Abstract

Structural members exhibit hysteretic behavior under cyclic loading. Among the hysteresis models available in the literature, the differential model proposed by Bouc-Wen is most widely used, owing to its robustness. This model involves many parameters that define the shape of the hysteresis loops. Estimating these unknown parameters is an identification problem that can be tackled by optimization algorithms by using prediction error as the objective function. Stochastic methods like simulated annealing and genetic algorithms can help find global minima but at a high computational cost. Here, the homotopy technique is employed to identify the unknown parameters. The efficiency of this technique in identifying the parameters of the Bouc–Wen model is demonstrated with examples. The present approach is then compared with global optimization methods, such as genetic algorithms and particle swarm optimization techniques. Numerical results confirm that the homotopy method is superior in terms of computational effort and convergence efficiency.

摘要

结构构件在循环载荷下表现出滞后行为。在文献中可用的滞后模型中，Bouc-Wen 提出的微分模型由于其鲁棒性而得到最广泛的应用。该模型涉及许多定义磁滞回线形状的参数。估计这些未知参数是一个识别问题，可以通过使用预测误差作为目标函数的优化算法来解决。模拟退火和遗传算法等随机方法可以帮助找到全局最小值，但计算成本很高。在这里，使用同伦技术来识别未知参数。该技术在识别 Bouc-Wen 模型参数方面的效率通过示例进行了展示。然后将本方法与全局优化方法进行比较，例如遗传算法和粒子群优化技术。数值结果证实，同伦方法在计算量和收敛效率方面具有优势。