考虑楼板刚度贡献的梁柱节点半刚性连接弯矩-转角神经网络模型

Neural Network Model of Moment-rotation Relation in Semi-rigid Beam-column Joints Considering Floor Stiffness

首先对已有钢结构梁柱节点半刚性连接弯矩-转角经典模型存在的不足进行了述评;然后采用非线性有限元方法对有无楼板刚度贡献的外伸端板半刚性连接节点进行了非线性仿真分析,把获得的这种节点半刚性连接弯矩-转角关系与常用梁柱节点半刚性连接弯矩-转角经典模型进行了对比分析;最后运用神经网络智能算法,首次提出了考虑楼板刚度影响的十参数梁柱节点外伸端板半刚性连接智能模型。研究结果表明:楼板的存在增大了梁柱节点刚度,减少了节点相对转动,使得实际工程中存在楼板刚度影响的半刚性连接弯矩-转角关系与现有半刚性连接弯矩-转角模型计算得到的弯矩-转角关系存在着较大误差;提出的十参数半刚性连接弯矩-转角神经网络模型能较好地模拟这种节点实际受力和变形情况;提出的智能模型具有较高精度和计算效率,同时也具有可靠性、有效性和实用性。研究结果可为考虑楼板刚度贡献的其他半刚性连接形式的弯矩-转角关系的进一步研究以及在实际工程中的应用提供参考。

The shortage of existing classic moment-rotation model which was semi-rigid connections of beam-column joints for steel structure was reviewed.The nonlinear simulation analysis of semi-rigid joints for end-plate steel structure with floor stiffness and without floor stiffness were carried out using nonlinear finite element method,and the moment-rotation relation in semi-rigid joints was compared with that in semi-rigid classic moment-rotation model.Finally,based on the neural network intelligent algorithm,the ten parameters neural network model which considered the effect of floor stiffness was established.The study results show that the floor can increase the stiffness of the beam-column joints,and reduce the relative rotation of joints which makes the curves for semi-rigid joints in practical engineering has large error with the existing classic semi-rigid joint model;the neural network model of semi-rigid joints for steel structure with floor stiffness effect can simulate the reality loading and transformation performance of semi-rigid beam-column joints;the neural network model has higher precision and computational efficiency,reliability,validity and practicability.The obtained results can provide references for the further study in other semi-rigid connections with floor stiffness and application in practical engineering.