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主管单位 中华人民共和国
工业和信息化部
主办单位 哈尔滨工业大学 主编 李隆球 国际刊号ISSN 0367-6234 国内刊号CN 23-1235/T

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引用本文:唐贞云,刘浩东,李勇.适于隔震结构的两阶段实时子结构试验方法[J].哈尔滨工业大学学报,2023,55(9):27.DOI:10.11918/202201053
TANG Zhenyun,LIU Haodong,LI Yong.Two-stage real-time hybrid testing method for isolated structures[J].Journal of Harbin Institute of Technology,2023,55(9):27.DOI:10.11918/202201053
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适于隔震结构的两阶段实时子结构试验方法
唐贞云1,刘浩东1,李勇2
(1.城市与工程安全减灾教育部重点实验室(北京工业大学),北京 100124; 2.道路与铁道工程安全保障教育部重点实验室(石家庄铁道大学),石家庄 050043)
摘要:
为在试验中同时模拟隔震结构中隔震层和被隔震结构的非线性,提出一种适于隔震结构的两阶段实时子结构试验方法。第一阶段先将隔震支座单独进行物理试验,通过试验观测数据训练神经网络,使其可以准确拟合隔震支座动力特性;第二阶段基于训练好的神经网络建立隔震层的数值子结构模型,与被隔震结构进行实时子结构试验,完成对隔震结构的动力性能测试。数值仿真中该方法与整体模型仿真结果的峰值误差在3%以内,试验验证中该方法与整体结构振动台试验的峰值误差在6%以内。数值仿真和试验对比表明:提出的两阶段实时子结构试验具有良好的精度。该方法避免了在振动台试验中由于缩尺导致隔震支座受到欠质量的影响,充分保证其力学性能的真实性。同时有效解决了在利用实时子结构试验对各部分都为强非线性的隔震结构进行试验测试时,由于数值子结构建模不准确导致的试验误差问题。为隔震结构的抗震性能测试提供了新的试验方法。
关键词:  实时子结构试验  隔震结构  神经网络  振动台试验  欠质量
DOI:10.11918/202201053
分类号:TU317
文献标识码:A
基金项目:国家自然科学基金(51978016);道路与铁道工程安全保障教育部重点实验室(石家庄铁道大学)开放课题(STDTKF202003)
Two-stage real-time hybrid testing method for isolated structures
TANG Zhenyun1,LIU Haodong1,LI Yong2
(1.Key Lab of Urban Security and Disaster Engineering (Beijing University of Technology), Ministry of Education, Beijing 100124, China; 2.Key Lab of Roads and Railway Engineering Safety Control (Shijiazhuang Tiedao University), Ministry of Education, Shijiazhuang 050043, China)
Abstract:
To simulate the nonlinearity of isolation layer and isolated structure simultaneously in seismic testing, this paper proposes a two-stage real-time hybrid testing method for isolated structures. In the first stage, the isolation bearing was tested separately, and the neural network was trained by the test data for fitting the dynamic characteristics of the isolation bearing. In the second stage, the trained neural network was used to build the numerical substructure model of isolation layer, which was then combined with the isolated structure to realize real-time hybrid testing (RTHT), so as to complete the dynamic performance test of the overall isolated structures. In numerical simulation, the peak error of the results between the proposed method and overall model simulation was within 3%, while in experimental verification, the peak error of the results between the proposed method and shaking table testing of overall structures was within 6%. Numerical simulation and experimental comparison show that the proposed two-stage real-time hybrid testing has a good accuracy. It avoids the influence of insufficient mass of isolation bearing caused by reduced scale in shaking table testing, and ensures the authenticity of its dynamic performance. Besides, it can solve the errors caused by inaccurate modeling of numerical substructure when using RTHT to test the isolated structures with strong nonlinearity in each part. The two-stage real-time hybrid testing provides a new method for seismic testing of isolated structures.
Key words:  real-time hybrid testing  isolated structures  neural network  shaking table testing  insufficient mass

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