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非高斯风压极值估计:基于无可行区限制的传递函数对比研究

吴凤波 郭增伟 刘敏 吴波 黄国庆

吴凤波, 郭增伟, 刘敏, 吴波, 黄国庆. 非高斯风压极值估计:基于无可行区限制的传递函数对比研究[J]. 工程力学, 2022, 39(9): 170-178, 203. doi: 10.6052/j.issn.1000-4750.2021.05.0386
引用本文: 吴凤波, 郭增伟, 刘敏, 吴波, 黄国庆. 非高斯风压极值估计:基于无可行区限制的传递函数对比研究[J]. 工程力学, 2022, 39(9): 170-178, 203. doi: 10.6052/j.issn.1000-4750.2021.05.0386
WU Feng-bo, GUO Zeng-wei, LIU Min, WU Bo, HUANG Guo-qing. A COMPARATIVE STUDY ON TRANSLATION FUNCTION WITH THE UNRESTRICTED APPLICATION REGION FOR EXTREME VALUE ESTIMATION OF NON-GAUSSIAN WIND PRESSURES[J]. Engineering Mechanics, 2022, 39(9): 170-178, 203. doi: 10.6052/j.issn.1000-4750.2021.05.0386
Citation: WU Feng-bo, GUO Zeng-wei, LIU Min, WU Bo, HUANG Guo-qing. A COMPARATIVE STUDY ON TRANSLATION FUNCTION WITH THE UNRESTRICTED APPLICATION REGION FOR EXTREME VALUE ESTIMATION OF NON-GAUSSIAN WIND PRESSURES[J]. Engineering Mechanics, 2022, 39(9): 170-178, 203. doi: 10.6052/j.issn.1000-4750.2021.05.0386

非高斯风压极值估计:基于无可行区限制的传递函数对比研究

doi: 10.6052/j.issn.1000-4750.2021.05.0386
基金项目: 国家自然科学基金项目(51878106,2021YFB2600601)
详细信息
    作者简介:

    吴凤波(1990−),男,四川人,讲师,博士,主要从事结构风工程研究(E-mail: fwu0923@outlook.com)

    刘 敏(1987−),男,江西人,讲师,博士,主要从事结构风工程研究(E-mail: liu.min@cqu.edu.cn)

    吴 波(1991−),男,四川人,博士,主要从事桥梁风工程研究(E-mail: wuswjtu@yeah.net)

    黄国庆(1976−),男,江苏人,教授,博士,博导,主要从事结构风工程研究(E-mail: ghuang1001@gmail.com)

    通讯作者:

    郭增伟(1985−),男,河南人,教授,博士,主要从事结构风工程研究(E-mail: zengweiguo@cqjtu.edu.cn)

  • 中图分类号: TU312+.1

A COMPARATIVE STUDY ON TRANSLATION FUNCTION WITH THE UNRESTRICTED APPLICATION REGION FOR EXTREME VALUE ESTIMATION OF NON-GAUSSIAN WIND PRESSURES

  • 摘要: 非高斯风压的极值估计对建筑围护结构抗风设计是极其重要的。由于简便性和无可行区限制,基于矩的piecewise HPM(PHPM)、Johnson转换模型(JTM)和piecewise JTM(PJTM)常用于非高斯风压极值估计。现阶段,PJTM对非高斯风压极值的估计效果还缺乏系统的研究,且对于三种无可行区限制模型的极值估计差别尚不明确。为探明三种模型的差别,从而提供一定的选择原则,该文系统对比了三种模型估计非高斯风压极值的精度。该文从理论上对比了三种模型的母本概率密度函数和传递函数;选用超长风洞试验风压数据对三种模型估计非高斯风压极值的精度进行了评估。结果表明:PHPM对非高斯风压(负偏度)极小值的估计精度比PJTM和JTM高,PHPM和PJTM对非高斯风压(负偏度)极大值的估计精度比JTM高。
  • 图  1  JTM的可行范围

    Figure  1.  Application range of JTM

    图  2  基于JTM、PJTM和PHPM的非高斯过程母本PDF

    Figure  2.  Parent PDF of the non-Gaussian process by JTM, PJTM and PHPM

    图  3  基于JTM、PJTM和PHPM的传递函数

    Figure  3.  Translation functions by JTM, PJTM and PHPM

    图  4  风洞试验中的鞍型屋盖及相应测点

    Figure  4.  Pressure measurements of a saddle roof model in wind tunnel

    图  5  测点56处的风压系数传递函数

    Figure  5.  The translation functions for the wind pressure coefficient recorded at Tap 56

    图  6  测点32处的风压系数传递函数

    Figure  6.  The translation functions for the wind pressure coefficient recorded at Tap 32

    图  7  测点46处的风压系数传递函数

    Figure  7.  The translation functions for the wind pressure coefficient recorded at Tap 46

    图  8  高斯风压峰值因子

    Figure  8.  Peak factor of the Gaussian wind pressure

    表  1  SU&SUSU&SBSB&SB三种情况的原始和新统计矩

    Table  1.   The original and new statistical moments for the cases SU&SUSU&SB and SB&SB

    案例原始统计矩新定义统计矩
    均值标准差偏度峰度尾部均值标准差偏度峰度
    SU&SU 0 1 −1.0 8.0 N 0.10 1.16 0 7.76
    P 0.10 0.82 0 4.75
    0 1 −1.0 13.0 N 0.07 1.23 0 11.17
    P 0.07 0.86 0 7.88
    SU&SB 0 1 −1.0 4.5 N 0.16 1.24 0 4.27
    P 0.16 0.72 0 2.17
    0 1 −1.0 5.5 P 0.13 1.20 0 5.39
    N 0.13 0.77 0 2.76
    SB&SB 0 1 −0.2 2.5 N 0.05 1.07 0 2.59
    P 0.05 0.93 0 2.31
    0 1 −0.2 2.8 N 0.04 1.06 0 2.93
    P 0.04 0.94 0 2.57
    注:N表示与极小值相关的统计矩;P表示与极大值相关的统计矩。
    下载: 导出CSV

    表  2  三种模型对测点56的极值估计结果

    Table  2.   Extreme value by three models for Tap 56

    模型高斯峰值
    因子PG
    非高斯峰值
    因子PNG (极大值)
    非高斯峰值
    因子PNG (极小值)
    观测值估计值
    (|误差|/(%))
    观测值估计值
    (|误差|/(%))
    JTM3.73.564.28 (20.2)−7.84−8.66 (10.5)
    PJTM3.73.563.63 (2.0)−7.84−8.74 (11.5)
    PHPM3.73.563.61 (1.4)−7.84−8.54 (8.9)
    下载: 导出CSV

    表  3  三种模型对测点32的极值估计结果

    Table  3.   Extreme values by three models for Tap 32

    模型高斯峰值
    因子PG
    非高斯峰值
    因子PNG (极大值)
    非高斯峰值
    因子PNG (极小值)
    观测值估计值(误差/(%))观测值估计值 (误差/(%))
    JTM 3.7 2.80 5.14 (83.6) −9.52 −9.56 (0.4)
    PJTM 3.7 2.80 2.68 (4.3) −9.52 −9.69 (1.8)
    PHPM 3.7 2.80 2.71 (3.2) −9.52 −9.49 (0.3)
    下载: 导出CSV

    表  4  三种模型对测点46的极值估计结果

    Table  4.   Extreme values by three models for Tap 46

    模型高斯峰值
    因子PG
    非高斯峰值
    因子PNG (极大值)
    非高斯峰值
    因子PNG (极小值)
    观测值估计值(误差/(%))观测值估计值 (误差/(%))
    JTM3.62.121.58 (25.5)−4.22−3.68 (12.8)
    PJTM3.62.121.63 (23.1)−4.22−4.06 (3.8)
    PHPM3.62.121.82 (14.2)−4.22−4.06 (3.8)
    下载: 导出CSV

    表  5  三种模型估计的峰值因子误差水平

    Table  5.   Error level of peak factor estimated by three models

    估计模型误差/(%)数量(极小)数量(极大)总数百分比/(%)
    JTM<545621467042.1
    5~1530538969443.7
    15~252068885.5
    >25141241388.7
    PJTM<546847794559.4
    5~1528829157936.4
    15~252318412.6
    >25169251.6
    PHPM<5540533107367.5
    5~1523325248530.5
    15~2599181.1
    >25131140.9
    下载: 导出CSV
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  • 收稿日期:  2021-05-25
  • 录用日期:  2021-12-02
  • 修回日期:  2021-10-09
  • 网络出版日期:  2021-12-02
  • 刊出日期:  2022-09-01

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