| description abstract | Accurate prediction of tunnel boring machine (TBM) penetration rates is of great significance for intelligent TBM construction. Traditional empirical and theoretical models of TBM penetration rates are difficult to adapt to complex and changeable formation environments. To improve the adaptability, this paper proposes a TBM penetration rate prediction model based on the particle swarm optimization (PSO)-Elman algorithm fusion. Particle swarm optimization (PSO) was used to find the optimal connection weight matrix, which was inserted into the Elman network, and the TBM penetration rate was predicted by the machine learning method. This study examined field data from two distinct tunnel sections, focusing on their geological conditions, construction challenges, and environmental impacts. By analyzing the characteristics unique to these sites, the research offers a comparative perspective on tunnel engineering in diverse settings. Five parameters—uniaxial compressive strength (UCS), rock integrity index (Kv), cutter head thrust (Fn), cutter head speed (RPM), and penetration degree (P)—were selected as the input parameters. The TBM penetration rate was estimated by neural network training of the model. The results show that the PSO method effectively can overcome the problem of being prone to a local minimum using the single Elman method, and the PSO-Elman model has a fast convergence speed and high accuracy. In the 20 groups of experimental samples selected, the mean absolute percentage error (MAPE) was 3.38%, and the coefficient of determination (R2) was 0.936. The prediction quality was better than that of the single Elman method or the backpropagation neural network (BP) method. The study yields specific insights into efficient tunnel construction methodologies and practical neural network tools for risk management, highlighting innovative approaches in environmental preservation and safety enhancement in tunnel engineering. | |
