Prediction research for blasting peak particle velocity based on random GA-BP network group

Guo, Qinpeng; Shi-jiao, Yang; Wang, Yuchen; Xiang, Zhibin

doi:10.1007/s12517-022-10615-3

Cited by 2 publications

(2 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GA belongs to the metaheuristic algorithm [33]. Using the algorithm function of GA (e.g., selection, crossover, and mutation) to optimize BP is considered adequate [52].…”

Section: Parameter Configuration Of the Models Used For Comparisonmentioning

confidence: 99%

“…The one with the smallest RMSE was taken to participate in the comparison. The research experience in [52] was introduced to show that the BP and GA-BP methods were applied in the prediction of PPV. The accuracy of the training model was set to 0.0001; the learning rate was 0.1; the maximum number of iterations was 1000.…”

Section: Parameter Configuration Of the Models Used For Comparisonmentioning

confidence: 99%

See 1 more Smart Citation

Prediction and Optimization of Blasting-Induced Ground Vibration in Open-Pit Mines Using Intelligent Algorithms

Guo

Zhao

Ping-feng³

2023

Applied Sciences

View full text Add to dashboard Cite

Prediction and parameter optimization are effective methods for mine personnel to control blast-induced ground vibration. However, the challenge of effective prediction and optimization lies in the multi-factor and multi-effect nature of open-pit blasting. This study proposes a hybrid intelligent model to predict ground vibrations using a least-squares support vector machine (LSSVM) optimized by a particle swarm algorithm (PSO). Meanwhile, multi-objective particle swarm optimization (MOPSO) was used to optimize the blast design parameters by considering the vibration of particular areas and the bulk rate of blast fragmentation. To compare the prediction performance of PSO-LSSVM, a genetic-algorithm-optimized BP neural network (GA-BP), unoptimized LSSVM, and BP were used, by applying the same database. In addition, the root-mean-squared error (RMSE), the mean absolute error (MAE), and the correlation coefficient (r) were regarded as the evaluation indicators. Furthermore, the optimization results of the blasting parameters were obtained by quoting the established vibration prediction model and bulk rate proxy model in MOPSO and verified by field tests. The results indicated that the PSO-LSSVM model provided the highest efficiency in predicting vibrations with an RMSE of 1.954, MAE of 1.717, and r of 0.965. Furthermore, the blasting vibration can be controlled by using the two-objective optimization model to obtain the best blasting parameters. Consequently, this study can provide more specific recommendations for vibration hazard control.

show abstract

“…GA belongs to the metaheuristic algorithm [33]. Using the algorithm function of GA (e.g., selection, crossover, and mutation) to optimize BP is considered adequate [52].…”

Section: Parameter Configuration Of the Models Used For Comparisonmentioning

confidence: 99%

Section: Parameter Configuration Of the Models Used For Comparisonmentioning

confidence: 99%

Prediction and Optimization of Blasting-Induced Ground Vibration in Open-Pit Mines Using Intelligent Algorithms

Guo

Zhao

Ping-feng³

2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

A comprehensive survey on machine learning applications for drilling and blasting in surface mining

Munagala,

Thudumu,

Logothetis

et al. 2024

Machine Learning with Applications

View full text Add to dashboard Cite

Prediction research for blasting peak particle velocity based on random GA-BP network group

Cited by 2 publications

References 58 publications

Prediction and Optimization of Blasting-Induced Ground Vibration in Open-Pit Mines Using Intelligent Algorithms

Prediction and Optimization of Blasting-Induced Ground Vibration in Open-Pit Mines Using Intelligent Algorithms

A comprehensive survey on machine learning applications for drilling and blasting in surface mining

Contact Info

Product

Resources

About