# Bridge Health Monitoring via Displacement Reconstruction-Based NB-IoT Technology
## 2 Methodology
### MEMS Accelerometer and NB-IoT Technology  
As described earlier, to have continuous or stable records in the period of the bridge health monitoring, NB-IoT technology is utilized to transmit the acceleration signals from the bridge site to the server. In this study, the ADXL355 acceleration sensor is used, as shown in Figure 5a. ADXL is selected due to its affordable cost and its adequate ability to sense the motion of the bridge. ADXL355 requires power around 3.6 V and has a sensing range of \(\pm\)3 g that is suitable for this study. The sampling frequency is 125 Hz, and the working temperature is from \(-\)40 \({}^{\circ}\)C up to 125 \({}^{\circ}\)C, which is suitable  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Freq.**} & \multicolumn{3}{c}{**Error in the Literature (\(1\times 10^{-3}\))**} & \multicolumn{3}{c}{**Error in the Proposed (\(1\times 10^{-3}\))**} \\ \cline{2-7}  & \(\Delta t\) = 0.02 s & \(\Delta t\) = 0.01 s & \(\Delta t\) = 0.005 s & \(\Delta t\) = 0.02 s & \(\Delta t\) = 0.01 s & \(\Delta t\) = 0.005 s \\ \hline
1 & 7.27 & 6.67 & 6.5 & 5.41 & 4.36 & 4.18 \\
2 & 3.74 & 3.09 & 3.05 & 1.52 & 1.41 & 1.41 \\
3 & 1.95 & 1.65 & 1.6 & 0.73 & 0.65 & 0.63 \\
4 & 1.29 & 1 & 0.89 & 0.5 & 0.41 & 0.38 \\
5 & 0.89 & 0.69 & 0.57 & 0.38 & 0.25 & 0.24 \\
6 & 0.65 & 0.44 & 0.39 & 0.27 & 0.19 & 0.18 \\
7 & 0.46 & 0.35 & 0.28 & 0.19 & 0.15 & 0.14 \\
8 & 0.37 & 0.28 & 0.21 & 0.17 & 0.11 & 0.11 \\
9 & 0.31 & 0.2 & 0.17 & 0.19 & 0.09 & 0.08 \\
10 & 0.24 & 0.17 & 0.13 & 0.13 & 0.08 & 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Root-mean-square error (RMSE) between the proposed and the literature methods [22].  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Optimization Method**} & \multicolumn{2}{c}{**SOS Algorithm**} \\ \hline Number of swarms & 20 \\ \hline Number of iteration & 50 \\ \hline Design variable & Window size (\(N_{w}\)) \\ \hline Objective & Minimization of RMS between exact and reconstructed displacements \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of metaheuristic optimization used in this study.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Withow Size (\(N_{w}\))**} \\ \hline \multirow{2}{*}{**Frequency**} & \multicolumn{3}{c}{**Literature [22]**} & \multicolumn{3}{c}{**Proposed Formula**} \\ \cline{2-7}  & \(\Delta t\) = 0.02 s & \(\Delta t\) = 0.01 s & \(\Delta t\) = 0.005 s & \(\Delta t\) = 0.02 s & \(\Delta t\) = 0.01 s & \(\Delta t\) = 0.005 s \\ \hline
1 & 150 & 300 & 600 & 50 & 100 & 200 \\
2 & 75 & 150 & 300 & 22 & 47 & 97 \\
3 & 50 & 100 & 200 & 14 & 30 & 63 \\
4 & 38 & 75 & 150 & 10 & 22 & 47 \\
5 & 30 & 60 & 120 & 8 & 17 & 37 \\
6 & 25 & 50 & 100 & 6 & 14 & 31 \\
7 & 21 & 43 & 86 & 5 & 12 & 26 \\
8 & 19 & 38 & 75 & 4 & 10 & 23 \\
9 & 17 & 33 & 67 & 4 & 9 & 20 \\
10 & 15 & 30 & 60 & 3 & 8 & 18 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of window size (\(N_{w}\)) between the proposed and the literature methods [22].  
for bridge monitoring tasks. The acceleration sensor ADXL355 is mounted to Nordic Semiconductor nRF9160, as shown in Figure 5(b) that will read the input signal from the sensor.  
nRF9160 is equipped with long term evolution (LTE) with bandwidth 700-960 MHz and 1710-2200 MHz that will send the signal to base transceiver station (BTS) and to the off-site server as shown in Figure 6. The low-cost sensor and NB-IoT package with low power requirements enable the bridge monitoring to be performed at an acceptable price at length.