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建筑物结构的地震模型参数优化。

Seismic Model Parameter Optimization for Building Structures.

机构信息

Department of Automation, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, Memorandumului Str. 28, 400014 Cluj-Napoca, Romania.

出版信息

Sensors (Basel). 2020 Apr 1;20(7):1980. doi: 10.3390/s20071980.

Abstract

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.

摘要

结构动力建模是分析建筑物在不同环境因素下行为的关键要素。考虑到这一点,作者提出了一个简单的非线性模型,用于研究地震情况下建筑物的行为。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,当时地震规范采用了约结构重量 10%的侧向荷载的静态分析。由于结构的动力和非线性响应,随着时间的推移,实施了先进的分析程序。作者的方法如下:具有非线性动力学模型(在这种情况下,是一个带有质量-弹簧-阻尼旋转关节的推车多段倒立摆)和至少两个建筑物数据集,使用优化算法(粒子群优化算法 (PSO) 和差分进化算法 (DE)) 估计建筑物模型的参数。由于在结构建模方面没有太多专业知识,本文主要关注两个方面:所提出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优于其对应算法。至于模型,结果表明它在预测场景中表现良好。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c39/7181031/86c9ae6d2c99/sensors-20-01980-g001.jpg

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