Prediction of an early failure point using infrared radiation characteristics and energy evolution for sandstone with different water contents

Water is one of the most effective agents that weaken the physio-mechanical properties of rock, trigger significant construction delays, endanger the construction operation, and lead to rock failure. Therefore, an early failure point (EFP) prediction of rock under such conditions is imperative for the robust and reliable implementation of underground engineering. In this research, an EFP of sandstone with different water contents was predicted based on infrared radiation and complex energy evolution during loading. The ratios of elastic to dissipation energy (K-ED) and elastic to total energy (K-ET) were proposed to predict EFP. The results show that K-ED and K-ET give EFP at the same time for sandstone with water contents 0%, 0.991%, 2.136%, and 3.109%, and the average time of EFP ahead 208 s, 250 s, 265.8 s, and 276.9 s than rock failure, respectively. Furthermore, the proposed K-ED and K-ET were predicted using an artificial neural network (ANN). The ANN models' efficacy was evaluated using the performance coefficient (R-2) and root-means-square error (RMSE). The findings revealed high R-2 and low RMSE for K-ED and K-ET of sandstone with different water contents. The research findings can be used effectively to monitor disasters for the safe and efficient execution of engineering projects.