Prediction of longwall mining‐induced stress in roof rock using LSTM neural network and transfer learning method

Real‐time monitoring of three‐dimensional stress in the field is an effective approach to detect evolving stress in roof rock and to evaluate rock bursts risk. However, the sensors or data transmission cables may be damaged due to the volatile environment found in coal mines, which can lead to the loss of relevant monitoring data, and some critical information for rock burst prediction may be missed entirely. A number of methods that use historical data to predict missing data or future structural states have been proposed. However, the performance of these methods is poor when the training data are insufficient owing to lack of data. To address this issue, a methodology framework is proposed to predict the mining‐induced stress when some monitoring data are missing. The framework uses a long short‐term memory neural network integrated with the transfer learning method. The proposed method can transfer the knowledge learned from complete monitored data of adjacent sensor to target sensor to boost forecasting. A case study has been conducted to evaluate the method. The results show that the developed model can significantly improve the prediction performance for the target domain, which can be improved further by increasing the size of the target domain training data available.