基于GSO算法的BRB改进Bouc-Wen模型参数识别

屈曲约束支撑(buckling-restrained brace, BRB)恢复力模型的合理与否直接影响BRB结构计算分析的准确度。改进的Bouc-Wen模型能很好地描述钢板装配式BRB的滞回特性。为了提高BRB改进Bouc-Wen模型参数识别的自动化程度及精度，采用GSO群智能搜索算法编写了BRB改进Bouc-Wen模型的参数识别优化程序。对不同屈服承载力的两根钢板装配式BRB滞回曲线进行了参数识别，把识别的滞回曲线与钢板装配式BRB试验结果所得的滞回曲线进行了对比。研究结果表明：采用GSO群智能搜索算法对BRB改进Bouc-Wen模型参数优化识别的方法可行，自动化程度高，拟合结果吻合良好，只需根据钢板装配式BRB试验结果所给出的力、位移以及对应的时间点便可实现对改进Bouc-Wen模型参数的自动识别。基于试验结果和相关参数的物理意义限定所识别参数取值范围的方法提高了GSO群智能搜索算法的识别效率。

Parametric identification of BRB based on improved Bouc-Wen model using GSO algorithm

The buckling-restrained brace (BRB) restoring force model quality directly affects the accuracy of BRB structure analysis. The improved Bouc-Wen model can well describe the hysteretic performance of steel-plate assembled BRB.In order to improve the automation and accuracy of parameter identification of the BRB based on improved Bouc-Wen model, the parameter identification and optimization program of steel-plate assembled BRB is compiled by using GSO algorithm. Parameter identification of the two steel-plate assembled BRB with different yield bearing capacity is carried out. The hysteresis loops of steel-plate assembled BRB are compared with those obtained from the test results. The research results show that the proposed parameter identification method for steel-plate assembled BRB with improved Bouc-Wen model by using GSO algorithm is feasible. It has high accuracy and high automation. The fitting results are in good agreement with the test results. According to the force and displacement data and the corresponding time points in the test results of steel-plate assembled BRB, the automatic identification of Bouc-Wen model parameters can be realized. The recognition efficiency of the proposed method is improved by limiting the range of the parameters based on the physical meaning and the test results.