Time-series traction prediction of surrounding rock deformation in tunnel construction based on mechanical parameter inversion

The deformation of the surrounding rock serves as a macroscopic representation of the stability status of the tunnel, and is also an important indicator for stability evaluation. To address the difficulty of accurately predicting long-term deformation in tunnel surrounding rock with limited monitoring data, a time-series traction prediction method based on the inversion of mechanical parameters was proposed. Employing the adaptive vigilance chaotic sparrow search algorithm (ACSSA) to optimize initial parameters in extreme learning machines (ELM) and long short-term memory (LSTM) neural networks. It then constructed two deformation time-series prediction models and a rock mechanics parameter inversion model based on the ACSSA-ELM and ACSSAAMLSTM algorithms. Based on the mechanical parameter inversion model and numerical simulation, the value of the surrounding rock deformation at the key construction nodes of the tunnel was calculated as the traction point, and the traction interval and traction control equation were defined. Cubic spline interpolation and wavelet decomposition were employed to preprocess the measured deformation data of the surrounding rock. Subsequently, the proposed time-series prediction model was utilized for prediction. The predicted results within the traction interval undergo correction based on the traction points, thereby achieving a more precise and intelligent prediction of the tunnel surrounding rock deformation. Based on literature cases and data from the Huayang Tunnel project in Chongqing, the proposed method was validated and applied. The model 's accuracy has been validated and successfully guided construction and production. Finally, the impact of different methods on traction effectiveness and the distribution pattern of traction points on model performance was discussed.