Prediction of the post‐failure behavior of rocks: Combining artificial intelligence and acoustic emission sensing

Abstract

Acoustic emission (AE) reading is among the most common methods for monitoring the behavior of brittle materials such as rock and concrete. This study uses discrete element method (DEM) simulations to explore the correlations between the pre-failure AE readings with the post-failure behavior and residual strength of rock masses. The deep learning (DL) method based on long short-term memory (LSTM) algorithms has been applied to generate predictive models based on the data from DEM simulations of biaxial compression. The dataset has been populated by varying interparticle friction while keeping bond cohesion constant. Various configurations of the LSTM algorithm were evaluated considering different scenarios for input features (strain, stress, and AE energy records) and a range of values for the key hyperparameters. The prime AI models show promising accuracy in predicting residual strength decay with strain based on pre-failure patterns in AE readings. The results indicate that the pre-failure AE indeed encapsulates information about the developing failure mechanisms and the post-failure response in rocks, which can be captured through artificial intelligence.

Study on Acoustic Emission Characteristics and Damage Evolution Law of Shale under Uniaxial Compression

“…AE technique is a developed nondestructive testing method, which has proved to be a reliable tool for many types of studies [9][10]. Figure 1 shows some characteristic parameters of a simplified AE signal, including rise time, duration time, AE count, and maximum amplitude, and these AE parameters can be used to evaluate the damage severity and identify the nature of damage directly or indirectly [11].…”

“…Recently, different artificial intelligence (AI) methods have been applied to various aspects of rock mechanics and civil engineering, owing to its ability to handle complex problems [28,29]. A study showed that the prefailure AE indeed encapsulates information about the developing failure mechanisms and the postfailure response in rocks, which can be captured through AI [10].…”