To provide theoretical support for the safety control of rectangular pipe jacking tunnels crossing an existing expressway, a method for predicting the surface settlement of a rectangular pipe jacking tunnel is proposed in this study. Therefore, based on the high approximation of the BP neural network to any function under the multiparameter input, the PSO-BP mixed prediction model of the ground subsidence of the ultrashallow buried large section rectangular pipe jacking tunnel is established by taking into account the adaptive mutation method, adopting the improved particle swarm optimization (IPSO) algorithm with adaptive inertia weight and mutation particles in the later stage to determine the optimal hyperparameters of the prediction model. Through the case study of an ultrashallow large cross-section rectangular pipe jacking tunnel, this algorithm is compared with the traditional algorithm and combined with field monitoring data for analysis and prediction. The prediction results show that compared with the traditional BP neural network prediction model, AWPSO-BP model and PWPSO-BP model, the improved PSO-BP mixed prediction model shows a more stable prediction effect when the change in surface subsidence is gentle and the concavity and convexity are large. The predicted subsidence value is close to the actual value, and the accuracy and robustness of the prediction are significantly improved.

为为矩形顶管隧道穿越既有高速公路的安全控制提供理论支持，提出了一种矩形顶管隧道地表沉降预测方法。因此，基于BP神经网络对多参数输入下任意函数的高度逼近，建立了考虑自适应变异的超浅埋大断面矩形顶管隧道地表沉降PSO-BP混合预测模型方法，在后期采用自适应惯性权重和变异粒子的改进粒子群优化（IPSO）算法来确定预测模型的最优超参数。通过超浅大断面矩形顶管隧道实例研究，该算法与传统算法进行对比，并结合现场监测数据进行分析预测。预测结果表明，与传统的BP神经网络预测模型、AWPSO-BP模型和PWPSO-BP模型相比，改进的PSO-BP混合预测模型在地表沉降变化平缓、凹陷度变化小的情况下表现出更稳定的预测效果。并且凸度很大。预测的沉降值接近实际值，显着提高了预测的准确性和鲁棒性。当地表沉降变化平缓、凹凸较大时，改进的PSO-BP混合预测模型显示出更稳定的预测效果。预测的沉降值接近实际值，显着提高了预测的准确性和鲁棒性。当地表沉降变化平缓、凹凸较大时，改进的PSO-BP混合预测模型显示出更稳定的预测效果。预测的沉降值接近实际值，显着提高了预测的准确性和鲁棒性。