometer sensitivity value of \(\pm 10\%\) to the accelerometer signals. The error band is outlined by a dashed line surrounding the accelerometer signal curve. Figure 19: Three modal shapes comparison between pre-compliance test and post-compliance test: Yellow markers represent the mode shape estimated before the compliance test and the cyan ones represent the mode shape estimated after the compliance test. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**0–400 Hz**} & \multicolumn{2}{c}{**400–1000 Hz**} & \multicolumn{2}{c}{**1000–3000 Hz**} \\ \hline
**Pre-Test** & **Post-Test** & **Pre-Test** & **Post-Test** & **Pre-Test** & **Post-Test** \\ \hline
46.9 & 45.6 & 436.24 & 435.11 & 1608.31 & 1593.50 \\ \hline
90.2 & 82.5 & 489.64 & 489.37 & 1650.3 & 1644.79 \\ \hline
106.6 & 103.7 & 534. The structure excitation was evaluated for each day; the graph corresponding to each excitation event is reported in time on the left and in frequency on the right. The tests performed during day 1 were acquired and are shown in Figure 21. During this day, air permeability and rain were performed. The power spectrum estimation function was used to reduce the signal noise and better evaluate information. Each 10 s time history was divided into 0.8 s chunks with a 50% overlap to obtain 25 averages. Figure 20: Signal RMS analysis. In sequence, top figure: RMS of day 1 (**a**); middle figure: RMS of day 2 (**b**); bottom figure: RMS of day 3 (**c**). The tests performed during day 2 were acquired and are shown in Figure 22, are presented following the chronological order of the appearance of events. During this day, wind (repeated), air permeability (repeated), rain (repeated), and building movements were performed. To reduce the signal-noise ratio, the power spectra of the signals were calculated using a frequency-domain averaging procedure. The 10 s time histories were divided into 0.8 s chunks with a 50% overlap to obtain 25 averages. Figure 21: Day 1 tests: respectively, story time and power spectrum for each test: air permeability acceleration/time (**a**); air permeability power spectrum /frequency (**b**); rain (static) acceleration/time (**c**); rain (static) power spectrum /frequency (**d**). Figure 22: _Cont._  
The time history registered during the building movement test Figure 22i is evidenced by several transients. The power spectrum of each transient signal was calculated and then averaged to improve the signal-to-noise ratio. The tests performed during day 3 were acquired and are shown in Figure 23 and are presented following the chronological order of the appearance of events. During this day, fan excitation and impact tests were performed. The power spectrum estimation function was used to reduce the signal noise and better evaluate information. Each 10 s time history was divided into 0.8 s chunks with a 50% overlap to obtain 25 averages.