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

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

吴凤波^1,,
郭增伟^1, ,,
刘敏^2,,
吴波^1,,
黄国庆^2,

1.
重庆交通大学省部共建山区桥梁及隧道工程国家重点实验室，重庆 400074
2.
重庆大学土木工程学院，重庆 400044

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
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- HTML全文浏览量: 88
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- 被引次数: 0
出版历程
- 收稿日期: 2021-05-25
- 修回日期: 2021-10-09
- 录用日期: 2021-12-02
- 网络出版日期: 2021-12-02
- 刊出日期: 2022-09-01

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

WU Feng-bo^1
,,
GUO Zeng-wei^{1
, ,},
LIU Min^2
,,
WU Bo^1
,,
HUANG Guo-qing^2
,

1.
State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, China
2.
School of Civil Engineering, Chongqing University, Chongqing 400044, China

摘要

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。
- 结构工程 /
- 结构风工程 /
- 非高斯风压 /
- 极值估计 /
- piecewise Hermite多项式模型 /
- Johnson转换模型 /
- piecewise Johnson转换模型
Abstract: The extreme value estimation for the non-Gaussian wind pressures is extremely important for the wind-resistant design of building envelope. With its simplicity and unrestricted application region, the moment-based piecewise HPM (PHPM), Johnson transformation model (JTM) and piecewise JTM (PJTM) are usually used to estimate the non-Gaussian wind pressure extremes. Currently, the systematic research on the performance of the PJTM in the non-Gaussian wind pressure extreme estimation is less addressed, and the differences in extreme values estimated by the three unrestricted application region models are unclear. To compare the differences of the three models and provide certain selection principles, this paper systematically compares the accuracy of the three models to estimate the non-Gaussian wind pressure extremes. Firstly, this paper compares the parent probability distribution functions (PDFs) and translation functions by the three models theoretically. Secondly, the very long wind pressure data from a wind tunnel test are used to evaluate the accuracy of the three models to estimate the extreme values of the non-Gaussian wind pressures. The results show that, for the minimum value estimation of the non-Gaussian wind pressure with negative skewness, the accuracy of PHPM is generally higher than that of JTM and PJTM, while for the maximum value estimation of the non-Gaussian wind pressure with negative skewness, the accuracy of PJTM and PHPM is generally higher than that of JTM.
- structural engineering /
- structural wind engineering /
- non-Gaussian wind pressure /
- extreme value estimation /
- piecewise Hermite polynomial model /
- Johnson transformation model /
- piecewise Johnson transformation model

HTML全文

图 1 JTM的可行范围

Figure 1. Application range of JTM

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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

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图 3 基于JTM、PJTM和PHPM的传递函数

Figure 3. Translation functions by JTM, PJTM and PHPM

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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

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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

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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

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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

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图 8 高斯风压峰值因子

Figure 8. Peak factor of the Gaussian wind pressure

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表 1 S_U&S_U、S_U&S_B和S_B&S_B三种情况的原始和新统计矩

Table 1. The original and new statistical moments for the cases S_U&S_U、S_U&S_B and S_B&S_B

案例	原始统计矩				新定义统计矩
案例	均值	标准差	偏度	峰度	尾部	均值	标准差	偏度	峰度
S_U&S_U	0	1	−1.0	8.0	N	0.10	1.16	0	7.76
	0	1	−1.0	8.0	P	0.10	0.82	0	4.75
	0	1	−1.0	13.0	N	0.07	1.23	0	11.17
	0	1	−1.0	13.0	P	0.07	0.86	0	7.88
S_U&S_B	0	1	−1.0	4.5	N	0.16	1.24	0	4.27
	0	1	−1.0	4.5	P	0.16	0.72	0	2.17
	0	1	−1.0	5.5	P	0.13	1.20	0	5.39
	0	1	−1.0	5.5	N	0.13	0.77	0	2.76
S_B&S_B	0	1	−0.2	2.5	N	0.05	1.07	0	2.59
	0	1	−0.2	2.5	P	0.05	0.93	0	2.31
	0	1	−0.2	2.8	N	0.04	1.06	0	2.93
	0	1	−0.2	2.8	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。

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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P_G	非高斯峰值因子P_NG (极大值)		非高斯峰值因子P_NG (极小值)
模型	高斯峰值因子P_G	观测值	估计值 (\|误差\|/(%))	观测值	估计值 (\|误差\|/(%))
JTM	3.7	3.56	4.28 (20.2)	−7.84	−8.66 (10.5)
PJTM	3.7	3.56	3.63 (2.0)	−7.84	−8.74 (11.5)
PHPM	3.7	3.56	3.61 (1.4)	−7.84	−8.54 (8.9)

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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P_G	非高斯峰值因子P_NG (极大值)		非高斯峰值因子P_NG (极小值)
模型	高斯峰值因子P_G	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)

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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P_G	非高斯峰值因子P_NG (极大值)		非高斯峰值因子P_NG (极小值)
模型	高斯峰值因子P_G	观测值	估计值(误差/(%))	观测值	估计值 (误差/(%))
JTM	3.6	2.12	1.58 (25.5)	−4.22	−3.68 (12.8)
PJTM	3.6	2.12	1.63 (23.1)	−4.22	−4.06 (3.8)
PHPM	3.6	2.12	1.82 (14.2)	−4.22	−4.06 (3.8)

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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	<5	456	214	670	42.1
	5~15	305	389	694	43.7
	15~25	20	68	88	5.5
	>25	14	124	138	8.7
PJTM	<5	468	477	945	59.4
	5~15	288	291	579	36.4
	15~25	23	18	41	2.6
	>25	16	9	25	1.6
PHPM	<5	540	533	1073	67.5
	5~15	233	252	485	30.5
	15~25	9	9	18	1.1
	>25	13	1	14	0.9

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