【摘要】 依托杭州地区淤泥质黏土地层中某盾构隧道工程，利用循环神经网络建立盾构隧道施工引发地表最大位移的预测模型，基于该预测模型和Morris方法分析地表最大位移对各个施工参数变化的全局敏感程度，以探究影响盾构掘进引发地表最大位移的主导因素。研究结果表明，建立的循环神经网络预测模型对地表最大位移的预测效果良好；盾构隧道掘进过程中的同步注浆压力是影响地表最大位移的主导因素，因此施加合适的注浆压力是控制地表最大沉降和隆起量的关键。研究成果可为实际盾构隧道工程中的地表变形控制提供有益参考。更多还原

【Abstract】 Based on a shield tunnel project in the muddy clay stratum of Hangzhou, a prediction model for the maximum ground displacement caused by shield tunneling is established using the recurrent neural network.The proposed prediction model and the Morris method are combined to obtain the global sensitivity of the maximum ground displacement to various construction parameters and determine the key influence parameters for the ground displacement due to shield excavation. The results show that the proposed model based on the recurrent neural network can predict the tunneling-induced maximum ground displacement fairly well. The synchronous grouting pressure during shield tunneling is the dominant factor affecting the maximum ground displacement, so the application of appropriate grouting pressure is important for the control the tunnelinginduced ground settlement or heave in muddy clay. The research results can provide a useful reference for the control of the ground displacement in practical shield tunnel engineering.更多还原