Assessment of Blasting-InducedGroundVibration in anOpen-Pit Mine under Different Rock Properties

In an open-pit mine slope, rock mass has multiple joint structures and blasting operations have an obvious influence on its stability. +erefore, accurately predicting the blasting vibration is necessary to ensure slope stability. In this study, the blasting vibration signals monitored at a blasting site with different rock masses were used to investigate the attenuation characteristics of blasting vibration through the peak particle velocity (PPV), frequency characteristics, and energy distribution of the blasting vibration signals analyzed with the time-frequency processing method. +e results demonstrated that the main vibration frequency of the blasting vibration of dolomite was wider than that of shale, and these main vibration frequencies occurred at 25 kHz and 14 kHz for dolomite and shale, respectively, at a distance of 50m from the blast area to the vibration monitoring point. With an increase in the distance from 50m to 200m, the main vibration frequencies decreased to less than 5Hz. With increasing joint degree, the attenuation rate of the vibration velocity and energy attenuation of the blasting vibration increase, indicating that the structural parameters of the rock mass (such as the number of joints) have a significant impact on the attenuation law of blasting vibration. Furthermore, a modified equation that can be used for predicting PPV was developed by considering the effect of the number of joints in the rock mass on the blasting vibration. For the same ground vibration readings, the correlation factor increased from 0.8 to 0.85 for the Nicholls-USBM equation and the modified equation, respectively. +e PPV of blasting under different rock masses of the Baideng open-pit phosphorite mine was used to verify the modified equation. +e results show that a modified equation can be used for predicting the PPV of blasting engineering in the Baideng phosphorite mine and that the prediction accuracy is acceptable.


Introduction
e primary operation in open-pit mines is rock blasting.In blasting, only 20%-30% of the energy produced by the explosives is converted into mechanical energy to fragment and displace the rock mass.e remainder of the explosive energy is wasted in the form of blast disturbances, such as rock vibrations, noise, and fly rock, among others.Rock masses are typically characterized by discontinuous and anisotropic inhomogeneous structures.ese discontinuous structures, such as faults, joints, fissures, and fractured zones, are randomly distributed in the rock mass and have important implications for blasting engineering.In blasting operations, natural cracks in the rock mass structure are changed by additional stresses induced by the blasting, and the shear strength of the structural surface is significantly reduced, thereby decreasing the stability of the rock [1,2].Studies on blast vibration harm control are conducted based on analysis of blast vibrations.Peak particle velocity (PPV) is an evaluation criterion for the blasting vibration which has been used for many years; it is predicted by the distance and the charge weight scaling law [3,4].In recent years, many researchers have conducted studies on the mechanical properties and strength of structural planes [5,6] and their influence on the slope stability of the surface structure of rock masses [7][8][9][10][11].However, only a few studies have been carried out regarding the vibration wave propagation characteristics of structural planes.Studies on the vibration effects of blasting have described the transmission and reflection of the stress wave on the surface of the structure using theoretical analysis and have focused primarily on the amplitude attenuation effect of structures on the stress wave [12][13][14].erefore, research on the effect of rock mass media on the attenuation of blasting vibration and accurate analysis of the influence of rock structures on the propagation of vibration attenuation in blasting engineering is worth further investigation.
Over the last several decades, additional sophisticated approaches, such as the finite element method and artificial neural networks, have been used to predict blast vibration [15][16][17].Many researchers have successfully attempted to process and analyze unstable random blast vibration signals using signal time-frequency analysis [18][19][20].Studies have also explored the influence of the distance from the blasting center on the frequency band energy distribution of blast vibration signals, and signal time-frequency analysis has become an effective method for investigating the energy distribution characteristics of a blast vibration signal under rock mass joints.Based on the results for the time-frequency characteristics of blasting signals, research on factors influencing blasting vibration, establishment of a regression formula describing blasting vibration, and analysis of signal time-frequency are effective and important methods for the study of blasting vibration hazards.
In this study, ground vibration monitoring data were obtained from the Baideng open-pit phosphorite mine in China.
e energy spectrum of the blast vibration signal obtained from the measurement data collected during blasting was then analyzed using a time-frequency analysis method.e influence of the rock mass structure on the attenuation of blast seismic waves was explored from the perspective of the blast vibration energy.Finally, these vibration monitoring data were used to develop a new relationship, in which the influence of discontinuous structures is included in the number of joints in rock masses.
e PPV for different rock masses was used to verify this relationship.

General Project Site Information.
is study was conducted at the Baideng open-pit phosphorite mine, which is a subsidiary of the Guangming Chemical Co., Ltd. is mine is located in Anning, Yunnan, China.e Baideng open-pit phosphorite mine lies at a latitude of 24 °52′N and a longitude of 102 °22′E.e dip of the strata is gently inclined and varies from 8 °to 20 °. e strata overlying the ore body are dolomite, shale, and quaternary eluvial alluvium.Deep-hole bench blasting at a height of 10 m is used in this mine, as illustrated in Figures 1 and 2. In the mining area, blasting excavation has been used in dolomite and shale, and these rock strata have developed fault joints.Mechanical parameters of the intact rock masses such as the uniaxial compressive strength and tensile strength were tested according to the methods recommended by the International Society of Rock Mechanics (ISRM) [21][22][23][24].e rock mass wave velocity and the number of joints were also measured [25].e mechanical properties of the intact rock masses are summarized in Table 1.
Ammonium nitrate-fuel oil (ANFO) and nonelectric detonators were used for the blasting excavation.e typical depth and diameter of the blast holes were 11 m and 130 mm, respectively, resulting in a blasting pattern with a burden of 4 m and spacing of 5 m.In-hole delay detonators operated at 400 ms.An initiation pattern was produced on the surface using NONEL with a delay of 25 ms.An example of the initiation network and pattern of drilling holes is shown in Figure 3.

PPV Monitoring and Prediction Methods.
e vibration monitoring points and blasting area were located at the same elevation.e distances between the monitoring points and blasting area were 50, 100, 150, and 200 m, as shown in Figure 4.A blasting vibration recorder (EXP 3850) and a sensor (CDJ-1) were used to monitor blasting vibrations.Each monitoring point was equipped with a vibration sensor.e sensor must be bonded to the surface of the intact rock with plaster, and the location of the monitoring points must be adjusted appropriately to ensure the accuracy of the measurements.us, the distance and azimuth were determined using GPS, as shown in Figure 5.
Over the last half century, researchers have proposed several empirical equations to describe the attenuation characteristics of blast vibrations and predict the attenuation of the PPV [26][27][28].
e PPV equations that have been proposed by different researchers are summarized in Table 2.In most of these equations, the distance from the free face and the maximum charge weight per delay are considered the main parameters influencing PPV prediction.However, is well known that PPV is influenced by other factors, such as the rock strength, rock mass discontinuity conditions, and blast geometry, which have not been explicitly incorporated in these empirical equations.In this study, the Nicholls-United States Bureau of Mines (USBM) empirical equation is used as the prediction equation.

Wavelet Packet Analysis Method.
Wavelet packet analysis is a time-frequency processing method for a nonstationary random signal.is signal is decomposed into two parts, i.e., the low and high frequencies, using low-and high-pass filters, respectively.e two decomposed signals are then further divided into two parts corresponding to the low and high frequencies.us, the signal is continuously decomposed, thereby exhibiting a high-frequency resolution [29].Analysis of the signal continued to the eighth decomposition level.e signal decomposition process is illustrated in Figure 6. e number of frequency bands in the blasting vibration signals at approximately 2 n can be obtained, such that n is the decomposition level of the wavelet packet analysis.If the lowest frequency of the blast vibration signal, s(t), is 0 and the highest frequency is W, the width of 2 Advances in Civil Engineering the frequency band at the nth decomposition level will be W/2 n .Based on the decomposition and reconstruction of the wavelet packet analysis method, the blast vibration signal, s(t), can be expressed as follows:   Advances in Civil Engineering 3

Quaternary eluvial alluvium
where s i,j is the reconstructed signal after wavelet packet decomposition, i is the decomposition level, and j is the order number of the frequency bands after decomposition, j � 0, 1, 2, 3, . . ., 2 i − 1. e energy of each reconstructed signal, E i,j , after wavelet packet decomposition is defined as where x i,j is the amplitude of the discrete points of the reconstructed signal, m is the number of discrete sampling points, and k is the number of discrete points, k � 1, 2, 3, . . ., m. e total energy of the analyzed signal, E 0 , is expressed as follows: e ratio of the energy in each frequency band to the total energy can be derived as follows:

Results and Discussion
Approximately 24 events from 6 blasts were recorded at the Baideng phosphorite open-pit mine, as summarized in Table 3. e blasting areas were grouped into two locations: the dolomite bench and the shale bench.Figure 7 shows the velocity histories of the blasting vibration monitoring.

Attenuation Law for the PPV of Blasting Vibration.
e relationship between PPV and scaled distance revealed by the blasting vibration test data is shown in Figure 8. e PPV of the dolomite and shale decreases steadily with increasing scaled distance.e PPV is larger in the dolomite than in the shale for a constant scaled distance.Lu et al. [30] reported that the total energy of a blast-induced seismic activity is directly proportional to the square of the PPV during the same blasting.erefore, more explosive energy was converted to rock mass vibration in the dolomite bench than that in the shale bench.e field data were analyzed by regression using the least squares fitting method.e attenuation equations for dolomite (PPV d ) and shale (PPV s ) are presented as follows: ese relationships indicate that the PPV decays proportionally to 1/R 2.57 for the dolomite and 1/R 1.94 for the shale with increasing distance for a constant explosive charge weight.
e relationships also indicate that PPV decreases more rapidly in dolomite than shale.

Attenuation Law of Energy for Blasting Vibration.
e sampling rate of the monitoring equipment during the blasting vibration monitoring is 0-4 kHz.Following the Note.R: distance from the blast area to the vibration monitoring point; Q: maximum charge weight per delay; k, n, a, α: site constants.

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Nyquist sampling theory, the highest frequency of the signals being analyzed is 2 kHz.e Daubechies wavelet series exhibits smoothness, compact support, and symmetry compared to a conventional wavelet. is wavelet series has been widely used in the analysis of blasting vibration signals.
In this study, the blasting vibration signal was decomposed into nine layers using wavelet packet analysis, and 2 9   frequency bands were generated in which each frequency band is 2000/2 9 � 3.90625 Hz.
e blast vibration signals underwent decomposition and reconstruction using db5-db10 in the wavelet packet according to Equations ( 2) and ( 4 5 indicate that the energy of the blast vibration signal is widely distributed in the frequency band; however, most of the energy is concentrated at 0-50.78125 Hz. e energy ratios for the eight signals at 0-50.78125 Hz that comprise the total energy are 93.537%,97.24%, 99.867%, 99.91%, 97.88%, 98.94%, 98.52%, and 99.88%.In Figure 9, the time-frequency spectrum distribution is considered in the range of 0-50 Hz to compare the in uence of rock properties and propagation distance on the distribution of blasting vibration energy.e main vibration frequencies of the blasting vibration in dolomite and shale at a distance of 50 m from the blast area are 25 and 14 Hz, respectively. e main vibration frequency decreases gradually with increasing distance from the blast area to the vibration monitoring point.e main vibration frequencies of dolomite and shale are less than 5 Hz at a distance from the blast area of 200 m.e upper limit of the natural vibration frequency of a ground building is 10 Hz. e energy of the blasting vibration waves is in the range of 0-20 Hz and has an obvious e ect on buildings.erefore, the blasting vibration wave in this study is divided into primary (0-20 Hz) and secondary (20-50 Hz) in uence frequency bands, and the energy attenuation law for the di erent frequency bands is analyzed.
e energy of each frequency band is subject to a normalized analysis.
e energy attenuation laws for the blasting vibration in di erent rock masses and frequency bands are shown in Figure 10.In Figure 10, at distances of 50-200 m from the blast area to the vibration monitoring point, the attenuation rate for the dolomite and shale blasting is lower in the primary in uence frequency band than in the secondary in uence frequency band.e attenuation is lower in the shale blasting than in the dolomite blasting for the same frequency band.Figures 9 and 10 show that, in the nearblasting eld (less than 50 m), the blasting vibration velocity and vibration energy of dolomite are higher than that of shale, and more vibration energy is distributed in the higher frequency range.In the far-blasting eld (more than 50 m), the decay rate of the vibration velocity and energy attenuation of the blasting vibration of dolomite is higher than that of shale.e rock mechanics parameters in Table 1 indicate that the uniaxial compression strength, tensile strength, and elastic modulus of dolomite are higher than that of shale, but the dolomite also had more developed joints than the shale.
ese results demonstrate that, in the near-blasting eld, the vibration attenuation of blasting is mainly a ected by the mechanical properties of the rock; with increasing rock strength and elastic modulus, the blasting vibration velocity and vibration energy also increased, which is consistent with the testing results reported by Xu et al. [31].However, in the far-blasting eld, the vibration attenuation of blasting is mainly a ected by the structural characteristics of the rock mass; with increasing number of joints in the rock mass, the attenuation rate of the vibration velocity and energy attenuation of blasting vibration increased.erefore, under geological conditions leading to joint development, the function of the joint should be considered in blasting vibration predictions.

Development of a New Relationship.
For simplicity, the distance from the blast area to the vibration monitoring point (R) and then to the square root of the maximum explosive charge per delay (Q) is called the scaled distance (SD), and their relationship can be expressed as follows: e PPV prediction with the Nicholls-USBM equation is written as follows: where k is the attenuation constant and n is the attenuation index.e results shown in Figure 10 indicate that the attenuation law of the blasting vibration was a ected by the number of joints in the rock mass.e in uence of rock mass joints on the attenuation law of the blasting vibration should be considered comprehensively to predict the exact PPV of blasting, and thus the attenuation equation needs to be modi ed.According to the studies conducted by Simangunsong and Wahyudi [32], the in uence of the number of coal seams should be considered in the prediction of blasting vibration.e proposed modi ed equation for PPV and SD can be expressed as follows:

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If JR is 0, then the new SD JF will become the original SD (Equation ( 6)), in which no in uence of the joint plane is considered in the prediction of PPV.
A total of 24 ground vibration readings at the Baideng open-pit phosphorite mine are listed in Table 2 and were used to examine the relationship between PPV, SD, and SD JF .
e results are plotted in Figure 11.e modi ed equation PPV JF exhibits a better correlation factor (R 2 ) than the original Nicholls-USBM relationship PPV; the correlation factors are 0.85 and 0.80, respectively.e newly modi ed equation, which considers the e ect of joints, demonstrates superior accuracy and applicability for predicting blasting vibration at the Baideng open-pit phosphorite mine.
In an open-pit mine eld, di erent rock properties are observed between the blasting area and the monitoring point, thereby corresponding to di erent joint degrees, as shown in Figure 12.JF in the di erent rock masses are expressed by JF 1 , JF 2 , . .., JF i , and the distances are expressed by R 1 ,R 2 , . .., R i , respectively.erefore, Equation (8) can be expressed as follows: A total of 15 vibration readings were obtained from the Baideng open-pit phosphorite mine, in which the blasting areas and monitoring points are located in the di erent rock masses, i.e., shale and dolomite, as summarized in Table 6.
e modi ed equation and Nicholls-USBM empirical equation were then used to predict the PPV of the remaining readings.e results are shown in Figure 13, in which the monitored PPV is compared with the predicted results.In Figure 13, the PPV predicted by the modi ed equation is visually a better t for the monitoring data than that predicted by the Nicholls-USBM equation.In particular, the prediction results are greater than the monitored data for predicting strong vibrations, which will bene t the analysis of slope stability during blasting.
e results demonstrate that the modi ed equation can be used for predicting the PPV of blasting engineering in the Baideng open-pit phosphorite mine and that the accuracy of the predictions is acceptable.

Conclusions
In this study, blasting vibration signals were monitored for dolomite and shale blasting areas in the Baideng open-pit phosphorite mine.Moreover, the e ect of attenuation laws on the blasting vibration signals in rock masses having di erent properties was analyzed using the wavelet packet time-frequency analysis method.Based on the results, the following conclusions can be made: (1) e blasting vibration attenuation coe cient exhibits a clear relationship with the rock mass.e attenuation law coe cient, k, and n for the blasting vibration waves were (3852, 2.57) and (1367, 1.94) at the dolomite and shale blasting areas in the Baideng open-pit mine, respectively.e attenuation rates of the PPV and energy are higher in the dolomite than in the shale.
(2) e blasting vibration signal spectra based on the wavelet packet time-frequency analysis are mainly composed of low frequencies, whereas the spectra of the blasting vibration signals are mainly composed of low frequencies, with the main vibration frequency in the range of 0-50 Hz. e main vibration frequency decreases with increasing distance to the monitoring points.
(3) e degree of development that the rock mass joints have undergone in uences the attenuation rate of the PPV, and the PPV attenuation rate is high when the number of joints in the rock mass is high.A new

Figure 6 :
Figure 6: Diagram for signal decomposition process.A stands for low frequency, D stands for high frequency, and the numbers 1, 2, and 3 stands for decomposition levels.

Figure 11 :
Figure 11: Relationships between PPV and SD and SD JF .

Figure 13 :
Figure 13: Comparison between predicted and monitored PPV.

Figure 12 :
Figure 12: Schematic diagram of lithology changes between blasting area and monitoring point.

Table 1 :
Mechanical parameters of rock masses.

Table 2 :
Empirical PPV predictor presented by different researches.
), respectively.e errors in the reconstructed signals are listed in Table 4. e statistics in Table 4 indicate that the db8 wavelet packet has the

Table 3 :
Summary of the blasting-induced vibration data at Baideng phosphorite.
smallest reconstruction error; thus, db8 is used to analyze the time-frequency energy of the signal in Figure6.e energy distribution of the wavelet packet frequency band for the blasting vibration signals was obtained for the dolomite and shale.eresultsarelisted in Table5.e results presented in Table

Table 5 :
e wavelet packet frequency band energy distribution for blasting vibration signals.

Table 6 :
e blasting vibration data at bench of di erent rock properties.S,D : distances of shale or dolomite; JF S,D : number of joints in shale or dolomite per 10 m; PPV 1 : predicted PPV by Nicholls-USBM; PPV JF : predicted PPV by modi ed relationship. R