Forecasting tunnel boring machine penetration rate using LSTM deep neural network optimized by grey wolf optimization algorithm

Abstract

Achieving an accurate and reliable estimation of tunnel boring machine (TBM) performance can diminish the hazards related to extreme capital costs and planning tunnel construction. Here, a hybrid long short-term memory (LSTM) model enhanced by grey wolf optimization (GWO) is developed for predicting TBM-penetration rate (TBM-PR). 1125 datasets were considered including six input parameters. To vanish overfitting, the dropout technique was used. The effect of input time series length on the model performance was studied. The TBM-PR results of the LSTM-GWO model were compared to some other machine learning (ML) models such as LSTM. The results were evaluated using root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (R2). Finally, the LSTM-GWO model produced the most accurate results (test: R2 = 0.9795; RMSE = 0.004; MAPE = 0.009 %). The mutual information test revealed that input parameters of rock fracture class and uniaxial compressive strength have the most and least impact on the TBM-PR, respectively.