A prediction method for shield tunneling attitude in subway tunnels based on EMD-LightGBM model

Abstract:

In response to the problem of difficult control of shield tunneling attitude in subway tunnels, taking a tunnel project in Changchun as an example, a shield tunneling attitude prediction model (EMD-LightGBM) was constructed based on on-site measured data, which integrates empirical mode decomposition (EMD) and lightweight gradient boosting machine (LightGBM). Firstly, filter the features of the original dataset through feature importance and correlation analysis. Then, the data is decomposed into multiple stationary subsequences and combined into a new dataset by EMD. Finally, EMD-LightGBM was fitted and trained by the new dataset to achieve the prediction of shield tunnel attitude, and the prediction performance of the model was compared with that of LightGBM alone and the EMD-BPNN. Verify the excellent performance of the EMD-LightGBM model through two evaluation systems: prediction accuracy and prediction stability. The results showed that compared with LightGBM and EMD-BPNN, EMD-LightGBM performed the best in predicting shield attitude deviation in the line graph, with a maximum mean absolute error (E_MA) and root mean square error (E_RMS) of 2.89 mm and 4.13 mm, respectively, and a minimum coefficient of determination R² of 0.95. Meanwhile, the maximum 95% confidence intervals for the E_MA and mean square error (E_MS) of EMD-LightGBM predictions are 3.5 mm and 25.6 mm², respectively. Combined with the good frequency distribution of its predicted absolute error (E_A) and square error (E_S), it demonstrates the high accuracy and stability of EMD-LightGBM in predicting shield tunnel attitudes. The research results can provide a theoretical method for the attitude control of shield tunneling in similar projects.