Identification of Tunneling Strata with PSO-BP Neural Network Based on SPB Tunneling Parameters
To address the issue of inaccurate real-time identification of tunneling strata by slurry pressure balance shield,this study focused on the Pearl River Delta water resource allocation project.The variations of tunnelling parame-ters such as shield thrust,cutterhead torque,tunneling speed,and cutterhead rotation speed in different strata were ana-lyzed.The method of strata identification based on PSO-BP neural network tunneling parameters was proposed.The strata identification model was established with four tunneling parameters(shield thrust,cutterhead torque,tunneling speed,and cutterhead rotation speed)as input features and strata code as the output set.The model was validated using engineering data.The results demonstrate that the model achieves an identification accuracy of 99.07% on the tunneling layers of the Pearl River Delta water resource allocation project dataset.The identification accuracy of the PSO-BP neural network algorithm significantly outperforms other machine learning algorithms such as BP,RF,RBF,and CNN.
万方数据
维普
