Prototype experiments were carried out on the explosion-proof performance of the RC blast wall.The mass of TNT detonated in the experiments is 5 kg and 20 kg respectively.The shock wave overpressure was tested in diff...Prototype experiments were carried out on the explosion-proof performance of the RC blast wall.The mass of TNT detonated in the experiments is 5 kg and 20 kg respectively.The shock wave overpressure was tested in different regions.The above experiments were numerically simulated,and the simulated shock wave overpressure waveforms were compared with that tested and given by CONWEP program.The results show that the numerically simulated waveform is slightly different from the test waveform,but similar to CONWEP waveform.Through dimensional analysis and numerical simulation under different working conditions,the equation for the attenuation rate of the diffraction overpressure behind the blast wall was obtained.According to the corresponding standards,the degree of casualties and the damage degree of the brick concrete building at a certain distance behind the wall can be determined when parameters are set.The above results can provide a reference for the design and construction of the reinforced concrete blast wall.展开更多
In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hy...In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hybrid predictive model named GWO-RF.This model combines the grey wolf optimization(GWO)algorithm with the random forest(RF)technique to predict the D_(80)value,a critical parameter in evaluating rock fragmentation quality.The study is conducted using a dataset from Sarcheshmeh Copper Mine,employing six different swarm sizes for the GWO-RF hybrid model construction.The GWO-RF model’s hyperparameters are systematically optimized within established bounds,and its performance is rigorously evaluated using multiple evaluation metrics.The results show that the GWO-RF hybrid model has higher predictive skills,exceeding traditional models in terms of accuracy.Furthermore,the interpretability of the GWO-RF model is enhanced through the utilization of SHapley Additive exPlanations(SHAP)values.The insights gained from this research contribute to optimizing blasting operations and rock fragmentation outcomes in the mining industry.展开更多
基金funded by Key R&D Projects in Hubei Province (Grant No.2020BCA084)Innovative Group Project of Hubei Natural Science Foundation (Grant No.2020CFA043)。
文摘Prototype experiments were carried out on the explosion-proof performance of the RC blast wall.The mass of TNT detonated in the experiments is 5 kg and 20 kg respectively.The shock wave overpressure was tested in different regions.The above experiments were numerically simulated,and the simulated shock wave overpressure waveforms were compared with that tested and given by CONWEP program.The results show that the numerically simulated waveform is slightly different from the test waveform,but similar to CONWEP waveform.Through dimensional analysis and numerical simulation under different working conditions,the equation for the attenuation rate of the diffraction overpressure behind the blast wall was obtained.According to the corresponding standards,the degree of casualties and the damage degree of the brick concrete building at a certain distance behind the wall can be determined when parameters are set.The above results can provide a reference for the design and construction of the reinforced concrete blast wall.
基金Projects(42177164,52474121)supported by the National Science Foundation of ChinaProject(PBSKL2023A12)supported by the State Key Laboratory of Precision Blasting and Hubei Key Laboratory of Blasting Engineering,China。
文摘In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hybrid predictive model named GWO-RF.This model combines the grey wolf optimization(GWO)algorithm with the random forest(RF)technique to predict the D_(80)value,a critical parameter in evaluating rock fragmentation quality.The study is conducted using a dataset from Sarcheshmeh Copper Mine,employing six different swarm sizes for the GWO-RF hybrid model construction.The GWO-RF model’s hyperparameters are systematically optimized within established bounds,and its performance is rigorously evaluated using multiple evaluation metrics.The results show that the GWO-RF hybrid model has higher predictive skills,exceeding traditional models in terms of accuracy.Furthermore,the interpretability of the GWO-RF model is enhanced through the utilization of SHapley Additive exPlanations(SHAP)values.The insights gained from this research contribute to optimizing blasting operations and rock fragmentation outcomes in the mining industry.
