Development of a blast-induced vibration prediction model using an artificial neural network (original) (raw)
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Bulletin of Engineering Geology and the Environment, 2014
This paper presents a new hybrid artificial neural network (ANN) optimized by imperialist competitive algorithm (ICA) to predict peak particle velocity (PPV) resulting from quarry blasting. For this purpose, 95 blasting works were precisely monitored in a granite quarry site in Malaysia and PPV values were accurately recorded in each operation. Furthermore, the most influential parameters on PPV were measured and used to train the ICA-ANN model. Considering the measured data from the granite quarry site, a new empirical equation was developed to predict PPV. For comparison, a pre-developed ANN model was developed for PPV prediction. The results demonstrated that the proposed ICA-ANN model is able to predict blasting-induced PPV better than other presented techniques.
Journal of sound and vibration, 2006
This paper presents the application of neural network for the prediction of ground vibration and frequency by all possible influencing parameters of rock mass, explosive characteristics and blast design. To investigate the appropriateness of this approach, the predictions by ANN is also compared with conventional statistical relation. Network is trained by 150 dataset with 458 epochs and tested it by 20 dataset. The correlation coefficient determined by ANN is 0.9994 and 0.9868 for peak particle velocity (PPV) and frequency while correlation coefficient by statistical analysis is 0.4971 and 0.0356.
A Neural Network Approach for the Prediction of Ground Vibrations Induced Due to Blasting
2016
This project presents the application of neural networks as well as statistical techniques for prediction of ground vibration by major influencing parameters of blast design. The predictions by artificial neural network (ANN) is compared with the predictions of conventional statistical relation. Ground vibrations and frequency induced due to blasting were monitored at Indian Detonators Limited Rourkela (IDL), Balphimali Bauxite mine (UAIL) and Dunguri Limestone mine (ACC). The neural network was trained by the data sets recorded at the various mine sites. From the analysis it was observed that the correlation coefficient determined for PPV and frequency by ANN was higher than the correlation coefficient of statistical analysis. The correlation coefficient determined for PPV and frequency by ANN for Balphimali Bauxite mine (UAIL) was 0.9563 and 0.9721 respectively and correlation coefficient determined for PPV and frequency by ANN for IDL was 0.9053 and 0.9136 while correlation coeff...
Prediction of blast-induced ground vibration using artificial neural network
International Journal of Rock Mechanics and …, 2009
An attempt has been made to evaluate and predict the blast-induced ground vibration and frequency by incorporating rock properties, blast design and explosive parameters using the artificial neural network (ANN) technique. A three-layer, feed-forward back-propagation neural network having 15 hidden neurons, 10 input parameters and two output parameters were trained using 154 experimental and monitored blast records from one of the major producing surface coal mines in India. Twenty new blast data sets were used for the validation and comparison of the peak particle velocity (PPV) and frequency by ANN and other predictors. To develop more confidence in the proposed method, same data sets have also been used for the prediction of PPV by commonly used vibration predictors as well as by multivariate regression analysis (MVRA). Results were compared based on correlation and mean absolute error (MAE) between monitored and predicted values of PPV and frequency.
Natural Resources Research, 2019
Ground vibration (PPV) is one of the hazard effects induced by blasting operations in openpit mines, which can affect the surrounding structures, particularly the stability of benches and slopes in open-pit mines, and impact underground water, railway, highway, and puzzling for neighboring communities. Therefore, controlling, accurate prediction, and mitigating blast-induced PPV are necessary. This study contributed a new computational model in predicting blast-induced PPV for the science community and practical engineering with high accuracy level. In this study, a novel hybrid artificial intelligence model based on the hierarchical k-means clustering algorithm (HKM) and artificial neural network (ANN), namely a HKM-ANN model, was considered and proposed for predicting blast-caused PPV in openpit mines. Accordingly, input data were first clustered by the HKM algorithm, and then, the ANN models were developed based on the obtained clusters. For this aim, 185 blasting events were collected and analyzed. A hybrid model based on fuzzy c-means clustering (FCM) and support vector regression (SVR), i.e., FCM-SVR model, which was proposed by previous authors was also applied for comparison of results with our proposed HKM-ANN model. In addition, an empirical method, several ANN and SVR models (without clustering), FCM-ANN, and HKM-SVR were developed for comparison purposes. For measuring the performance of the improved models, coefficient determination (R 2), root-mean-square error, and variance account for were used as the performance indicators. The results show that the HKM algorithm played a significant role in improving the accuracy of the ANN models. The proposed HKM-ANN model was the most superior model in estimating PPV caused by blasting operations in this study.
Environmental Earth Sciences, 2008
The aim of this study is to predict the peak particle velocity (PPV) values from both presently constructed simple regression model and fuzzy-based model. For this purpose, vibrations induced by bench blasting operations were measured in an open-pit mine operated by the most important magnesite producing company (MAS) in Turkey. After gathering the ordered pairs of distance and PPV values, the site-specific parameters were determined using traditional regression method. Also, an attempt has been made to investigate the applicability of a relatively new soft computing method called as the adaptive neuro-fuzzy inference system (ANFIS) to predict PPV. To achieve this objective, data obtained from the blasting measurements were evaluated by constructing an ANFIS-based prediction model. The distance from the blasting site to the monitoring stations and the charge weight per delay were selected as the input parameters of the constructed model, the output parameter being the PPV. Valid for the site, the PPV prediction capability of the constructed ANFIS-based model has proved to be successful in terms of statistical performance indices such as variance account for (VAF), root mean square error (RMSE), standard error of estimation, and correlation between predicted and measured PPV values. Also, using these statistical performance indices, a prediction performance comparison has been made between the presently constructed ANFIS-based model and the classical regression-based prediction method, which has been widely used in the literature. Although the prediction performance of the regression-based model was high, the comparison has indicated that the proposed ANFIS-based model exhibited better prediction performance than the classical regression-based model.
A predictive approach for vibration analysis in underground mining operation
2019 6th Intl. Conference on Soft Computing & Machine Intelligence (ISCMI 2019), 2019
Mine fatalities, accidents and incidents are often associated with ground, roof, stope or side instability. Attenuation of rock integrity or the presence of (under)ground pockets of gases or ground waters lead to the collapse of the tunnel. In this paper, the blast vibration in an Open-pit Lignite Mine has been predicted by incorporating the frequency, the charge per delay, the distance and scaled distance using Artificial Neural Network (ANN). The particle velocities (PPV) namely transverse peak, vertical peak and longitudinal peak are successively the output parameters considered. Particle Swarm Optimization (PSO) was used to train the neural network with 54 experimental and monitored blast records. Results were compared based on correlation between monitored and predicted values of PPV. This study demonstrates the possibility to predict and control blasting effect.
Application of soft computing to predict blast-induced ground vibration
Engineering with Computers, 2011
In this study, an attempt has been made to evaluate and predict the blast-induced ground vibration by incorporating explosive charge per delay and distance from the blast face to the monitoring point using artificial neural network (ANN) technique. A three-layer feed-forward back-propagation neural network with 2-5-1 architecture was trained and tested using 130 experimental and monitored blast records from the surface coal mines of Singareni Collieries Company Limited, Kothagudem, Andhra Pradesh, India. Twenty new blast data sets were used for the validation and comparison of the peak particle velocity (PPV) by ANN and conventional vibration predictors. Results were compared based on coefficient of determination and mean absolute error between monitored and predicted values of PPV.
Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting
Environmental Earth Sciences, 2015
One of the most significant environmental issues of blasting operations is ground vibration, which can cause damage to the surrounding residents and structures. Hence, it is a major concern to predict and subsequently control the ground vibration due to blasting. This paper presents two artificial intelligence techniques, namely, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network for the prediction of ground vibration in quarry blasting site. For this purpose, blasting parameters as well as ground vibrations of 109 blasting operations were measured in ISB granite quarry, Johor, Malaysia. Moreover, an empirical equation was also proposed based on the measured data. Several AI-based models were trained and tested using the measured data to determine the optimum models. Each model involved two inputs (maximum charge per delay and distance from the blast-face) and one output (ground vibration). To control capacity performances of the predictive models, the values of root mean squared error (RMSE), value account for (VAF), and coefficient of determination (R 2 ) were computed for each model. It was found that the ANFIS model can provide better performance capacity in predicting ground vibration in comparison with other predictive techniques. The values of 0.973, 0.987 and 97.345 for R 2 , RMSE and VAF, respectively, reveal that the ANFIS model is capable to predict ground vibration with high degree of accuracy.