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In situ investigations and detailed laboratory tests indicated that the granite at the Laxiwa hydraulic station is a typical hard rock, with high compressive strength and elasto-brittle failure modes, such as spalling and slabbing, and that the underground caverns are prone to brittle failure. Thus, an intelligent optimization method for cavern excavation was developed to improve the underground engineering’s stability during its construction. This artificial intelligence method utilized the advantages of both the particle swarm optimization algorithm, which is capable of global optimization, and the support vector machine algorithm, which is capable of highly nonlinear mapping. The corresponding numerical analysis indicated that this optimization of excavation sequencing can considerably reduce both the total volume of the damage zone and the brittle failure of the surrounding rock. Furthermore, the measured deformations, the depth of the tested excavation damage zone, and the exposed in situ failures resulting from the applied excavation scheme were similar to the results predicted by the numerical simulation of the cavern excavation.

This paper develops a joint deformation tensor (JD), which considers all of the joint's mechanical and geometrical parameters that affect the deformability of the rock mass. The method based on JD (JD method) and an elastic deformation anisotropy index (EDAI) are deduced for estimating the spatial anisotropy deformation of a jointed rock mass. The numerical modeling and in situ true triaxial compressive experiments well verified the effectiveness of the EDAI and JD method for the rock mass containing one joint set, orthogonal joint sets or the rock mass containing any types of joint network with unity stiffness ratio.