DOI: 10.11799/ce202508017
TBM boreability prediction and rock mass classification method based on fused intelligent optimization algorithms
SHENG Yuanwei
LIU Hongli
ZHU Hao
ZHANG Chaofan
Abstract：Highly variable and complex geological conditions often lead to severe tunnel boring machine(TBM)load fluctuations,accelerated cutter wear,and reduced excavation efficiency.In order to establish an accurate boreability prediction and rock mass classification method to provide a scientific basis for TBM parameter optimization and construction planning.We propose an intelligent prediction model-GWO-VMD-SSA-LSTM-by integrating Grey Wolf Optimization(GWO),Variational Mode Decomposition(VMD),Sparrow Search Algorithm(SSA),and Long Short-Term Memory(LSTM)networks.The model enables accurate prediction of TBM boreability and quantitative classification of surrounding rock.Results show that the model achieves excellent performance on the test set,with a MAE of 0.4324,RMSE of 0.6005,MAPE of 1.5486%,and R2 of 0.9527,significantly outperforming other comparison models in terms of accuracy and generalization ability.Furthermore,a rock mass classification system based on the Field Penetration Index(FPI)is developed,enabling rapid determination of boreability levels.Engineering validation demonstrates that the method effectively reflects excavation difficulty across various lithologies and provides intelligent decision-making support for TBM tunneling in complex geological conditions.
Keywords：TBMboreability predictionsurrounding rock classificationroadway excavationintelligent optimization algorithmdeep learning
Publication Date：2025-08-20
Online Publishing Date：2025-09-10(First online date of this platform, not the publication date of the document)
Pages：8( 122-129 )