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Abbas Taheri
Researcher at University of Adelaide
Publications - 128
Citations - 3478
Abbas Taheri is an academic researcher from University of Adelaide. The author has contributed to research in topics: Rock mass classification & Compressive strength. The author has an hindex of 28, co-authored 116 publications receiving 2144 citations. Previous affiliations of Abbas Taheri include Cooperative Research Centre & Queen's University.
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Pre-Peak and Post-Peak Rock Strain Characteristics During Uniaxial Compression by 3D Digital Image Correlation
TL;DR: In this article, a non-contact optical method for strain measurement applying three-dimensional digital image correlation (3D DIC) in uniaxial compression is presented. But this method is limited to the case of sandstone.
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Rockburst characteristics and numerical simulation based on a strain energy density index: A case study of a roadway in Linglong gold mine, China
TL;DR: In this paper, a numerical investigation associated with an energy index, strain energy density (SED), is conducted to simulate the energy accumulation and dissipation characteristics of the failure process.
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Fracture Energy-Based Brittleness Index Development and Brittleness Quantification by Pre-peak Strength Parameters in Rock Uniaxial Compression
TL;DR: In this paper, new brittleness indices were developed based on fracture strain energy quantities obtained from the complete stress-strain characteristics of rocks, which were evaluated in a series of quasi-static uniaxial compression tests after properly implementing lateral strain control in a closed-loop system.
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Rock Drilling Performance Evaluation by an Energy Dissipation Based Rock Brittleness Index
TL;DR: In this article, the performance of a single polycrystalline diamond compact (PDC) cutter and different drilling methods including PDC rotary drilling, roller-cone rotary and percussive drilling were investigated.
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Long-term prediction of rockburst hazard in deep underground openings using three robust data mining techniques
TL;DR: This study focuses on the applicability of three novel data mining techniques including emotional neural network (ENN), gene expression programming (GEP), and decision tree-based C4.5 algorithm along with five conventional criteria to predict the occurrence of rockburst in a binary condition.