## 2 Construction of Intelligent Automatic Blasting Vibration Monitoring System
### The Effectiveness of the Intelligent Automatic Blasting Vibration Monitoring System  
The R station was selected to test the effectiveness of the intelligent automatic blasting vibration monitoring system; the purpose was to test the validity of data collection of TC-6850 and the operability of the system software.  
The TC-6850 and the traditional TC-4850 developed in this paper were placed at the same location on the ground near the blasting site, and both were fixed to the ground and buildings near the blasting site utilizing plaster cement, as shown in Figure 5. The blasting vibration data of two sensors were collected to analyze the sensitivity difference between the two sensors, as shown in Figure 6.  
Figure 3: (**a**) Relative errors between predicted values and measured data of Model-Stratum. (**b**) Relative errors between predicted values and measured data of Model-Hollow. (**c**) Relative errors between predicted values and measured data of Model-Building.  
Figure 4: Schematic diagram of the intelligent automatic blasting vibration monitoring system.  
Figure 5: Sensor mounting position diagram of TC-4850 and TC-6850.  
It is schematically shown in Figure 6 that the difference between the blasting vibration speeds measured on the ground near the blasting point is not evident (see Figure 6a). The blasting vibration data collected by TC-4850 are slightly larger than that collected by TC-6850 on buildings. The overall data are not much different, with a maximum difference of 0.21 cm/s, mainly because TC-4850 is closer to the blasting points. However, during the monitoring process, the TC-4850 sensor has untriggered points, the rate of which reaches 46% (see Figure 6b). The TC-6850 monitoring can usually trigger, indicating that its use is relatively stable. The comparison of TC-6850 network vibration meter and the traditional TC-4850 blasting vibration meter are shown in Table 4.  
In the test, the software ran well and could effectively collect, analyze, and predict data (see Figure 7). The accuracy of mid-range prediction decreases with the increase of data acquisition, indicating that the system can be used effectively in both hardware and software.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **TC-4850** & **TC-6850** \\ \hline Shape & Square & Cylindrical \\ Size & 168 mm \(\times\) 99 mm \(\times\) 64 mm & D = 81 mm, H = 80 mm \\ Weight & 1 kg & 0.56 kg \\ Integration & Separate sensor and host & Combined sensor and host \\ Working temperature & \(-\)10\(-\)75 \({}^{\circ}\)C & \(-\)20\(-\)75 \({}^{\circ}\)C \\ Sealing, waterproof and & Poor & Good \\ Data transmission & USB & WIFI/4G/3G \\ Power & Lithium battery & Lithium battery/Solar energy \\ Operation & Wired & Wireless \\ Data acquisition & Manual & Automatic \\ Data output & PC & PC \& Mobile phone \\ Parameters output & Velocity & Velocity \& Acceleration \\ Data viewing & Paper report & Network data platform \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of TC-4850 and TC-6850.  
Figure 6: **(a)** The comparison of monitoring data between TC-4850 and TC-6850 on the ground. **(b)** The comparison of monitoring data between TC-4850 and TC-6850 on the building.  
Figure 7: **(a)** The ‘home’ page of the intelligent automatic blasting vibration monitoring system. **(b)** The ‘FFT analysis’ page of the intelligent automatic blasting vibration monitoring system. **(c)** The ‘HHT analysis’ page of the intelligent automatic blasting vibration monitoring system. **(d)** The ‘training and prediction of artificial neural network’ page of the intelligent automatic blasting vibration monitoring system.