Non-elastic instantaneous response in rock rheology

Installing flexible layer is one kind of supporting techniques to deal with the large deformation in tunnels excavated in viscoelastic rocks. The role of flexible layer is to absorb rock deformation due to rock rheology. For further understanding the effect of flexible layer on mechanical behavior of tunnels, a three-layered model is established to study the mechanical behavior of tunnel where flexible layer is installed between surrounding rock and primary support. Visco-elastic analytical solutions for displacements and interaction forces in the rock/flexible layer interface and in the flexible layer/primary support interface are provided. Numerical calculation by use of finite element software Abaqus is carried out to verify the effectiveness and reliability of theoretical analysis. It could be found that flexible layer has a good ability to absorb rock deformation. Compared with rigid support structure, pressure and displacement of primary support in tunnels employing flexible layer could achieve a good improvement. This improvement is dramatically affected by the thickness and deformability of reserved flexible layer.

Download Full-text

Study on the Levenberg-Marquardt Neural Network Model for Rock Rheology

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.71-78.4103 ◽

2011 ◽

Vol 71-78 ◽

pp. 4103-4108

Author(s):

Yu Zhou Jiang ◽

Rui Hong Wang ◽

Jie Bing Zhu

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Testing Machine ◽

Experimental Result ◽

Left Bank ◽

Marquardt Algorithm ◽

Levenberg Marquardt ◽

Rheological Experiments ◽

Rock Rheology

Rheological experiments were carried out for sandstone and marble specimens from left bank high slope of Jingping First Stage Hydropower Project by using the rock servo-controlling rheology testing machine. Typical triaxial rheological curves under step loading and temperature curves in the process of rheological experiment were gained. BP neural network is improved by Levenberg-Marquardt algorithm. Improved neural network model for rock rheology is established in accordance with the rheology experimental results of rock specimen. The improved neural network model was used to forecast rock rheological experimental curves, and the result shows that the forecasted rock rheology curves are closely accorded with the experimental result. The improved neural network model takes into account the influence of loading history and temperature difference on the rock rheological deformation, and the forecasted result can reflect better the rheology deformation behavior of rock material.

Download Full-text