A New Classification Method of Surrounding Rock Quality for Phyllite Tunnels under the Condition of Layer Orientation Parallel to the orientation of tunnel Axis

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Abstract

Surrounding rock classification is a critical factor in evaluating tunnel stability, determining construction methods, and selecting support parameters. Various engineering sectors utilize different methods for grading tunnel surrounding rock quality. The HC method iswidely adopted in the hydropower industry for this purpose. However, due to the anisotropy of layered phyllite, the classification results obtained using the HC method for phyllite tunnels—when the layer orientation is parallel to the tunnel axis-differ significantly from those based on actual field investigations. This study conducted uniaxial compression tests on rocks, revealing that layered phyllite exhibits notable anisotropy at bedding angles of 0°, 45°, and 90°. The compressive strength follows a V-shaped trend as the angle between the bedding and loading orientations changes, while the deformation modulus decreases linearly with increasing angular deviation between the loading orientation and tunnel axis. Numerical simulations were performed to observe tunnel deformation at various bedding-to-tunnel axis angles. Results showed that, as the bedding angle decreases, deformation of the tunnel wall and crown increases progressively. At angles of 0°, 30°, 45°, 60°, and 90°, the deformation ratios for the tunnel wall were 1:3.7:3:4.74:5.44:7.7, and for the tunnel crown, the ratios were 1:1.3:1.94:4.7:6.7. When the traditional HC method was used to classify the surrounding rock in tunnels with parallel phyllite layers, the agreement rate was only 13.33%, indicating low accuracy. By modifying the occurrence score for major structural joints and incorporating the weight ratios derived from numerical simulations, the HC method’s accuracy improved, achieving an agreement rate of 100%. This study enhances the precision and applicability of surrounding rock classification and offers valuable insights for tunnel construction.

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