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基于IPOA的巨型组合框架结构震损快速预测模型研究

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
  • 维普
为实现巨型组合框架结构的地震损伤程度快速评估,提出了一种基于改进鹈鹕优化算法(IPOA)的多参数震损预测方法.设计了5个不同参数的巨型组合框架结构模型,利用振动台试验和有限元软件进行非线性时程分析获取结构动态响应数据,并采用结构损伤指数量化评估结构的损伤程度.同时,引入K均值聚类优化策略和惯性权重自适应优化策略改进传统的鹈鹕优化算法.基于振动台试验和有限元分析结果数据,比较了不同输入参数组合预测结构损伤的准确性,构建了能反映结构参数与结构损伤之间非线性关系的智能算法快速预测模型.最后,将模型预测结果与一缩尺比为1/15的振动台试验模型结构损伤程度进行对比验证.结果表明:(1)改进鹈鹕优化算法模型的准确性和泛化能力均优于其他算法模型;(2)最大层间位移角与结构损伤相关性最高,增加影响结构损伤的输入参数可提高预测模型的准确度和泛化能力;(3)模型预测的结构损伤指数与试验结果相比误差均小于10%,预测结构损伤等级与试验结果一致,所提出的快速预测模型能高效准确地预测结构的损伤指标,为巨型组合框架结构震后损伤快速评估提供了一种新方法.
Rapid Prediction Model for Seismic Damage in Mega Composite Frame Structures Based on IPOA Methods
To rapidly assess the extent of seismic damage in mega composite frame structures,this study introduced a multi-parameter seismic damage prediction method utilizing Improved Pelican Opti-mization Algorithm(IPOA).Five models with different parameters were developed,and dynamic re-sponse data for the structures were obtained through shaking table tests and nonlinear time-history analyses using finite element(FE)software.Structural damage indices were quantified to assess the ex-tent of damage.Additionally,the traditional Pelican Optimization Algorithm(POA)was enhanced by incorporating K-means clustering optimization and inertia weight adaptive optimization strategy.Based on the data from shaking table tests and FE analyses,the accuracy of structural damage predictions us-ing different input parameter combinations was compared.A rapid prediction model using an intelli-gent algorithm was constructed to reflect the nonlinear relationship between structural parameters and its damage.Finally,the model's predictions were compared and verified against the seismic damage extent from shaking table tests on a 1/15 scale model.The results indicated that:(1)The IPOA model exhibited superior accuracy and generalization capability compared to other algorithm models;(2)The maximum inter-story drift angle showed the highest correlation with structural damage.The introduc-tion of additional input parameters that affected structural damage could enhance the model's predic-tion accuracy and its generalization capability;(3)The predicted structural damage indices exhibited an error of less than 10%compared to the experimental results,and the predicted levels of structural damage aligned with the experimental results.The proposed rapid prediction model can effectively and accurately predict structural damage indicators.

machine learningmega structurerapid assessmentPOAstructural damage

黄志、周芙蓉、陈娟、蒋丽忠、周旺保、戚菁菁

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湖南科技大学土木工程学院,湖南 湘潭 411201

湖南省智慧建造装配式被动房工程技术研究中心,湖南 湘潭 411201

湖南科技大学信息与电气工程学院,湖南 湘潭 411201

中南大学土木工程学院,湖南 长沙 410075

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机器学习 巨型结构 快速评估 鹈鹕优化算法 结构损伤

国家自然科学基金项目国家自然科学基金项目湖南省自然科学基金项目湖南省教育厅科学研究优秀青年项目湖南省教育厅科学研究优秀青年项目湖南省教育厅科学研究重点项目

52204210518082132023JJ3024221B045220B21420A184

2024

10.13409/j.cnki.jdpme.20231025001

防灾减灾工程学报
中国灾害防御协会 江苏省地震局

防灾减灾工程学报

CSTPCD北大核心
影响因子：0.529
ISSN：1672-2132
年,卷(期)：2024.44(2)
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