There are several underground mines in India which operate in close proximity to an operating surface mine.Under such scenario,the blast induced stress waves generated due to surface blasting may be a potential source...There are several underground mines in India which operate in close proximity to an operating surface mine.Under such scenario,the blast induced stress waves generated due to surface blasting may be a potential source to cause instability of adjoining underground mine structures.Using seismographs,54 blast induced vibration data were recorded at various locations in the roof,floor and pillars of the underground mine at Hingir Rampur mine of Coal India Limited by synchronizing the timing of surface blasting carried at an adjacent Samleshwari opencast mine.Results of this study show that Artificial Neural Network(ANN)has better prediction potential of peak particle velocity(PPV)and damage to adjacent underground structures due to surface blasting as compared to conventional regression methods.In order to assess and predict the impact of surface blasts on underground workings,Blast Damage Factor(BDF)has been evolved.The study shows that site specific charts can predict the blast damage class at an underground location due to surface blasting for known distances and explosive charge per delay.The severe damage in case study mine site took place when peak particle velocity exceeded 162 mm/s and PPV less than 51 mm/s had no probability of damage to underground structures due to surface blasting.展开更多
文摘There are several underground mines in India which operate in close proximity to an operating surface mine.Under such scenario,the blast induced stress waves generated due to surface blasting may be a potential source to cause instability of adjoining underground mine structures.Using seismographs,54 blast induced vibration data were recorded at various locations in the roof,floor and pillars of the underground mine at Hingir Rampur mine of Coal India Limited by synchronizing the timing of surface blasting carried at an adjacent Samleshwari opencast mine.Results of this study show that Artificial Neural Network(ANN)has better prediction potential of peak particle velocity(PPV)and damage to adjacent underground structures due to surface blasting as compared to conventional regression methods.In order to assess and predict the impact of surface blasts on underground workings,Blast Damage Factor(BDF)has been evolved.The study shows that site specific charts can predict the blast damage class at an underground location due to surface blasting for known distances and explosive charge per delay.The severe damage in case study mine site took place when peak particle velocity exceeded 162 mm/s and PPV less than 51 mm/s had no probability of damage to underground structures due to surface blasting.
