摘要
为提高复杂结构模型更新数值混合模拟精度及工程应用能力,提出自复位摩擦耗能支撑结构多尺度模型更新数值混合模拟方法。以OpenSees和MATLAB为计算平台,对二层带有自复位摩擦耗能支撑结构钢框架进行在线数值混合模拟。该方法采用容积卡尔曼滤波器算法,同时对单轴钢材Giuffre-Menegotto-Pinto材料本构模型和自复位摩擦耗能支撑构件模型进行参数识别,并更新整体结构数值模型。结果表明,与传统数值混合模拟方法相比,多尺度模型更新数值混合模拟方法有效提高了数值模型精度,显著降低了耗能、残余变形、顶层相对位移、最大层间位移角的相对误差,验证了多尺度模型更新数值混合模拟方法的有效性。
Here,to improve accuracy and engineering application ability of model updating numerical hybrid simulation of complex structures,a multi-scale model updating numerical hybrid simulation method of self-centering friction energy dissipation braced structures was proposed.Taking OpenSees and MATLAB as calculation platforms,the on-line numerical hybrid simulation for a two-story steel frame with self-centering friction energy dissipation braced structure was performed.In this method,the cubature Kalman filter algorithm was used to identify parameters of the uniaxial steel Giuffre-Menegotto-Pinto material constitutive model and the self-centering friction energy dissipation brace model,and update numerical model of the overall structure.The results showed that compared with the traditional numerical hybrid simulation method,the proposed multi-scale model updating numerical hybrid simulation method can effectively improve accuracy of numerical model,and significantly reduce relative errors of energy consumption,residual deformation,top layer relative displacement and maximum interlayer displacement angle;its effectiveness is verified.
作者
王涛
李勐
孟丽岩
许国山
刘家秀
谢婧怡
WANG Tao;LI Meng;MENG Liyan;XU Guoshan;LIU Jiaxiu;XIE Jingyi(School of Civil Engineering,Heilongjiang University of Science&Technology,Harbin 150022,China;School of Civil Engineering,Harbin Institute of Technology,Harbin 150090,China)
出处
《振动与冲击》
EI
CSCD
北大核心
2022年第17期25-34,62,共11页
Journal of Vibration and Shock
基金
国家自然科学基金项目(51978213,52078398)
哈尔滨工业大学结构工程灾变与控制教育部重点实验室开放基金课题(HITCE202008)。
关键词
多尺度模型更新
数值混合模拟
容积卡尔曼滤波器(CKF)
自复位摩擦耗能支撑
模型更新
参数识别
multi-scale model updating
numerical hybrid simulation
cubature Kalman filter(CKF)
self-centering friction energy dissipation brace
model update
parametric identification
