# Predictive Modelling for Blasting-Induced Vibrations from Open-Pit Excavations
## 3 Datasets  
The authors collected 1191 blasting datasets, which are more than six times the datasets used in the previous studies, from 50 diverse construction sites, representing each region of South Korea. The locations of 50 diverse construction sites by 28 administrative districts are depicted in Figure 2. The number of construction sites that were conducted in the same administrative district is expressed in the circle. Even though the construction sites are located in the same administrative district, they are different construction sites. Building and road construction were the main site activities, and open-pit blasting was used at all 50 construction sites. Of the total 1191 datasets, 714 (60%) and 179 (15%) were used for prediction model development as training and validation datasets, respectively. The remaining 298 (25%) were used to test the models. The datasets were randomly designated for Training, Validation, and Testing via PYTHON code.  
Predicting PPV requires a selection of influential factors. Since this study aims to predict the PPV accurately and easily at any open-pit blasting site, the influential factors should not only affect the PPV but also be easily obtained by untrained field staff.  
Eleven common initial influential factors satisfied these conditions from 1191 blasting datasets: type of explosive (TE), charge weight per delay (W), specific weight (SW), length of drilling hole (LH), the height of the bench (HB), burden spacing (BS), hole spacing (HS), type of rock (TR), the distance between blasting and monitoring points (D), site factor K, and site factor m. To use an influential factor as quantitative data, the TE and the TR must be converted to values that express the velocity of detonation (VoD) and the velocity of the P-wave (VoP). The explosive types used at the 50 sites were Megamex, New emulate, Newmite, and Lovex manufactured by Hanwha Corporation [21]. The eight types of rock were gneiss, granite, limestone, schist, shale, andesite, rhyolite, and tuff. The conversion values are summarized in Tables 1 and 2.  
It is necessary to remove or change the initial influential factors to avoid multicollinearity that negatively affects prediction due to the high correlations between independent variables [22]. As shown in Figure 3, factors W, LH, HB, BS and HS are strongly correlated (>0.88) with each other. To remove a strong correlation between influential factors, we removed the LH, HB, BS and HS since W is the most important factor to PPV among the five factors. Finally, we selected seven influential factors relevant to PPV. The units and ranges of the selected factors and PPV are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Explosive Type** & Megamex & New Emulite & NewMITE & LoVEX \\ \hline
**Velocity of Detonation (m/s)** & 6000 & 5900 & 5700 & 3400 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input values for types of rock.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rock Type** & **P-Wave Velocity (m/s)** & **Reference** \\ \hline \hline \begin{tabular}{c} Gneiss \\ Granite \\ Limestone \\ Schist \\ \end{tabular} & \
\begin{tabular}{c} 5500 \\ 5470 \\ 4550 \\ \end{tabular} &
\begin{tabular}{c} [23] \\ [23] \\ [23] \\ [23] \\ [23] \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Input values for types of rock.  
Figure 2: Locations of 50 diverse construction sites by administrative district.