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Dynamic Intelligent Prediction of Tunnel Surrounding Rock Geological Information Based on M-LSTM Method
(1. State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology,Chengdu 610059; 2. College of Environmental and Civil Engineering, Chengdu University of Technology, Chengdu 610059)
Abstract To improve the accuracy of intelligent prediction for tunnel surrounding rock geological information, quantitative indicators such as rock integrity, rock hardness, water abundance condition, rock weathering degree,and geostress state were used as geological information parameters. By collecting various geological indicator data from the excavated sections of the tunnel and using the K-means clustering algorithm to clean the data, a highly correlated database for geological information indicators of tunnel surrounding rocks was established. Based on the sample database, a dynamic intelligent prediction model for surrounding rock geological information during tunnel construction, based on an improved long short-term memory neural network (M-LSTM), was developed. This model enables dynamic intelligent prediction of the time-series geological information data for unexcavated sections based on intelligent learning from the geological information of the excavated tunnel sections. The results show that the prediction accuracy for rock integrity is 91.6%, rock hardness is 93.8%, water abundance condition is 85.4%, rock weathering degree is 85.4%, and geostress state is 87.5%. Meanwhile, the M-LSTM method demonstrates higher computational efficiency and accuracy compared to the LSTM method and the ordinary neural network (ANN) method.
Abstract：
To improve the accuracy of intelligent prediction for tunnel surrounding rock geological information, quantitative indicators such as rock integrity, rock hardness, water abundance condition, rock weathering degree,and geostress state were used as geological information parameters. By collecting various geological indicator data from the excavated sections of the tunnel and using the K-means clustering algorithm to clean the data, a highly correlated database for geological information indicators of tunnel surrounding rocks was established. Based on the sample database, a dynamic intelligent prediction model for surrounding rock geological information during tunnel construction, based on an improved long short-term memory neural network (M-LSTM), was developed. This model enables dynamic intelligent prediction of the time-series geological information data for unexcavated sections based on intelligent learning from the geological information of the excavated tunnel sections. The results show that the prediction accuracy for rock integrity is 91.6%, rock hardness is 93.8%, water abundance condition is 85.4%, rock weathering degree is 85.4%, and geostress state is 87.5%. Meanwhile, the M-LSTM method demonstrates higher computational efficiency and accuracy compared to the LSTM method and the ordinary neural network (ANN) method.
.Dynamic Intelligent Prediction of Tunnel Surrounding Rock Geological Information Based on M-LSTM Method[J] MODERN TUNNELLING TECHNOLOGY, 2025,V62(1): 74-82
2025, Vol. 62 