| Abstrakt: |
Mass mining methods such as block and panel caving are more commonly being utilized to generate value from deep, low-grade ore deposits. In addition to mining deeper and encountering higher in situ stresses, these projects are also mining larger volumes in more competent rock masses, increasing their exposure to brittle rock failure mechanisms such as spalling and rockbursting. Rockbursting presents a health and safety risk for mine personnel, a financial risk for mine operators, and a significant design challenge for engineers. A methodology is proposed for recording, databasing, and performing data-driven assessments of rockbursting in cave mines with the goal of correctly identifying the damage mechanisms, the triggers, and important controlling factors. Novel databasing techniques and new indices are introduced that are specifically tailored to cave mining operations where the damage from rockbursts may be widespread across a large extraction-level footprint. A case study of the Deep Mill Level Zone (DMLZ) panel cave mine is presented to demonstrate the data-driven analysis techniques. Using the proposed indices and data analysis techniques, strainbursting was identified as the dominant rockburst damage mechanism in the DMLZ, with load redistribution from blasting and advance of the undercut and mining of the cave being identified as the main triggers. Contributing factors for the rockbursting in the DMLZ are discussed including the mining sequence, the mine geometry, and the geology. Evidence for veining heterogeneity contributing to increased susceptibility to strainbursting is presented, a key finding with practical implications for future mass mines. Highlights: Novel databasing techniques, indices, and assessments are introduced to identify the rockburst damage mechanisms, trigger mechanisms, and important controlling factors in a cave mining setting. Two distinct databases are proposed to track local excavation damage and the cumulative impact of widespread rockburst damage that is possible in large-scale mining. New indices are proposed including the Rockburst Damage Index and Rockburst Cluster Index that are practical and useful for data driven assessments of rockbursting. A methodology is presented using multivariate logistic regression and a grid cell system to study spatial susceptibility to rockburst damage in cave mining. The geometry of cave mining lends itself well to spatial modelling using the proposed grid system and the selection of mechanistically meaningful explanatory variables. The approach therefore combines robust procedures for logistic regression with data processing that is suitable for the cave mining environment, leading to highly interpretable results. A case study of the DMLZ is shown to demonstrate the value of the proposed assessments. Key findings from the DMLZ include the identification of strainbursting as the dominant damage mechanism, the important role of veining intensity in strainburst susceptibility, and the positive role of a deformation-based ground support design in suppressing low severity strainbursting. [ABSTRACT FROM AUTHOR] |
Full text from SpringerLink