摘要
集中塑性铰模型常被用于基于构件的结构弹塑性分析,计算精度依赖于模型参数的选取,但经验公式难以表征构件受力特性与模型参数的复杂非线性关系。该研究收集低周往复加载RC梁试验数据。建立构件试验数据库,采用捏拢型(pinch Ibarra-Medina-Krawinkler,Pinch-IMK)本构建立基于深度学习的RC梁构件集中塑性铰模型参数预测模型。基于试验数据对三折线骨架特征点参数、本构滞回参数进行参数辨识,得到182组骨架特征点参数数据和91组滞回参数数据;以构件特征参数为输入,以骨架特征点参数、滞回参数为输出,建立Pinch-IMK集中塑性铰RC梁构件参数深度学习预测模型HDLM。将HDLM预测骨架特征点参数与截面分析及现有经验公式等方法的计算结果作对比,可见HDLM预测结果有更高的精度;将基于HDLM预测参数计算的滞回曲线与基于经验公式的IMK模型计算结果进行对比,可见HDLM预测滞回曲线更为准确,能够较好地表现RC梁的强度退化、刚度退化和捏拢效应。
The lumped plastic hinge model is often used in component-based elasto-plastic analysis of structures.The calculation accuracy depends on the selection of model parameters,but empirical formulas are difficult to describe the complex nonlinear relationship between the component mechanical characteristics and model parameters.It collects the experimental results of RC beam under revered cyclic loadings as the database.The pinch Ibarra-Medina-Krawinkler(Pinch-IMK)model was used as the basic hysteretic model of RC beams,and a lumped-plastic-hinge-model parameter prediction model of RC beam is proposed based on deep learning.By the grounds of experimental data,the Trilinear skeleton feature point parameters and hysteresis parameters are identified.182 sets of skeleton feature point parameters and 91 sets of hysteresis parameters are obtained.Take the component feature parameters as inputs,the skeleton feature point parameters and the hysteresis parameters as outputs to establish a Pinch-IMK parameters deep learning prediction model of RC beam,named HDLM.By comparing the results of fiber moment-curvature analysis and that of existing empirical formulas,it is obvious that HDLM predicts the skeleton feature point parameters more accurately.By comparing the hysteretic curve calculated based on HDLM prediction parameters with that of IMK model based on empirical formula,it can be seen that HDLM is more accurate in predicting hysteretic curve and can better represent the strength degradation,stiffness degradation and pinch effect of RC beams.
作者
韩小雷
冯润平
季静
吴梓楠
HAN Xiao-lei;FENG Run-ping;JI Jing;WU Zi-nan(School of Civil Engineering and Transportation,South China University of Technology,Guangzhou 510640,China;State Key Laboratory of Subtropical Building Science,South China University of Technology,Guangzhou 510640,China)
出处
《工程力学》
EI
CSCD
北大核心
2021年第11期160-169,共10页
Engineering Mechanics
基金
广东省自然科学基金项目(2020A1515010739)
广州市科技计划项目(201904010221)。
关键词
RC梁
集中塑性铰模型
深度学习
滞回曲线
骨架曲线
RC beam
lumped plastic hinge model
deep learning
hysteresis loops
skeleton curve
