Soil–structure interaction is a key aspect to take into account when simulating the response of civil engineering structures subjected to dynamic actions. To this end, and due to its simplicity and ease of implementation, the dynamic Winkler model has been widely used in practical engineering applications. In this model, soil–structure interaction is simulated by means of spring–damper elements. A crucial point to guarantee the adequate performance of the approach is to accurately estimate the constitutive parameters of these elements. To this aim, this article proposes the application of a recently developed parameter identification method to address such problem. In essence, the parameter identification problem is transformed into an optimization problem, so that the parameters of the dynamic Winkler model are estimated by minimizing the relative differences between the numerical and experimental modal properties of the overall soil–structure system. A recent and efficient hybrid algorithm, based on the combination of the unscented Kalman filter and multi-objective harmony search algorithms, is satisfactorily implemented to solve the optimization problem. The performance of this proposal is then validated via its implementation in a real case-study involving an integral footbridge.

中文翻译：

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使用混合无味卡尔曼滤波器-多目标和声搜索算法识别动态温克勒土-结构相互作用模型的参数

土-结构相互作用是模拟土木工程结构在动态作用下的响应时要考虑的一个关键方面。为此，由于其简单易行，动态Winkler模型在实际工程应用中得到了广泛的应用。在这个模型中，土壤-结构相互作用是通过弹簧-阻尼元件模拟的。保证该方法足够性能的一个关键点是准确估计这些元素的本构参数。为此，本文提出应用最近开发的参数识别方法来解决此类问题。本质上，参数辨识问题转化为一个优化问题，从而通过最小化整个土壤结构系统的数值和实验模态特性之间的相对差异来估计动态 Winkler 模型的参数。一种基于无迹卡尔曼滤波器和多目标和声搜索算法相结合的最新高效混合算法，令人满意地实现了解决优化问题。然后，通过在涉及集成人行桥的真实案例研究中的实施来验证该提案的性能。