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Shi X.Z.,Central South University | Shi X.Z.,Postdoctoral Workstation of Daye Nonferrous Metals Company | Chen S.H.R.,Central South University
Soil Dynamics and Earthquake Engineering | Year: 2011

Blasting induced vibration is one of the fundamental problems in the open-pit mines and intense vibration can cause critical damage to structures and plants nearby the open-pit mines, especially to the final pit wall's stability. It is very important to study how to control vibration induced by blasting in the mitigation of negative effects of blasting in open-pit mines. This study aims to examine the propagation of blasting induced ground vibrations and find the feasible approaches to reduce the harmful effects of vibrations induced by blasting on the final pit wall's stability. For this purpose, a series of field experiments were conducted in XinQiao Mining Co. Ltd. Sixty-six events and the blasting parameters of these shots were carefully recorded. During the statistical analysis of the collected data, the predictor equation proposed by the United States Bureau of Mines (USBM) was used to establish a relationship between the Peak Particle Velocity (PPV) and the Scaled Distance (SD) factor. The relationship between PPV and SD was determined and proposed to be used in this open-pit mine. Control of maximum charge amount per delay and the selection optimum interval time to reduce the intensity of vibration by waveform interference were applied in practice. Based on the field experiments, we can determine the maximum charge amount per delay and 15. ms delay were proposed to be used in this site, and a decrease in vibration of 24.5% was obtained. © 2011. Source


Shi X.,Central South University | Shi X.,Postdoctoral Workstation of Daye Nonferrous Metals Company | Zhou J.,Central South University
Advanced Science Letters | Year: 2012

Due to the complex features of blasting vibration damage effect assessment systems, a support vector machine (SVM) model for predicting of classification of residential house's damage (RHD) against blasting vibration of open pit mining was established based on the statistical learning theory and the actual characteristics of the project in this study. Ten indexes, i.e., peak particle velocity, dominant frequency, dominant frequency duration, gray joints intensity, the rate of brick walls, height of housing, roof forms, the structural column of circle beam, the quality of construction, site conditions, were defined as the criterion indices for blasting vibration prediction of RHD in the proposed model. In order to determine reasonable and efficient the parameters of SVM, the SVM parameters are optimized by using grid searching method (GSM) and genetic algorithm (GA) respectively, therefore, the support vector machine with GSM/GA (SVMG) model was established. The SVMG model was obtained through training 108 sets of measured data of blasting vibration, the cross-validation method was introduced to verify the stability of SVMG model. Moreover, the proposed model was used to predict 12 new samples from field investigation, the correct rate of prediction results is 100% and are identical with actual situation. From experiment results, it can be concluded that both approaches, can speed up SVM parameter optimization search. The proposed model has a high credibility in the study of RHD against blasting vibration of open pit mining prediction, which can be applied to practical use at mines. © 2012 American Scientific Publishers. All rights reserved. Source


Zhou J.,Central South University | Li X.,Central South University | Shi X.,Central South University | Shi X.,Postdoctoral Workstation of Daye Nonferrous Metals Company
Safety Science | Year: 2012

Rockburst possibility prediction is an important activity in many underground openings design and construction as well as mining production. Due to the complex features of rockburst hazard assessment systems, such as multivariables, strong coupling and strong interference, this study employs support vector machines (SVMs) for the determination of classification of long-term rockburst for underground openings. SVMs is firmly based on the theory of statistical learning algorithms, uses classification technique by introducing radial basis function (RBF) kernel function. The inputs of models are buried depth H, rocks' maximum tangential stress σ θ, rocks' uniaxial compressive strength σ c, rocks' uniaxial tensile strength σ t, stress coefficient σ θ/ σ c, rock brittleness coefficient σ c/ σ t and elastic energy index W et. In order to improve predictive accuracy and generalization ability, the heuristic algorithms of genetic algorithm (GA) and particle swarm optimization algorithm (PSO) are adopted to automatically determine the optimal hyper-parameters for SVMs. The performance of hybrid models (GA + SVMs = GA-SVMs) and (PSO + SVMs = PSO-SVMs) have been compared with the grid search method of support vector machines (GSM-SVMs) model and the experimental values. It also gives variance of predicted data. A rockburst dataset, which consists of 132 samples, was employed to evaluate the current method for predicting rockburst grade, and the good results of overall success rate were obtained. The results indicated that the heuristic algorithms of GA and PSO can speed up SVMs parameter optimization search, the proposed method is robust model and might hold a high potential to become a useful tool in rockburst prediction research. © 2011 Elsevier Ltd. Source


Shi X.-Z.,Central South University | Shi X.-Z.,Postdoctoral Workstation of Daye Nonferrous Metals Company | Zhou J.,Central South University | Du K.,Central South University | And 2 more authors.
Zhendong yu Chongji/Journal of Vibration and Shock | Year: 2010

The control and prediction of blasting vibration hazards is an essential study content in blast engineering. Based on the principles of Bayes discriminating analysis (BDA), the Bayes discriminating analysis model to predict the destructive effect of blast vibration on housing was established here. Ten indexes, i. e., blasting vibration amplitude, dominant frequency, dominant frequency duration, gray joints intensity, the rate of brick walls, height of housing, roof forms, the structural column of circle beam, the quality of construction, and site conditions were used as blast vibration prediction of houses discriminating factors. A BDA model was obtained through training 64 measured data of blasting vibration. The re-substitution method was introduced to verify the stability of the BDA model and the ratio of mis-discrimination was 0.0938. The BDA model was used to discriminate 12 new samples and the prediction results agreed well with actual situation. The results showed that the BDA model has better classifying performance, higher prediction accuracy and lower misdiscrimination rate and can be used widely in practical blast engineering. Source


Guo X.-Y.,Central South University | Li F.,Central South University | Tian Q.-H.,Central South University | Tian Q.-H.,Postdoctoral Workstation of Daye Nonferrous Metals Company | Ji K.,Central South University
Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) | Year: 2012

The process of extracting aluminum from secondary aluminum dross using low-temperature alkaline smelting was studied. The factors, including alkali dross ratio, the addition of different addictive, salt dross ratio, smelting temperature, smelting time, and different mix methods in smelting, were addressed during the transfer of Al into water soluble salt. The results show that the optimum conditions of the process are determined as follows: alkali dross ratio 1.3, salt dross ratio 0.7 (NaNO 3) or 0.4 (Na 2O 2), smelting temperature 500°C, smelting time 60 min. Wet mixture of the sample can improve the leaching efficiency of aluminum. 87.52% of Al might be leached in water with dry mixing of the sample, while 92.71% of Al may be leached with wet mixing by using NaNO 3 as addictive in smelting process, and 92.76% of Al can be dissolved with Na 2O 2 as additive. Source

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