Structural dynamic modeling is a key element in the analysis of building behavior for different environmental factors. Having this in mind, the authors propose a simple nonlinear model for studying the behavior of buildings in the case of earthquakes. Structural analysis is a key component of seismic design and evaluation. It began more than 100 years ago when seismic regulations adopted static analyzes with lateral loads of about 10% of the weight of the structure. Due to the dynamics and non-linear response of the structures, advanced analytical procedures were implemented over time. The authors' approach is the following: having a nonlinear dynamic model (in this case, a multi-segment inverted pendulum on a cart with mass-spring-damper rotational joints) and at least two datasets of a building, the parameters of the building's model are estimated using optimization algorithms: Particle Swarm Optimization (PSO) and Differential Evolution (DE). Not having much expertise on structural modeling, the present paper is focused on two aspects: the proposed model's performance and the optimization algorithms performance. Results show that among these algorithms, the DE algorithm outperformed its counterpart in most situations. As for the model, the results show us that it performs well in prediction scenarios.

中文翻译：

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建筑结构抗震模型参数优化。

结构动力学建模是针对不同环境因素分析建筑行为的关键要素。考虑到这一点，作者提出了一个简单的非线性模型来研究地震情况下的建筑物行为。结构分析是地震设计和评估的关键组成部分。它始于100多年前，当时地震法规采用静态分析，其侧向载荷约为结构重量的10％。由于结构的动力学和非线性响应，随着时间的流逝实施了先进的分析程序。作者的方法如下：拥有非线性动力学模型（在这种情况下，是具有质量弹簧-阻尼器旋转接头的手推车上的多段倒立摆）和至少两个建筑物的数据集，建筑物的参数' s的模型是使用以下优化算法估算的：粒子群优化（PSO）和差分演化（DE）。由于对结构建模没有足够的专业知识，因此本文主要集中在两个方面：所提出的模型的性能和优化算法的性能。结果表明，在这些算法中，DE算法在大多数情况下都优于同类算法。至于模型，结果表明我们在预测场景中表现良好。在大多数情况下，DE算法的性能都优于同类算法。至于模型，结果表明我们在预测场景中表现良好。在大多数情况下，DE算法的性能都优于同类算法。至于模型，结果表明我们在预测场景中表现良好。