Strain Energy Dissipation Characteristics and Neural Network Model during Uniaxial Cyclic Loading and Unloading of Dry and Saturated Sandstone

Abstract

The energy dissipation characteristics are important features of rock damage and failure during loading. However, the quantitative relationship between energy dissipation and rock failure is not clear. In this work, acoustic emission monitoring tests during uniaxial cyclic loading and unloading were conducted on sandstones in two conditions, namely dry and saturated, to investigate the energy evolution characteristics. Then, an index of the absolute energy ratio and a dynamic adjustment coefficient were put forward to represent rock damage and failure. A recurrent neural network was employed to predict the dynamic adjustment coefficient of dissipative strain energy. The results showed that (1) water saturation promoted the increased rate of dissipative strain energy during the last loading and unloading, but suppressed the sudden drop in elastic strain energy. (2) In the early and middle stages of cyclic loading–unloading, the absolute acoustic emission energy of dry and saturated rock samples was mainly generated by the historical maximum stress, while the absolute acoustic emission energy was mainly generated by cycle loading–unloading in the final cyclic stages. (3) The absolute energy ratio of both dry and saturated rock samples showed a sudden increase at the last cyclic loading–unloading, and this phenomenon can be taken as a precursor of rock damage of cycle loading–unloading. (4) The recurrent neural network for the prediction of the dynamic adjustment coefficient shows good agreement for rock failure related to dissipative strain energy. The results can provide experimental and prediction models for the monitoring and warning of rock engineering disasters in slopes, hydraulic fractures, tunnels, and coal mines.

Keywords

• cyclic unloading and loading
• strain energy
• neural network
• absolute energy
• damage precursor