Assessment of coal pillar stability using principal component analysis and stepwise selection and elimination

Abstract

Prediction of pillar stability is one of the most critical tasks in underground mining industries. This pillar stability analysis requires many input parameters and some of them are difficult to be determined. Various statistical based analysis is presented in literature for assessing pillar stability successfully. In the present work, the data from three mines had been to determine the factor of safety. A total of 63 pillar cases had been collected from the mines. Principal component analysis (PCA) and Stepwise selection and elimination (SSE) models were developed by using multi variate linear regression (MLR) on 45 data sets and subsequently the proposed models were validated on 18 different data sets. The value of coefficient of determination (R2) is 0.86 and 0.84 for PCA and SSE respectively. The root mean square error for PCA and SSE are found to be 0.112 and 0.123 respectively. On validation of the proposed model developed by PCA and SSE, the PCA model provided a better validation results. Hence, PCA is recommended for modelling pillar stability.