• Tunnel and Underground Space
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• v.31 no.4
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• pp.270-288
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• 2021

We proposed a numerical method to simulate the hydro-mechanical behavior of rock fracture using a grain-based distinct element model (GBDEM) in the paper. As a part of DECOVALEX-2023 Task G, we verified the method via benchmarks with analytical solutions. DECOVALEX-2023 Task G aims to develop a numerical method to estimate the coupled thermo-hydro-mechanical processes within the crystalline rock fracture network. We represented the rock sample as a group of tetrahedral grains and calculated the interaction of the grains and their interfaces using 3DEC. The micro-parameters of the grains and interfaces were determined by a new methodology based on an equivalent continuum approach. In benchmark modeling, a single fracture embedded in the rock was examined for the effects of fracture inclination and roughness, the boundary stress condition and the applied pressure. The simulation results showed that the developed numerical model reasonably reproduced the fracture slip induced by boundary stress condition, the fracture opening induced by fluid injection, the stress distribution variation with fracture inclination, and the fracture roughness effect. In addition, the fracture displacements associated with the opening and slip showed good agreement with the analytical solutions. We expect the numerical model to be enhanced by continuing collaboration and interaction with other research teams of DECOVALEX-2023 Task G and validated in further study experiments.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.1
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• pp.39-58
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• 2024

Tunnel Boring Machines (TBM) use multiple disc cutters to excavate tunnels through rock. These cutters wear out due to continuous contact and friction with the rock, leading to decreased cutting efficiency and reduced excavation performance. The rock's abrasivity significantly affects cutter wear, with highly abrasive rocks causing more wear and reducing the cutter's lifespan. The Cerchar Abrasivity Index (CAI) is a key indicator for assessing rock abrasivity, essential for predicting disc cutter life and performance. This study aims to develop a new method for effectively estimating CAI using rock strength, petrological characteristics, linear regression, and machine learning. A database including CAI, uniaxial compressive strength, Brazilian tensile strength, and equivalent quartz content was created, with additional derived variables. Variables for multiple linear regression were selected considering statistical significance and multicollinearity, while machine learning model inputs were chosen based on variable importance. Among the machine learning prediction models, the Gradient Boosting model showed the highest predictive performance. Finally, the predictive performance of the multiple linear regression analysis and the Gradient Boosting model derived in this study were compared with the CAI prediction models of previous studies to validate the results of this research.