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基于应变监测数据预测值的藏式古建筑木结构健康状态评估

杨娜 李天昊 赵恭民

杨娜, 李天昊, 赵恭民. 基于应变监测数据预测值的藏式古建筑木结构健康状态评估[J]. 工程力学, 2023, 40(2): 112-123, 134. doi: 10.6052/j.issn.1000-4750.2021.08.0636
引用本文: 杨娜, 李天昊, 赵恭民. 基于应变监测数据预测值的藏式古建筑木结构健康状态评估[J]. 工程力学, 2023, 40(2): 112-123, 134. doi: 10.6052/j.issn.1000-4750.2021.08.0636
YANG Na, LI Tian-hao, ZHAO Gong-min. HEALTH ASSESSMENT OF TIBETAN ANCIENT WOOD STRUCTURES BASED ON THE PREDICTED VALUE OF STRAIN MONITORING DATA[J]. Engineering Mechanics, 2023, 40(2): 112-123, 134. doi: 10.6052/j.issn.1000-4750.2021.08.0636
Citation: YANG Na, LI Tian-hao, ZHAO Gong-min. HEALTH ASSESSMENT OF TIBETAN ANCIENT WOOD STRUCTURES BASED ON THE PREDICTED VALUE OF STRAIN MONITORING DATA[J]. Engineering Mechanics, 2023, 40(2): 112-123, 134. doi: 10.6052/j.issn.1000-4750.2021.08.0636

基于应变监测数据预测值的藏式古建筑木结构健康状态评估

doi: 10.6052/j.issn.1000-4750.2021.08.0636
基金项目: 国家自然科学基金面上项目(51778045,51878034);高等学校学科创新引智计划项目(B13002)
详细信息
    作者简介:

    李天昊(1992−),男,辽宁人,博士生,主要从事中国古建筑木结构健康监测研究(E-mail: celith@bjtu.edu.cn)

    赵恭民(1995−),女,山东人,硕士,主要从事监测数据分析研究(E-mail: 18121175@bjtu.edu.cn)

    通讯作者:

    杨 娜(1974−),女(满族),辽宁人,教授,博士,博导,主要从事钢-木结构与结构健康监测研究(E-mail: nyang@bjtu.edu.cn)

  • 中图分类号: TU17

HEALTH ASSESSMENT OF TIBETAN ANCIENT WOOD STRUCTURES BASED ON THE PREDICTED VALUE OF STRAIN MONITORING DATA

  • 摘要: 为增加藏式古建筑木结构的安全冗余度,该文提出了短期应变监测数据预测方法,以此对藏式古建筑木结构健康状态进行预判性评估。以回廊结构为对象,利用Sobel算子与卷积神经网络方法处理传感器带来的多种数据异常。通过多项式回归和功率谱密度的方法确定应变监测数据组分。引入变分模态分解法对数据进行解耦,以Prophet算法预测短期温度监测应变,以Gumbel极值理论预测不同重现期下的人群监测应变。以不同重现期下的应变监测数据预测值的叠加结果确定需重点关注的结构位置和需立即修缮的结构位置,以满足藏式古建木构的预防性保护要求和最小扰动原则。
  • 图  1  藏式古建筑木结构

    Figure  1.  Tibetan ancient wood structure

    图  2  文章框架

    Figure  2.  Framework of the method

    图  3  监测结构示意图

    Figure  3.  Schematic diagram of the monitoring structure

    图  4  监测对象的结构形式

    Figure  4.  The structure form of monitoring object

    图  5  回廊结构中传感器位置

    Figure  5.  Locations of sensors in the corridor structure

    图  6  光栅光纤传感器记录的应变和温度监测数据

    Figure  6.  Strain and temperature data obtained by FBG

    图  7  基于Sobel算子的数据可视化

    Figure  7.  Data visualization based on the Sobel operator

    图  8  卷积神经网络结构示意图

    Figure  8.  Schematic diagram of the convolutional neural network

    图  9  CNN模型训练过程

    Figure  9.  Training process of the CNN model

    图  10  混淆矩阵

    Figure  10.  Confusion matrix

    图  11  回廊结构温度-应变监测曲线

    Figure  11.  Temperature-strain monitoring curve of corridor structure

    图  12  功率谱密度曲线

    Figure  12.  Power spectral density curve

    13  基于VMD方法的应变监测数据解耦

    13.  Strain monitoring data decoupling based on VMD

    图  14  基于Prophet算法的温度应变预测

    Figure  14.  Prediction of temperature strain based on Prophet

    图  15  Gumbel极值分布参数获取过程

    Figure  15.  Parameter acquisition process of Gumbel extreme value distribution

    图  16  监测数据提取位置

    Figure  16.  Locations of monitoring data

    表  1  应变监测数据失真模式

    Table  1.   Distortion modes of strain monitoring data

    编号异常模式现象图例
    1数据缺失曲线不连续,缺失部分显示为空白
    2数据重复曲线存在不随时间改变的直线
    3离群值曲线中存在不合常理的跳点
    下载: 导出CSV

    表  2  各月份决定系数R2

    Table  2.   Coefficient of determination R2 of each month

    月份123456
    R20.850.870.860.870.890.84
    月份789101112
    R20.760.750.770.870.880.89
    下载: 导出CSV

    表  3  预测结果与阈值对比

    Table  3.   Comparison between prediction results and threshold

    位置温度应变
    预测峰值/με
    人群应变
    预测值/με
    初始应
    变/με
    叠加应
    变/με
    规范阈
    值/με
    B1056.35罕遇:31.762.2590.36171.81
    多遇:6.9965.59
    B1150.23罕遇:33.530.3384.09171.81
    多遇:7.1957.75
    B2361.35罕遇:38.627.36107.33171.81
    多遇:7.2175.92
    B2467.66罕遇:35.112.71105.48171.81
    多遇:8.1378.51
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-08-15
  • 修回日期:  2022-02-18
  • 网络出版日期:  2022-03-06
  • 刊出日期:  2023-02-01

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