ABSTRACT:In recent years, geological disposal of radioactive wastes is considered to be the most promising option, which requires the understanding of the coupled mechanical, hydraulic and thermal properties of the host rock masses and rock fractures.

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The long-term deformations of mountain tunnels, which attract more and more attentions, are closely related to the time-dependent features of the surrounding rock mass. However, it is not easy to determine an appropriate rheological model and its corresponding parameters for a certain engineering instance. This paper presents a rheological parameter estimation technique by using error backpropagation neural network (BN) and genetic algorithm (GA). The application of the proposed technique to an engineering instance, Ureshino Tunnel Line I on Nagasaki Expressway, is expatiated in detailed. The stochastic nature of the proposed technique is also discussed through case studies. It is proved that the proposed technique can provide the engineer with an optimal estimation of the rheological parameters, which can help the prediction of long-term deformations of mountain tunnels in the future.

Abstract:The time-dependent features of soft rock, named rheology generally, should be taken into account in the long-term design and maintenance of mountain tunnels. Based on the classic Burger-MC rheological model, aBurger-Deterioration rheological model is proposed in this paper and is implemented in the numerical codes FLAC 3D .A deterioration threshold and two deterioration ratios are introduced in this model to consider the time-dependent strength deterioration aspect of the rock mass. The proposed model is applied to an engineering instance (Ureshino Tunnel Line I, Nagasaki, Japan) to account for the delayed deformations that occurred after its completion since Nov.1992. The delayed crown settlement and invert upheaval computed from simulations are featured by an exponential characteristic and a stair-typed characteristic, respectively, which agree well with the in-site monitoring data qualitatively. In addition, the realistic rheological parameters involved in the proposed model can be back-analyzed from the in-site monitoring data.

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