摘要
This research examines optimization of blasting parameters for economic production of granite aggregates in Ratcon and NSCE quarries located atIbadan,OyoState. Samples were collected from the study areas for the determination of rock density and porosity. Schmidt hammer was used for in situ determination of rock hardness. Uniaxial compressive strength of in situ rock was estimated from the values obtained from Schmidt hammer rebound hardness test and density determined from laboratory test. Blasting data were collected from the study areas for optimization. Multiple regression analysis using computer aided solution SPSS (Statistical Package for the Social Sciences) was used to analyse data obtained from the laboratory test, field test and the study areas. The estimated mean uniaxial compressive strength value of NSCE is 240 MPa and that of Ratcon is 200 MPa and their average densities and average porosities are2.63g/cm3,2.55g/cm3, 1.88% and 2.25% respectively. Eleven parameters were input into the multiple regression analysis to generate the models. Two parameters out of eleven input parameters such as geometric volume of blast (Y1) and number of boulders generated after blasting (Y2) were dependent variables and the remaining nine such as X1 (Drill hole diameter), X2 (Drill hole depth), X3 (Spacing), X4 (Burden), X5 (Average charge per hole), X6 (Rock density), X7 (Porosity), X8 (Uniaxial compressive strength) and X9 (Specific charge) were input as independent variables. The results of the models show that out of the nine independent variables seven of them that is X1 (Borehole diameter), X2 (Borehole depth), X3 (Spacing), X4 (Burden), X5 (Average charge per hole), X8 (Uniaxial compressive strength) and X9 (Specific charge) have significant contribution to the models while X6 (Rock Density) and X7 (Porosity) have insignificant contribution they are therefore automatically deleted by the SPSS. The result of the models developed for the optimization reveals that blasting number 5 gives the required product at lowe
This research examines optimization of blasting parameters for economic production of granite aggregates in Ratcon and NSCE quarries located atIbadan,OyoState. Samples were collected from the study areas for the determination of rock density and porosity. Schmidt hammer was used for in situ determination of rock hardness. Uniaxial compressive strength of in situ rock was estimated from the values obtained from Schmidt hammer rebound hardness test and density determined from laboratory test. Blasting data were collected from the study areas for optimization. Multiple regression analysis using computer aided solution SPSS (Statistical Package for the Social Sciences) was used to analyse data obtained from the laboratory test, field test and the study areas. The estimated mean uniaxial compressive strength value of NSCE is 240 MPa and that of Ratcon is 200 MPa and their average densities and average porosities are2.63g/cm3,2.55g/cm3, 1.88% and 2.25% respectively. Eleven parameters were input into the multiple regression analysis to generate the models. Two parameters out of eleven input parameters such as geometric volume of blast (Y1) and number of boulders generated after blasting (Y2) were dependent variables and the remaining nine such as X1 (Drill hole diameter), X2 (Drill hole depth), X3 (Spacing), X4 (Burden), X5 (Average charge per hole), X6 (Rock density), X7 (Porosity), X8 (Uniaxial compressive strength) and X9 (Specific charge) were input as independent variables. The results of the models show that out of the nine independent variables seven of them that is X1 (Borehole diameter), X2 (Borehole depth), X3 (Spacing), X4 (Burden), X5 (Average charge per hole), X8 (Uniaxial compressive strength) and X9 (Specific charge) have significant contribution to the models while X6 (Rock Density) and X7 (Porosity) have insignificant contribution they are therefore automatically deleted by the SPSS. The result of the models developed for the optimization reveals that blasting number 5 gives the required product at lowe
