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.

中文翻译：

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岩石破坏后行为预测：人工智能与声发射传感相结合

声发射 (AE) 读数是监测岩石和混凝土等脆性材料行为的最常用方法之一。本研究使用离散元法 (DEM) 模拟来探索破坏前 AE 读数与破坏后行为和岩体剩余强度之间的相关性。基于长短期记忆 (LSTM) 算法的深度学习 (DL) 方法已被应用于基于双轴压缩的 DEM 模拟数据生成预测模型。该数据集已通过改变粒子间摩擦来填充，同时保持键内聚力恒定。考虑到输入特征（应变、应力和 AE 能量记录）的不同场景以及关键超参数的一系列值，评估了 LSTM 算法的各种配置。主要的 AI 模型在基于 AE 读数中的故障前模式预测残余强度随应变衰减方面显示出有希望的准确性。结果表明，失效前 AE 确实封装了有关岩石中发展中的失效机制和失效后响应的信息，这些信息可以通过人工智能捕获。