The law of blasting vibration caused by blasting in rock is very complex.Traditional numerical methods cannot well characterize all the influencing factors in the blasting process.The effects of millisecond time,charg...The law of blasting vibration caused by blasting in rock is very complex.Traditional numerical methods cannot well characterize all the influencing factors in the blasting process.The effects of millisecond time,charge length and detonation velocity on the blasting vibration are discussed by analyzing the characteristics of vibration wave generated by finite length cylindrical charge.It is found that in multi-hole millisecond blasting,blasting vibration superimpositions will occur several times within a certain distance from the explosion source due to the propagation velocity difference of P-wave and S-wave generated by a short column charge.These superimpositions will locally enlarge the peak velocity of blasting vibration particle.The magnitude and scope of the enlargement are closely related to the millisecond time.Meanwhile,the particle vibration displacement characteristics of rock under long cylindrical charge is analyzed.The results show that blasting vibration effect would no longer increase when the charge length increases to a certain extent.This indicates that the traditional simple calculation method using the maximum charge weight per delay interval to predict the effect of blasting vibration is unreasonable.Besides,the effect of detonation velocity on blasting vibration is only limited in a certain velocity range.When detonation velocity is greater than a certain value,the detonation velocity almost makes no impact on blasting vibration.展开更多
Blast-induced ground vibration,quantified by peak particle velocity(PPV),is a crucial factor in mitigating environmental and structural risks in mining and geotechnical engineering.Accurate PPV prediction facilitates ...Blast-induced ground vibration,quantified by peak particle velocity(PPV),is a crucial factor in mitigating environmental and structural risks in mining and geotechnical engineering.Accurate PPV prediction facilitates safer and more sustainable blasting operations by minimizing adverse impacts and ensuring regulatory compliance.This study presents an advanced predictive framework integrating Cat Boost(CB)with nature-inspired optimization algorithms,including the Bat Algorithm(BAT),Sparrow Search Algorithm(SSA),Butterfly Optimization Algorithm(BOA),and Grasshopper Optimization Algorithm(GOA).A comprehensive dataset from the Sarcheshmeh Copper Mine in Iran was utilized to develop and evaluate these models using key performance metrics such as the Index of Agreement(IoA),Nash-Sutcliffe Efficiency(NSE),and the coefficient of determination(R^(2)).The hybrid CB-BOA model outperformed other approaches,achieving the highest accuracy(R^(2)=0.989)and the lowest prediction errors.SHAP analysis identified Distance(Di)as the most influential variable affecting PPV,while uncertainty analysis confirmed CB-BOA as the most reliable model,featuring the narrowest prediction interval.These findings highlight the effectiveness of hybrid machine learning models in refining PPV predictions,contributing to improved blast design strategies,enhanced structural safety,and reduced environmental impacts in mining and geotechnical engineering.展开更多
基金Project(50878123)supported by the National Natural Science Foundation of ChinaProject(20113718110002)supported by the Specialized Research Fund for the Doctoral Program of Higher Education of China+1 种基金Project(DPMEIKF201307)supported by the Fund of the State key Laboratory of Disaster Prevention&Mitigation of Explosion&Impact(PLA University and Technology),ChinaProject(13BS402)supported by Huaqiao University Research Foundation,China
文摘The law of blasting vibration caused by blasting in rock is very complex.Traditional numerical methods cannot well characterize all the influencing factors in the blasting process.The effects of millisecond time,charge length and detonation velocity on the blasting vibration are discussed by analyzing the characteristics of vibration wave generated by finite length cylindrical charge.It is found that in multi-hole millisecond blasting,blasting vibration superimpositions will occur several times within a certain distance from the explosion source due to the propagation velocity difference of P-wave and S-wave generated by a short column charge.These superimpositions will locally enlarge the peak velocity of blasting vibration particle.The magnitude and scope of the enlargement are closely related to the millisecond time.Meanwhile,the particle vibration displacement characteristics of rock under long cylindrical charge is analyzed.The results show that blasting vibration effect would no longer increase when the charge length increases to a certain extent.This indicates that the traditional simple calculation method using the maximum charge weight per delay interval to predict the effect of blasting vibration is unreasonable.Besides,the effect of detonation velocity on blasting vibration is only limited in a certain velocity range.When detonation velocity is greater than a certain value,the detonation velocity almost makes no impact on blasting vibration.
基金the Deanship of Scientific Research at Northern Border University,Arar,KSA for funding this research work through the project number"NBUFFMRA-2025-2461-09"。
文摘Blast-induced ground vibration,quantified by peak particle velocity(PPV),is a crucial factor in mitigating environmental and structural risks in mining and geotechnical engineering.Accurate PPV prediction facilitates safer and more sustainable blasting operations by minimizing adverse impacts and ensuring regulatory compliance.This study presents an advanced predictive framework integrating Cat Boost(CB)with nature-inspired optimization algorithms,including the Bat Algorithm(BAT),Sparrow Search Algorithm(SSA),Butterfly Optimization Algorithm(BOA),and Grasshopper Optimization Algorithm(GOA).A comprehensive dataset from the Sarcheshmeh Copper Mine in Iran was utilized to develop and evaluate these models using key performance metrics such as the Index of Agreement(IoA),Nash-Sutcliffe Efficiency(NSE),and the coefficient of determination(R^(2)).The hybrid CB-BOA model outperformed other approaches,achieving the highest accuracy(R^(2)=0.989)and the lowest prediction errors.SHAP analysis identified Distance(Di)as the most influential variable affecting PPV,while uncertainty analysis confirmed CB-BOA as the most reliable model,featuring the narrowest prediction interval.These findings highlight the effectiveness of hybrid machine learning models in refining PPV predictions,contributing to improved blast design strategies,enhanced structural safety,and reduced environmental impacts in mining and geotechnical engineering.
