Prediction of longitudinal surface settlement in composite formation using large-diameter shield machine based on machine learning techniques

Abstract

The employment of large-diameter shield machines has increased the likelihood of encountering composite formations, posing engineering challenges associated with excessive surface settlement. To tackle this issue, this study introduces a hybrid model which integrates the extreme learning machine (ELM) with the sparrow search algorithm (SSA) to predict longitudinal surface settlement. Based on on-site measurements. this study analyzed longitudinal surface settlement patterns across both homogeneous and composite formations. Tunneling parameters, geological parameters, and geometrical parameters were considered as input parameters. Furthermore, this study conducted a comparative analysis of the predictive performance among SSA-ELM, ELM, and SSA-back propagation (BP), with respect to coefficient of determination (R²), mean absolute error (MAE), root mean square error (RMSE), and training time. Last, in anticipation of potential risks, a feasible optimization approach is provided. SSA-ELM outperforms both ELM and SSA-BP in terms of R², MAE, and RMSE, with values of 0.8822, 0.3357, and 0.4072, respectively. Regarding training time, SSA-ELM requires 0.2346 s, prior to SSA-BP with a value of 1.8427. Although it is not as fast as ELM, the discrepancy between SSA-ELM and ELM is only 0.1187 s. Overall, SSA-ELM demonstrates higher performance and serves as an effective tool to guide the construction process.

Graphical abstract

Keywords

large-diameter shield machine / composite formation / extreme learning machine / sparrow search algorithm / longitudinal surface settlement

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