Uncertainty modelling in power spectrum estimation of environmental processes with applications in high rise building performance evaluation

环境过程功率谱估计的不确定性建模及其在高层建筑性能评估中的应用

基本信息

项目摘要

The overall aim of the project is to provide a general framework of algorithms for producing non-stationary spectral stochastic load models that utilize process record ensemble statistics to account for inherent uncertainties that exist in real data sets. Although the resulting models will be highly general, and hence widely applicable across different engineering fields, they will be considered in this project primarily in the context of super high-rise building dynamics problems. Specifically, the problem of extreme earthquake and wind loading, to which super high-rise buildings are particularly vulnerable, will be investigated.Nowadays, owing to the development of cheaper, more reliable data acquisition systems, vast amounts of environmental load process data are becoming accessible. As such data becomes ever more numerous, in many cases, when estimating power spectra, the need for assuming spectral models and fitting them to the data becomes unnecessary. This realization is further supported by the fact that many established spectral model assumptions, for various scientific fields are highly outdated.Particularly in the field of environmental stochastic load modelling, where this project is concentrated, when estimating any spectral model from multiple source records, the common ergodic assumption that each record, if it existed in the limit, conforms to the same power spectrum is highly improbable. Therefore, there is a need for a stochastic load representation framework that accounts for epistemic model uncertainties by encompassing inherent statistical differences that exist across real data sets. Only recently has it become possible that such uncertainties may be reliably quantified, due to the growing size and availability of source data.The initial project focus will be to define improved, robust estimation techniques for traditional spectral model determination by following a general treatment of record ensemble characteristics. This will yield immediate results that are directly applicable in scenarios where power spectra are estimated from process record ensembles. Following this, avenues for quantifying the uncertainty in the spectral model will be explored, ultimately resulting in more realistic process representation methods. Once formulated, a probability density evolution method will be employed for utilizing the new models in the context of induced wind and earthquake loading of high-rise buildings. This final proof-of-concept stage will validate the research, directly demonstrating its practicality in addressing real-world problems.Throughout the project, every attempt will be made to account for data sets that may be unevenly sampled, presenting difficulty for the majority of standard spectrum estimation methods. Although data sampling problems are not the primary focus of this work, developing methodologies that are robust in this setting will extend the value of the research and further justify its practicality.
该项目的总体目标是提供产生非平稳谱随机负荷模型的算法的一般框架,该模型利用过程记录集合统计来考虑真实数据集中存在的固有不确定性。虽然所得到的模型将是高度通用的,因此广泛适用于不同的工程领域,但在本项目中,它们将主要在超高层建筑动力学问题的背景下进行考虑。具体地说,将调查超高层建筑特别容易受到极端地震和风荷载影响的问题。如今,由于更便宜、更可靠的数据采集系统的发展,大量的环境负荷过程数据正在变得可用。随着这样的数据变得越来越多,在许多情况下,在估计功率谱时,假设谱模型并将其与数据进行拟合的需要变得不必要了。特别是在环境随机负荷建模领域,在该项目集中的环境随机负荷建模领域,当从多个源记录估计任何谱模型时,如果每条记录存在于极限内,则符合相同功率谱的常见遍历假设是非常不可能的。因此,需要一种随机负载表示框架,该框架通过包含跨真实数据集存在的固有统计差异来解释认知模型的不确定性。直到最近,由于源数据的大小和可用性的增加,这种不确定性才可能被可靠地量化。最初的项目重点将是通过对记录集合特征的一般处理来定义用于传统谱模型确定的改进的、稳健的估计技术。这将产生直接适用于从过程记录集合估计功率谱的场景的即时结果。在此之后,将探索量化光谱模型中的不确定性的途径,最终导致更现实的过程表示方法。一旦形成,将采用概率密度演化方法在高层建筑的诱导风和地震荷载的背景下使用新模型。这一最后的概念验证阶段将验证该研究,直接展示其在解决现实世界问题方面的实用性。通过该项目,将尽一切努力解决可能被不均匀采样的数据集,这给大多数标准谱估计方法带来了困难。虽然数据抽样问题不是这项工作的主要焦点,但开发在这种情况下稳健的方法论将扩大研究的价值,并进一步证明其实用性。

项目成果

期刊论文数量(8)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Relaxed power spectrum estimation from multiple data records utilising subjective probabilities
  • DOI:
    10.1016/j.ymssp.2021.108346
  • 发表时间:
    2022-02
  • 期刊:
  • 影响因子:
    8.4
  • 作者:
    Marco Behrendt;M. Bittner;Liam A. Comerford;M. Beer;Jianbing Chen
  • 通讯作者:
    Marco Behrendt;M. Bittner;Liam A. Comerford;M. Beer;Jianbing Chen
Stochastic Processes Identification from Data Ensembles via Power Spectrum Classification
  • DOI:
    10.22725/icasp13.407
  • 发表时间:
    2019-05
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Marco Behrendt;Liam A. Comerford;M. Beer
  • 通讯作者:
    Marco Behrendt;Liam A. Comerford;M. Beer
Reduction of random variables in the Stochastic Harmonic Function representation via spectrum-relative dependent random frequencies
通过频谱相关的相关随机频率减少随机调和函数表示中的随机变量
  • DOI:
    10.1016/j.ymssp.2020.106718
  • 发表时间:
    2020-07
  • 期刊:
  • 影响因子:
    8.4
  • 作者:
    Chen Jianbing;Comerford Liam;Peng Yongbo;Beer Michael;Li Jie
  • 通讯作者:
    Li Jie
Development of a Relaxed Stationary Power Spectrum using Imprecise Probabilities with Application to High-rise Buildings
RELAXED STATIONARY POWER SPECTRUM MODEL USING IMPRECISE PROBABILITIES
使用不精确概率的松弛稳态功率谱模型
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Professor Dr.-Ing. Michael Beer其他文献

Professor Dr.-Ing. Michael Beer的其他文献

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{{ truncateString('Professor Dr.-Ing. Michael Beer', 18)}}的其他基金

Efficient reliability analysis of complex systems
复杂系统的高效可靠性分析
  • 批准号:
    335796111
  • 财政年份:
    2017
  • 资助金额:
    --
  • 项目类别:
    Research Grants
Stichprobeninduzierte Simulationsverfahren zur fuzzy-probabilistischen Tragwerksanalyse und Sicherheitsbeurteilung
用于模糊概率结构分析和安全评估的采样诱导模拟方法
  • 批准号:
    5392182
  • 财政年份:
    2003
  • 资助金额:
    --
  • 项目类别:
    Research Grants
Experimentally-validated stochastic model for freeze-thaw microstructural degradation and damage of hardened cement paste
经过实验验证的硬化水泥浆体冻融微观结构退化和损坏的随机模型
  • 批准号:
    496491159
  • 财政年份:
  • 资助金额:
    --
  • 项目类别:
    Research Grants
Intelligent resilience analysis for infrastructure considering uncertain real-time data
考虑不确定实时数据的基础设施智能弹性分析
  • 批准号:
    501624329
  • 财政年份:
  • 资助金额:
    --
  • 项目类别:
    Priority Programmes

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