## 2 Construction of Intelligent Automatic Blasting Vibration Monitoring System
### Neural Network Model Based on the Fuzzy-Rough Set  
The neural network based on fuzzy-rough set uses the ability of rough set to find rules from a large amount of data, determine the initial topology structure of FNN, reduce the scale of the network, and improve the training speed of the network, whose effectiveness has been verified in various fields [18; 19]. The topology of the fuzzy neural network can be divided into five layers. The first layer is the input layer, and the input vector is the precise numerical vector or fuzzy quantity. The second layer is the fuzzy layer, which uses Gaussian Function as the membership function. The \(j\) node related to \(x_{i}\) can be expressed as  
\[\mu_{ij}(x_{i})=\exp\left[-\left(x_{i}-m_{ij}\right)^{2}/\sigma_{ij}^{2}\right] \tag{1}\] \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Technical Description** \\ \hline Number of channels & X/Y/Z \\ Frequency & 5–300 Hz \\ Range (speed) & 0–25 cm/s \\ Range (acceleration) & \(\pm\)16 g \\ Sample rate & 6.4 KSps \\ Sample time & 1/2/5/10/15 s \\ Storage capacity & 1 GB \\ Measurement accuracy & 0.1 cm/s, 0.02 g \\ Short-message warning & Support \\ System error & \(<\)5\% \\ Working time & \(>\)72 H \\ Stand-by time & \(>\)240 H \\ Battery capacity & 6400 mAH \\ Operating temperature & \(-\)20 \({}^{\circ}\)C to +70 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The parameters of the TC-6850. Figure 1: (**a**) The network vibrometer: TC-6850; (**b**) The site installation of TC-6850. The third layer is the normalized layer. Assuming that \(x_{i}\) has \(n_{i}\) term vertices used for fuzzy division, the normalized output of \(\mu_{ij}(x_{i})\) can be expressed as \(\mu^{\prime}_{ij}(x_{i})\)  
\[\mu^{\prime}_{ij}(x_{i})=\mu_{ij}(x_{i})/\sum_{k=1}^{n_{i}}\mu_{ij}(x_{i}) \tag{2}\]  
The fourth layer is the regularization layer, which connects normalized nodes with output nodes. Each rule node is connected to only one normalized node. The output of the \(j\) rule node can be expressed as  
\[z_{j}=\prod_{i=1}^{n}\mu^{\prime}_{ij}(x_{i}) \tag{3}\]  
The fifth layer is the defuzzification layer, which realizes the central average anti-fuzzification operation of rule nodes to obtain the numerical output:  
\[y=\sum_{j=1}^{m}G!\prod_{i=1}^{n}\mu^{\prime}_{ij}(x_{i}) \tag{4}\]  
In this paper, the blasting parameters, construction parameters, and site parameters were reduced and fuzzy. A rough set fuzzy neural network model for blasting parameters prediction is constructed through the practice of a large number of data to realize the prediction of blasting parameters. In this paper, to test the time-space effect of blasting vibration propagation, the blasting vibration data of R-Station, Y-Station, and H-Station of an individual line of Qingdao metro were collected. R-Station is an underground station with multiple high-rise buildings on both sides. Data collection points are located on the ground floor of the building on the 2 nd, 6 th, 12 th, and 20 th floors. The inclined well of Y-Station is adjacent to a foundation pit; the closest distance is 15 m. The foundation pit is 25 m deep, and three lithologies are exposed. From top to bottom, lithologies are, respectively, mixed fill, highly weathered granite, and moderately weathered granite. The data collection point is installed on the surrounding structure of the near tunnel side 2 m, 10 m, and 20 m deep. The underground air shaft of H-Station is inclined to the highway of the city, and many pipelines are distributed on the highway's both sides. Data collection points are arranged on both sides of the highway with an arrangement distance of 20 m. Two data collection points are arranged on both sides of the tunnel's central axis. We selected an appropriate installation position to fix the monitoring instrument at the blasting site, and then left the site after turning on the power supply. If the installation position is indoors, we use an indoor AC power supply.