The blast-induced ground vibration prediction using scaled distance regression analysis is one of the most popular methods employed by engineers for many decades. It uses the maximum charge per delay and distance of m...The blast-induced ground vibration prediction using scaled distance regression analysis is one of the most popular methods employed by engineers for many decades. It uses the maximum charge per delay and distance of monitoring as the major factors for predicting the peak particle velocity(PPV). It is established that the PPV is caused by the maximum charge per delay which varies with the distance of monitoring and site geology. While conducting a production blasting, the waves induced by blasting of different holes interfere destructively with each other, which may result in higher PPV than the predicted value with scaled distance regression analysis. This phenomenon of interference/superimposition of waves is not considered while using scaled distance regression analysis. In this paper, an attempt has been made to compare the predicted values of blast-induced ground vibration using multi-hole trial blasting with single-hole blasting in an opencast coal mine under the same geological condition. Further,the modified prediction equation for the multi-hole trial blasting was obtained using single-hole regression analysis. The error between predicted and actual values of multi-hole blast-induced ground vibration was found to be reduced by 8.5%.展开更多
Blast-induced ground vibration is one of the inevitable outcomes of blasting in mining projects and may cause substantial damage to rock mass as well as nearby structures and human beings.In this paper,an attempt has ...Blast-induced ground vibration is one of the inevitable outcomes of blasting in mining projects and may cause substantial damage to rock mass as well as nearby structures and human beings.In this paper,an attempt has been made to present an application of artificial neural network(ANN)to predict the blast-induced ground vibration of the Gol-E-Gohar(GEG)iron mine,Iran.A four-layer feed-forward back propagation multi-layer perceptron(MLP)was used and trained with Levenberg–Marquardt algorithm.To construct ANN models,the maximum charge per delay,distance from blasting face to monitoring point,stemming and hole depth were taken as inputs,whereas peak particle velocity(PPV)was considered as an output parameter.A database consisting of69data sets recorded at strategic and vulnerable locations of GEG iron mine was used to train and test the generalization capability of ANN models.Coefficient of determination(R2)and mean square error(MSE)were chosen as the indicators of the performance of the networks.A network with architecture4-11-5-1and R2of0.957and MSE of0.000722was found to be optimum.To demonstrate the supremacy of ANN approach,the same69data sets were used for the prediction of PPV with four common empirical models as well as multiple linear regression(MLR)analysis.The results revealed that the proposed ANN approach performs better than empirical and MLR models.展开更多
The blast-induced vibration during excavation by drilling and blasting method has an important impact on thesurrounding structures. In particular, with the development of tunnel engineering, the impact of blasting vib...The blast-induced vibration during excavation by drilling and blasting method has an important impact on thesurrounding structures. In particular, with the development of tunnel engineering, the impact of blasting vibrationon tunnel construction has attracted extensive attention. In this paper, the propagation attenuation characteristicsof blast-induced vibration (PPV, peak particle velocity) on different tunnel structures were systematically studiedbased on the field monitoring data. Initially, the attenuation characteristics of blasting vibration PPV on the lowerbench surface, the side wall of the excavated tunnel and the closely spaced adjacent tunnel were investigated.Subsequently, the capacity of several widely utilized empirical prediction equations to estimate the PPV on tunnelstructures was examined, along with a comparative analysis of their prediction accuracy. The research findingsindicate that it is feasible to predict the PPV on the tunnel structures using empirical equations. The attenuationcharacteristics of blasting vibration PPV are different in different structures and directions. The prediction accuracy of the empirical equations varies, while the discrepancies are minimal. The principal variation amongthese equations lies in the site-specific coefficients k, β, λ, highlighting the differential impact of structural anddirectional considerations on the predictive efficacy. Based on the empirical equation and safe PPV provided bythe blasting vibration safe standards on tunnels of China (GB6722-2014), and considering the influence of allstructures and directions, it is determined that the safe distance of blasting vibration in the tested tunnel projectshould be larger than 20.28–18.31 m, 18.31–16.16 m, and 16.16–13.75 m for blasting vibration frequency locatedin 10 Hz, 10–50 Hz, and >50 Hz.展开更多
This study considered and predicted blast-induced ground vibration(PPV)in open-pit mines using bagging and sibling techniques under the rigorous combination of machine learning algorithms.Accordingly,four machine lear...This study considered and predicted blast-induced ground vibration(PPV)in open-pit mines using bagging and sibling techniques under the rigorous combination of machine learning algorithms.Accordingly,four machine learning algorithms,including support vector regression(SVR),extra trees(ExTree),K-nearest neighbors(KNN),and decision tree regression(DTR),were used as the base models for the purposes of combination and PPV initial prediction.The bagging regressor(BA)was then applied to combine these base models with the efforts of variance reduction,overfitting elimination,and generating more robust predictive models,abbreviated as BA-ExTree,BAKNN,BA-SVR,and BA-DTR.It is emphasized that the ExTree model has not been considered for predicting blastinduced ground vibration before,and the bagging of ExTree is an innovation aiming to improve the accuracy of the inherently ExTree model,as well.In addition,two empirical models(i.e.,USBM and Ambraseys)were also treated and compared with the bagging models to gain a comprehensive assessment.With this aim,we collected 300 blasting events with different parameters at the Sin Quyen copper mine(Vietnam),and the produced PPV values were also measured.They were then compiled as the dataset to develop the PPV predictive models.The results revealed that the bagging models provided better performance than the empirical models,except for the BA-DTR model.Of those,the BA-ExTree is the best model with the highest accuracy(i.e.,88.8%).Whereas,the empirical models only provided the accuracy from 73.6%–76%.The details of comparisons and assessments were also presented in this study.展开更多
Blast-induced vibration produces a very complex signal,and it is very important to work out environmental problems induced by blasting.In this study,blasting vibration signals were measured during underground excavati...Blast-induced vibration produces a very complex signal,and it is very important to work out environmental problems induced by blasting.In this study,blasting vibration signals were measured during underground excavation in carbonaceous shale by using vibration pickup CB-30 and FFT analyzer AD-3523.Then,wavelet analysis on the measured results was carried out to identify frequency bands reflecting changes of blasting vibration parameters such as vibration velocity and energy in different frequency bands.Frequency characteristics are then discussed in view of blast source distance and charge weight per delay.From analysis of results,it can be found that peak velocity and energy of blasting vibration in frequency band of 62.5–125 Hz were larger than ones in other bands,indicating the similarity to characteristics in the distribution band(31–130 Hz)of main vibration frequency.Most frequency bands were affected by blasting source distance,and the frequency band of 0–62.5 Hz reflected the change of charge weight per delay.By presenting a simplified method to predict main vibration frequency,this research may provide significant reference for future blasting engineering.展开更多
文摘The blast-induced ground vibration prediction using scaled distance regression analysis is one of the most popular methods employed by engineers for many decades. It uses the maximum charge per delay and distance of monitoring as the major factors for predicting the peak particle velocity(PPV). It is established that the PPV is caused by the maximum charge per delay which varies with the distance of monitoring and site geology. While conducting a production blasting, the waves induced by blasting of different holes interfere destructively with each other, which may result in higher PPV than the predicted value with scaled distance regression analysis. This phenomenon of interference/superimposition of waves is not considered while using scaled distance regression analysis. In this paper, an attempt has been made to compare the predicted values of blast-induced ground vibration using multi-hole trial blasting with single-hole blasting in an opencast coal mine under the same geological condition. Further,the modified prediction equation for the multi-hole trial blasting was obtained using single-hole regression analysis. The error between predicted and actual values of multi-hole blast-induced ground vibration was found to be reduced by 8.5%.
文摘Blast-induced ground vibration is one of the inevitable outcomes of blasting in mining projects and may cause substantial damage to rock mass as well as nearby structures and human beings.In this paper,an attempt has been made to present an application of artificial neural network(ANN)to predict the blast-induced ground vibration of the Gol-E-Gohar(GEG)iron mine,Iran.A four-layer feed-forward back propagation multi-layer perceptron(MLP)was used and trained with Levenberg–Marquardt algorithm.To construct ANN models,the maximum charge per delay,distance from blasting face to monitoring point,stemming and hole depth were taken as inputs,whereas peak particle velocity(PPV)was considered as an output parameter.A database consisting of69data sets recorded at strategic and vulnerable locations of GEG iron mine was used to train and test the generalization capability of ANN models.Coefficient of determination(R2)and mean square error(MSE)were chosen as the indicators of the performance of the networks.A network with architecture4-11-5-1and R2of0.957and MSE of0.000722was found to be optimum.To demonstrate the supremacy of ANN approach,the same69data sets were used for the prediction of PPV with four common empirical models as well as multiple linear regression(MLR)analysis.The results revealed that the proposed ANN approach performs better than empirical and MLR models.
基金supported by the General Project of China Postdoctoral Science Foundation(2023M742141)the Open Fund of State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mine(SKLMRDPC23KF06)the Talent Introduction Project of Shandong University of Science and Technology(0104060540171).
文摘The blast-induced vibration during excavation by drilling and blasting method has an important impact on thesurrounding structures. In particular, with the development of tunnel engineering, the impact of blasting vibrationon tunnel construction has attracted extensive attention. In this paper, the propagation attenuation characteristicsof blast-induced vibration (PPV, peak particle velocity) on different tunnel structures were systematically studiedbased on the field monitoring data. Initially, the attenuation characteristics of blasting vibration PPV on the lowerbench surface, the side wall of the excavated tunnel and the closely spaced adjacent tunnel were investigated.Subsequently, the capacity of several widely utilized empirical prediction equations to estimate the PPV on tunnelstructures was examined, along with a comparative analysis of their prediction accuracy. The research findingsindicate that it is feasible to predict the PPV on the tunnel structures using empirical equations. The attenuationcharacteristics of blasting vibration PPV are different in different structures and directions. The prediction accuracy of the empirical equations varies, while the discrepancies are minimal. The principal variation amongthese equations lies in the site-specific coefficients k, β, λ, highlighting the differential impact of structural anddirectional considerations on the predictive efficacy. Based on the empirical equation and safe PPV provided bythe blasting vibration safe standards on tunnels of China (GB6722-2014), and considering the influence of allstructures and directions, it is determined that the safe distance of blasting vibration in the tested tunnel projectshould be larger than 20.28–18.31 m, 18.31–16.16 m, and 16.16–13.75 m for blasting vibration frequency locatedin 10 Hz, 10–50 Hz, and >50 Hz.
基金funded by Vietnam National Foundation for Science and Tech-nology Development(NAFOSTED)under Grant No.105.99-2019.309.
文摘This study considered and predicted blast-induced ground vibration(PPV)in open-pit mines using bagging and sibling techniques under the rigorous combination of machine learning algorithms.Accordingly,four machine learning algorithms,including support vector regression(SVR),extra trees(ExTree),K-nearest neighbors(KNN),and decision tree regression(DTR),were used as the base models for the purposes of combination and PPV initial prediction.The bagging regressor(BA)was then applied to combine these base models with the efforts of variance reduction,overfitting elimination,and generating more robust predictive models,abbreviated as BA-ExTree,BAKNN,BA-SVR,and BA-DTR.It is emphasized that the ExTree model has not been considered for predicting blastinduced ground vibration before,and the bagging of ExTree is an innovation aiming to improve the accuracy of the inherently ExTree model,as well.In addition,two empirical models(i.e.,USBM and Ambraseys)were also treated and compared with the bagging models to gain a comprehensive assessment.With this aim,we collected 300 blasting events with different parameters at the Sin Quyen copper mine(Vietnam),and the produced PPV values were also measured.They were then compiled as the dataset to develop the PPV predictive models.The results revealed that the bagging models provided better performance than the empirical models,except for the BA-DTR model.Of those,the BA-ExTree is the best model with the highest accuracy(i.e.,88.8%).Whereas,the empirical models only provided the accuracy from 73.6%–76%.The details of comparisons and assessments were also presented in this study.
基金The authors are grateful for the financial support received from the National Science and Technical Development Foundation of DPRK Korea(No.24-210301).
文摘Blast-induced vibration produces a very complex signal,and it is very important to work out environmental problems induced by blasting.In this study,blasting vibration signals were measured during underground excavation in carbonaceous shale by using vibration pickup CB-30 and FFT analyzer AD-3523.Then,wavelet analysis on the measured results was carried out to identify frequency bands reflecting changes of blasting vibration parameters such as vibration velocity and energy in different frequency bands.Frequency characteristics are then discussed in view of blast source distance and charge weight per delay.From analysis of results,it can be found that peak velocity and energy of blasting vibration in frequency band of 62.5–125 Hz were larger than ones in other bands,indicating the similarity to characteristics in the distribution band(31–130 Hz)of main vibration frequency.Most frequency bands were affected by blasting source distance,and the frequency band of 0–62.5 Hz reflected the change of charge weight per delay.By presenting a simplified method to predict main vibration frequency,this research may provide significant reference for future blasting engineering.
