Stability analysis of underground mine hard rock pillars via combination of finite difference methods, neural networks, and Monte Carlo simulation techniques

• Main Authors: Chuanqi Li, Jian Zhou, Danial Jahed Armaghani, Xibing Li
• Format: Article
• Language: English
• Published: Elsevier 2021-08-01
• Series: Underground Space
• Subjects: Hard rock pillar; Numerical simulation; Neural networks; Safety factor; Geological strength index; Monte Carlo simulation
• Online Access: http://www.sciencedirect.com/science/article/pii/S2467967420300441

Pillar stability is always evaluated using the safety factor (SF), which is defined as the ratio of pillar strength to pillar stress. However, most researchers have estimated pillar stress using the pillar shape ratio (w/h), uniaxial compressive strength (UCS) of the intact rock mass, and pillar depth (H). In this study, the geological strength index (GSI) of hard rock pillars was considered as a new variable for predictive purposes. This index was developed by combining numerical simulation software (i.e., FLAC3D) and a backpropagation neural network (BPNN). A hard rock pillar stability analysis, based on three methods including deterministic method, sensitivity analysis, and Monte Carlo simulation (MCS), was performed. A new formula was proposed to estimate the SF values based on the predicted stress, considering the GSI variable in the deterministic method. The sensitivity analysis indicated that the variables impacting the SF from high to low are UCS, GSI, w/h, and H. In this study, pillar stability was analyzed mainly using the GSI and MCS techniques. The MCS results revealed that the GSI is also a major factor in pillar stability and has a greater effect on weak pillars than on strong ones. Furthermore, a pillar is more likely to be unstable when both the GSI and the UCS are decreased. This study provides several references and procedures for improving the design of stable pillars considering the GSI as an important factor.