考虑承载-破坏全过程的钢筋网力学行为分析与预测

ANALYSIS AND PREDICTION OF MECHANICAL BEHAVIOR OF STEEL MESH CONSIDERING THE WHOLE LOADING-FAILURE PROCESS

考虑钢筋网承载-破坏全过程,探明锚网喷结构中钢筋网的力学行为,可切实指导钢筋网选型与参数设计。采用已有的试验结果对计算模型与参数的合理性进行验证,在此基础上完成承载-破坏全过程的钢筋网力学行为分析,并利用多元非线性回归和BP神经网络方法对钢筋网的最大应力和竖向位移进行预测与分析。研究发现:单一荷载作用下钢筋网的应力分布呈现分区现象,1区和2区特定范围的钢筋应力基本处于单轴拉伸状态,2区其余区域的应力处于拉-剪复合状态;钢筋网的力学行为受钢材型号影响显著:由HPB300、HPB400、HPB500钢筋制成的钢筋网存在流幅现象,由1650级、1770级高强不锈钢丝制成的钢筋网无流幅现象;BP神经网络模型预测精度明显优于多元非线性回归方法,对于最大应力和竖向位移两个指标,采用BP神经网络预测的平均相对误差分别为1.31%和1.67%,而多元非线性回归方法的预测误差为7.39%和8.19%。

Considering the whole loading-failure process of a steel mesh,the mechanical behavior of the steel mesh in the bolt-mesh-shotcrete structure can be verified,which can guide the selection and parameter design of the steel mesh practically.The existing experimental results were used to verify the rationality of the calculation model and parameters.On this basis,the mechanical behavior analysis of the steel mesh in the whole process of loading-failure was completed.The maximum stress and vertical displacement of the steel mesh were predicted and analyzed by multivariate nonlinear regression and BP neural network method.It is found that:The stress distribution of the steel mesh under a single load presents a zoning phenomenon.The steel stress in the specific range of area 1 and area 2 is basically in the uniaxial tensile state,and the stress in the other areas of area 2 is in a tensile-shear composite state.The mechanical behavior of the steel mesh is significantly affected by the steel type such as the steel mesh made of HPB300,HPB400 and HPB500 bars has plastic flow range phenomenon.There is no plastic flow range in the steel mesh made of 1650 grade and of 1770 grade high strength stainless steel wire.The prediction accuracy of BP neural network model is better than that of multivariate nonlinear regression method obviously.The average relative errors of maximum stress and vertical displacement predicted by BP neural network are 1.31%and 1.67%,respectively.The prediction errors of multivariate nonlinear regression method are 7.39%and 8.19%.