郭玉荣 龙沐恩

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.

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

中图分类号：TU317                                文献标志码：A

文章编号：1674—2974（2021）01—0126—09

Abstract：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.

Key words：hysteretic model;hysteretic behavior;parameter identification;robust SVD-UKF algorithm;particle swarm optimization algorithm

鋼筋混凝土框架结构的地震响应混合模拟[1] 及其抗倒塌性能的分析，需要可有效模拟钢筋混凝土构件滞回特征[2]的恢复力模型及准确的模型参数. 塑性铰模型是框架结构非线性模拟常采用的一种模型，它不仅反映构件的力学特征，还与构件的材料、约束状况及空间布局密切相关. 几十年以来，塑性铰模型已有了飞速发展，Clough等[3]开发了双线性模型;Wen[4]提出了光滑的塑性铰模型;Takeda等[5]开发了三线性塑性铰模型. 但是上述常见模型不能充分考虑构件在循环往复荷载作用下的刚度和强度退化，影响了整体结构模拟分析的精确性. Ibarra等[6-7]开发了复杂的塑性铰模型即……