Estimating Time-to-Failure and Long-Term Strength of Rocks Based on Creep Strain Rate Model

Abstract

AbstractSustainable mining development requires structures on or within rock masses that can withstand deformation over a long period without compromising safety. Understanding of time-dependent behaviour of rocks is essential for such a purpose which is commonly investigated under sustained loading or so-called “creep” condition within the laboratory environment. A large number of experimental and analytical studies have examined creep behaviour of different rock types. However, some questions have still remained unanswered, particularly regarding the estimation of long-term strength of rocks and predicting their time-to-failure. This study proposes a novel method for prediction of time-to-failure of rock materials under creep loading governed by the secondary creep strain rate as well as estimation of their long-term strength through laboratory data. To do so, six different stress magnitudes ranging from 0.4 to 0.95 of the uniaxial compressive strength were selected for conventional creep compressive tests on Gosford sandstone. Throughout each experiment, the stress magnitude was kept constant until the sample reached failure. The results demonstrated that the secondary creep strain rate is strongly dependent on the magnitude of applied stress. A mere 10% reduction in the applied stress resulted in a decrease in the secondary creep strain rate of approximately three orders of magnitude. The proposed approach for time-to-failure prediction under creep loading included utilisation of secondary creep strain rates as a set of predictive indicators to overcome inherent variability or heterogeneity in rocks. Finally, the validation study was conducted based on the creep data obtained from various rock types to highlight consistent linear correlation between the secondary creep strain rate and the time-to-failure regardless of the magnitude of applied stress. Such an innovative approach can be a suitable tool for practitioners to better predict the stability of rock structures subjected to long-term loading leading to sustainable mining operation.