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作 者:郭玉荣[1,2] 龙沐恩 GUO Yurong;LONG Muen(College of Civil Engineering,Hunan University,Changsha 410082,China;Key Laboratory of Building Safety and Energy Efficiency(Hunan University),Ministry of Education,Changsha 410082,China)
机构地区:[1]湖南大学土木工程学院,湖南长沙410082 [2]建筑安全与节能教育部重点实验室(湖南大学),湖南长沙410082
出 处:《湖南大学学报(自然科学版)》2021年第1期126-134,共9页Journal of Hunan University:Natural Sciences
基 金:国家自然科学基金资助项目(51878259,51161120360,91315301-09)。
摘 要:提出了一种利用钢筋混凝土柱拟静力试验数据识别改进IMK模型骨架曲线参数,进而提高钢筋混凝土框架结构非线性模拟精度的方法.通过引入可抗差的基于奇异值分解的无迹卡尔曼滤波算法(抗差SVD-UKF算法),抑制观测值粗差对参数识别的影响,采用粒子群算法对初始协方差矩阵、过程噪声矩阵和测量噪声矩阵进行自动寻优,在MATLAB中实现了柱滞回特征正负向对称与非对称两种情况下改进IMK恢复力模型骨架曲线参数的识别.钢筋混凝土柱实测滞回曲线的模型骨架曲线参数识别结果及其在框架结构非线性模拟中的应用结果验证了本文方法的有效性.A method for identifying the backbone curve parameters of the modified Ibarra-Medina-Krawinkler(IMK)model by using quasi-static test data of reinforced concrete columns and thus improving the simulation accuracy of reinforced concrete frame structures is proposed in this paper.In this method,a robust unscented Kalman filtering algorithm based on singular value decomposition(robust SVD-UKF algorithm)is introduced to suppress the influence of gross error of the observation on the parameter identification,and the particle swarm optimization algorithm is adopted to automatically optimize the initial covariance matrix,the process and measurement noise matrices.The identification of backbone curve parameters of the modified IMK model is realized using MATLAB,in which the symmetric and asymmetric hysteresis behavior of the columns in the positive and negative direction is considered.The effectiveness of the proposed method is verified by model backbone curve parameter identification based on the measured hysteretic curves of reinforced concrete columns and its application in the nonlinear simulation of frame structures.
关 键 词:恢复力模型 滞回特征 参数识别 抗差SVD-UKF算法 粒子群算法
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