According to the neural network theory, combined with the technical characteristicsof the hole-by-hole detonation technology, a BP network model on the forecast forblasting vibration parameters was built.Taking the de...According to the neural network theory, combined with the technical characteristicsof the hole-by-hole detonation technology, a BP network model on the forecast forblasting vibration parameters was built.Taking the deep hole stair demolition in a mine asan experimental object and using the raw information and the blasting vibration monitoringdata collected in the process of the hole-by-hole detonation, carried out some training andapplication work on the established BP network model through the Matlab software, andachieved good effect.Also computed the vibration parameter with the empirical formulaand the BP network model separately.After comparing with the actual value, it is discoveredthat the forecasting result by the BP network model is close to the actual value.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
Given their technical and economic advantages,the application of explosive substances to rock mass excavation is widely used.However,because of serious environmental restraints,there has been an increasing need to use...Given their technical and economic advantages,the application of explosive substances to rock mass excavation is widely used.However,because of serious environmental restraints,there has been an increasing need to use complex tools to control environmental effects due to blast-induced ground vibrations.In the present study,an artificial neural network(ANN)with k-fold cross-validation was applied to a dataset containing 1114 observations that was obtained from published results;furthermore,quantitative and qualitative parameters were considered for ground vibration amplitude prediction.The best ANN model obtained has a maximum coefficient of determination of 0.840 and a mean absolute error of 5.59 and it comprises 17 input parameters,12 neurons in a one-layer hidden layer,and a sigmoid transfer function.Compared with the traditional models,the model obtained using the proposed methodology demonstrated better generalization ability.Furthermore,the proposed methodology offers an ANN model with higher prediction ability.展开更多
基金Supported by the National Natural Science Foundation of China(50778107)
文摘According to the neural network theory, combined with the technical characteristicsof the hole-by-hole detonation technology, a BP network model on the forecast forblasting vibration parameters was built.Taking the deep hole stair demolition in a mine asan experimental object and using the raw information and the blasting vibration monitoringdata collected in the process of the hole-by-hole detonation, carried out some training andapplication work on the established BP network model through the Matlab software, andachieved good effect.Also computed the vibration parameter with the empirical formulaand the BP network model separately.After comparing with the actual value, it is discoveredthat the forecasting result by the BP network model is close to the actual value.
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the support of CERENA–Center for Natural Resources and Environment(strategic project FCT-UID/ECI/04028/2019),Portugal.
文摘Given their technical and economic advantages,the application of explosive substances to rock mass excavation is widely used.However,because of serious environmental restraints,there has been an increasing need to use complex tools to control environmental effects due to blast-induced ground vibrations.In the present study,an artificial neural network(ANN)with k-fold cross-validation was applied to a dataset containing 1114 observations that was obtained from published results;furthermore,quantitative and qualitative parameters were considered for ground vibration amplitude prediction.The best ANN model obtained has a maximum coefficient of determination of 0.840 and a mean absolute error of 5.59 and it comprises 17 input parameters,12 neurons in a one-layer hidden layer,and a sigmoid transfer function.Compared with the traditional models,the model obtained using the proposed methodology demonstrated better generalization ability.Furthermore,the proposed methodology offers an ANN model with higher prediction ability.
