摘要
梁-弹簧模型计算公式简洁、数值实施方便,可以较好地评价衬砌管片的接头效应,在盾构隧道衬砌结构设计中已被广泛应用。作为表征接头性能的重要参数,接头刚度(尤其是抗弯刚度)的选取直接决定了梁-弹簧模型的计算精度,对盾构隧道衬砌结构的内力分析具有十分重要的意义。以内力(弯矩、轴力)为输入特征,以接头抗弯刚度为输出建立可以反映接头抗弯刚度非线性特征的深层神经网络模型。基于接头足尺试验数据,通过前向传播和反向传播算法训练得到具有解析形式的、全域适用的接头抗弯刚度与内力之间的非线性映射函数,克服了传统方法拟合结果局部性及难以得到整个内力空间接头抗弯刚度表达式的缺陷,为基于接头足尺试验数据的接头抗弯刚度非线性预测及统一连续表达提供了一种新的手段。深层神经网络接头抗弯刚度非线性模型预测值与足尺试验数据的对比及其在整环衬砌结构计算分析中的应用表明了该方法的正确性和有效性。
The beam-spring model calculation formula is concise and easy to use in numerical calculation,it can be used to effectively evaluate the jointing effect of lining segments,and it has been widely used for design of lining structure of shield tunnel.As an important parameter that represents the joint performance,the joint stiffness(particularly the flexural stiffness)selected will directly determine the calculation accuracy of the beam-spring model and it is very important for internal forces analysis of lining structure of shield tunnel.With the internal forces(bending moment and axial force)as the input features and the flexural stiffness of joint as the output,this paper offers a deep neural network model that can indicate the nonlinear feature of flexural stiffness of joint.Based on the joint fullscale test data and through forward propagation and back propagation algorithms training,the nonlinear mapping function between joint flexural stiffness and internal forces that has analytical form and that is universally applicable has been obtained.This overcomes the limitedness of fitting results of traditional method and solves the problem that it is difficult to obtain the expression for joint flexural stiffness in the entire internal forces space.This comes as a new approach for nonlinear prediction and uniform continuous expression of joint flexural stiffness based on joint full-scale test data.The comparison between the joint flexural stiffness value predicted by the nonlinear model based on deep neural network and the full-scale test data and the application of the method in calculation and analysis of full-ring lining structure have proven the correctness and effectiveness of this method.
作者
闫鹏飞
蔡永昌
周龙
YAN Pengfei;CAI Yongchang;ZHOU Long(State Key Laboratory of Disaster Reduction in Civil Engineering,Tongji University,Shanghai 200092)
出处
《现代隧道技术》
CSCD
北大核心
2023年第3期24-33,73,共11页
Modern Tunnelling Technology
基金
国家自然科学基金(52008308).
