有限实验数据下工程结构概率建模及随机响应分析

张瑞景; 戴鸿哲

doi:10.11990/jheu.202305023

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有限实验数据下工程结构概率建模及随机响应分析

更新时间：2025-09-01

- 有限实验数据下工程结构概率建模及随机响应分析
- Probabilistic modeling and stochastic response analysis of engineering structures with experimental data
- 哈尔滨工程大学学报 2024年45卷第4期页码：674-681
- 作者机构：
  
  哈尔滨工业大学土木工程学院, 黑龙江哈尔滨 150090
- 作者简介：
  
  [ "张瑞景, 男, 博士研究生" ]
  [ "戴鸿哲, 男, 教授, 博士生导师" ]
- 基金信息：
  
  国家自然科学基金项目(12272109)
- DOI：10.11990/jheu.202305023
  中图分类号： O324
- 收稿：2023-05-11，
  
  网络首发：2024-03-05，
  
  纸质出版：2024-04-05
- 稿件说明：
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张瑞景, 戴鸿哲. 有限实验数据下工程结构概率建模及随机响应分析[J]. 哈尔滨工程大学学报, 2024,45(4):674-681.

Ruijing ZHANG, Hongzhe DAI. Probabilistic modeling and stochastic response analysis of engineering structures with experimental data[J]. Journal of Harbin Engineering University, 2024, 45(4): 674-681.
张瑞景, 戴鸿哲. 有限实验数据下工程结构概率建模及随机响应分析[J]. 哈尔滨工程大学学报, 2024,45(4):674-681. DOI： 10.11990/jheu.202305023.

Ruijing ZHANG, Hongzhe DAI. Probabilistic modeling and stochastic response analysis of engineering structures with experimental data[J]. Journal of Harbin Engineering University, 2024, 45(4): 674-681. DOI： 10.11990/jheu.202305023.

摘要

为了研究实验数据下工程结构输入参数的概率建模以及依据所建模型的响应分析的问题

本文提出了一种实验数据下工程结构概率建模及随机响应求解的方法。依据Karhunen-Loeve展开和任意多项式混沌(aPC)展开理论

提出了一种基于核密度估计的新型随机模型; 发展了结构随机响应的求解技术

并提出了用于随机响应aPC系数估计的D-optimal加权插值方法

实现了结构响应的高精度求解。研究表明: 本文的研究为实验数据下工程结构的合理随机建模和高效响应分析提供了一个有效的框架

本文方法有效。

Abstract

In our quest to understand the probabilistic modeling of engineering structure inputs from experimental data and the associated response analysis of the constructed model

we have developed a new method that involves the construction of a nonGaussian random model using experimental data

followed by a random response analysis. First

we introduced an innovative random model that utilizes both Karhunen-Loeve expansion and arbitrary polynomial chaos (aPC) expansion. We then expanded on this by developing a technique for solving structural random responses based on this stochastic model. Ultimately

we developed the D-optimal weighted interpolation method for estimating the aPC coefficient of the random response

resulting in a highly precise solution for the structural response. The research indicates that our proposed method offers a practical framework for creating accurate random models and conducting efficient response analyses of engineering systems. These systems

which rely on real-life experimental data

can greatly benefit from our approach.

关键词

Keywords

references

李杰. 工程结构可靠性分析原理[M]. 北京: 科学出版社, 2021.

LI Jie, Fundamental of structural reliability analysis[M], Beijing: Scientific Press, 2021.

GHANEM R G, SPANOS P D. Stochastic finite elements: a spectral approach[M]. New York: Springer New York, 1991.

LI Jie, CHEN Jianbing. The principle of preservation of probability and the generalized density evolution equation[J]. Structural safety, 2008, 30(1): 65-77.

CHEN Guohai, YANG Dixiong. Direct probability integral method for stochastic response analysis of static and dynamic structural systems[J]. Computer methods in applied mechanics and engineering, 2019, 357: 112612.

CHEN Jianbing, KONG Fan, PENG Yongbo. A stochastic harmonic function representation for non-stationary stochastic processes[J]. Mechanical systems and signal processing, 2017, 96: 31-44.

DAI Hongzhe, ZHANG Ruijing, BEER M. A new perspective on the simulation of cross-correlated random fields[J]. Structural safety, 2022, 96: 102201.

ZHANG Ruijing, DAI Hongzhe. A non-Gaussian stochastic model from limited observations using polynomial chaos and fractional moments[J]. Reliability engineering&system safety, 2022, 221: 108323.

李建宇, 杨坤, 王博, 等. 小样本数据下圆柱薄壳初始缺陷不确定性量化的极大熵方法[J]. 力学学报, 2023, 55(4): 1028-1038.

LI Jianyu, YANG Kun, WANG Bo, et al. A maximum entropy approach for uncertainty quantification of initial geometric imperfections of thin-walled cylindrical shells with limited data[J]. Chinese journal of theoretical and applied mechanics, 2023, 55(4): 1028-1038.

ZHANG Ruijing, DAI Hongzhe. Independent component analysis-based arbitrary polynomial chaos method for stochastic analysis of structures under limited observations[J]. Mechanical systems and signal processing, 2022, 173: 109026.

WANG Yu, ZHAO Tengyuan, PHOON K K. Statistical inference of random field auto-correlation structure from multiple sets of incomplete and sparse measurements using Bayesian compressive sampling-based bootstrapping[J]. Mechanical systems and signal processing, 2019, 124: 217-236.

PASPARAKIS G D, DOS SANTOS K R M, KOUGIOUMTZOGLOU I A, et al. Wind data extrapolation and stochastic field statistics estimation via compressive sampling and low rank matrix recovery methods[J]. Mechanical systems and signal processing, 2022, 162: 107975.

MONTOYA-NOGUERA S, ZHAO Tengyuan, HU Yue, et al. Simulation of non-stationary non-Gaussian random fields from sparse measurements using Bayesian compressive sampling and Karhunen-Loève expansion[J]. Structural safety, 2019, 79: 66-79.

GHANEM R G, DOOSTAN A. On the construction and analysis of stochastic models: characterization and propagation of the errors associated with limited data[J]. Journal of computational physics, 2006, 217(1): 63-81.

MEHREZ L, DOOSTAN A, MOENS D, et al. Stochastic identification of composite material properties from limited experimental databases, Part Ⅱ: uncertainty modelling[J]. Mechanical systems and signal processing, 2012, 27: 484-498.

DAS S, GHANEM R, SPALL J C. Asymptotic sampling distribution for polynomial chaos representation of data: a maximum entropy and fisher information approach[C]//Proceedings of the 45th IEEE Conference on Decision and Control. Piscataway, NJ: IEEE, 2006: 4139-4144.

DAS S, GHANEM R, FINETTE S. Polynomial chaos representation of spatio-temporal random fields from experimental measurements[J]. Journal of computational physics, 2009, 228(23): 8726-8751.

SOIZE C. Polynomial chaos expansion of a multimodal random vector[J]. ASA journal on uncertainty quantification, 2015, 3(1): 34-60.

SILVERMAN B W. Density estimation for statistics and data analysis[M]. New York: CRC Press, 2018.

XIU Dongbin, KARNIADAKIS G E. The Wiener: askey polynomial chaos for stochastic differential equations[J]. SIAM journal on scientific computing, 2002, 24(2): 619-644.

RAHMAN S. A polynomial chaos expansion in dependent random variables[J]. Journal of mathematical analysis and applications, 2018, 464(1): 749-775.

ZHANG Ruijing, DAI Hongzhe. Stochastic analysis of structures under limited observations using kernel density estimation and arbitrary polynomial chaos expansion[J]. Computer methods in applied mechanics and engineering, 2023, 403: 115689.

SOIZE C, GHANEM R. Physical systems with random uncertainties: chaos representations with arbitrary probability measure[J]. SIAM journal on scientific computing, 2004, 26(2): 395-410.

WITTEVEEN J A S, BIJL H. Modeling arbitrary uncertainties using gram-schmidt polynomial chaos[C]//Proceedings of the 44th AIAA Aerospace Sciences Meeting and Exhibit. Reston, Virigina: AIAA, 2006: AIAA2006-896.

DAI Hongzhe,ZHANG Ruijing, BEER M. A new method for stochastic analysis of structures under limited observations[J]. Mechanical systems and signal processing, 2023, 185: 109730.

BERVEILLER M, SUDRET B, LEMAIRE M. Stochastic finite element: a non intrusive approach by regression[J]. European journal of computational mechanics, 2006, 15(1/2/3): 81-92.

GUO Ling, NARAYAN A, YAN Liang, et al. Weighted approximate fekete points: sampling for least-squares polynomial approximation[J]. SIAM journal on scientific computing, 2018, 40(1): A366-A387.

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