Measurement | Vol.96, Issue.0 | | Pages 34-51
Establishment of blasting design parameters influencing mean fragment size using state-of-art statistical tools and techniques
In the present study, principal component analysis (PCA) and stepwise selection and elimination (SSE) techniques were used to establish significant parameters (blasting design, rock and explosive) in surface coal mines by reducing dimensionality and variables from a host of blasting parameters. Mean fragment size (MFS) prediction models were subsequently developed using multiple linear regression (MLR) analysis technique. The two constructed and proposed models adequately selected relevant blast design, explosive and rock mass parameters. The performances of these models were assessed through the determination coefficient (R2), F-ratio, standard error of estimate and root mean square error (RMSE). The PCA technique has shown good promise in eliminating the redundant parameters and in selecting relevant blast design parameters. Hierarchical cluster analysis technique was used for confirming the similarity of blasting design parameters in two trial blasting data set. The results were tested and validated with the 19 actual blast data set at acceptable correlation levels and have been illustrated in the form of figures, tables and graphs. MFS prediction equations based on PCA and SSE techniques were simple and suitable for practical use in overburden bench blasting of Indian coal mines.
Original Text (This is the original text for your reference.)
Establishment of blasting design parameters influencing mean fragment size using state-of-art statistical tools and techniques
In the present study, principal component analysis (PCA) and stepwise selection and elimination (SSE) techniques were used to establish significant parameters (blasting design, rock and explosive) in surface coal mines by reducing dimensionality and variables from a host of blasting parameters. Mean fragment size (MFS) prediction models were subsequently developed using multiple linear regression (MLR) analysis technique. The two constructed and proposed models adequately selected relevant blast design, explosive and rock mass parameters. The performances of these models were assessed through the determination coefficient (R2), F-ratio, standard error of estimate and root mean square error (RMSE). The PCA technique has shown good promise in eliminating the redundant parameters and in selecting relevant blast design parameters. Hierarchical cluster analysis technique was used for confirming the similarity of blasting design parameters in two trial blasting data set. The results were tested and validated with the 19 actual blast data set at acceptable correlation levels and have been illustrated in the form of figures, tables and graphs. MFS prediction equations based on PCA and SSE techniques were simple and suitable for practical use in overburden bench blasting of Indian coal mines.
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stepwise selection and elimination sse techniques surface coal hierarchical cluster analysis technique principal component analysis pca blast design explosive blasting parameters mean fragment size pca technique rock mass root mean square error dimensionality error of estimate determination coefficient multiple linear regression mlr analysis prediction trial blasting data overburden bench blasting of indian coal
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