Cumulative power spectral density-based damping estimation

被引:1
|
作者
Kim, Wonsul [1 ]
Hwang, Jae-Seung [2 ]
Kwon, Dae-Kun [3 ]
Kareem, Ahsan [4 ]
机构
[1] Korea Author Land & Infrastruct Safety, Jinju Si, Gyeongsangnam D, South Korea
[2] Chonnam Natl Univ, Sch Architecture, Gwangju 61186, South Korea
[3] Univ Notre Dame, Ctr Res Comp CRC, Notre Dame, IN USA
[4] Univ Notre Dame, NatHaz Modeling Lab, Notre Dame, IN USA
来源
EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS | 2024年 / 53卷 / 05期
基金
新加坡国家研究基金会;
关键词
classically damped system; cumulative power spectral density; damping estimation; damping ratio; non-classically damped system; system identification; BLIND SOURCE SEPARATION; MODAL IDENTIFICATION; TALL BUILDINGS; FREQUENCY; MECHANISM; AMPLITUDE; DESIGN; SYSTEM;
D O I
10.1002/eqe.4092
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Damping is one of the critical factors in evaluating the performance of a structure under loads resulting from winds, waves, and earthquakes. Due to significant uncertainties associated with the damping mechanism and methods of its evaluation, its accurate estimation remains a challenging task. Therefore, many studies have focused on the development of damping estimation schemes. In this study, a simple yet effective scheme based on "Dynamics 101" for estimating damping in structures that draws upon attributes of the cumulative power spectral density (CPSD) function is proposed. The underlying principle is that the damping ratio can be estimated by comparing the computed CPSD of the modal response derived from measured data with the corresponding theoretical CPSD that is, defined by natural frequency and damping ratio. Unlike the jaggedness often observed in the power spectral density (PSD) that leads to uncertainty in damping estimates, the CPSD is characterized by its smoothness as it stems from the integration of the PSD. Theoretical characteristics of CPSD were utilized for extracting damping, and it was also extended to include non-classically damped systems. Using numerically simulated data and full-scale measurements, the validity and efficacy of the proposed scheme have been demonstrated. It is also demonstrated that this simplistic approach, requiring elementary knowledge of dynamics, yields results of the quality that match those of more advanced techniques. This offers an attractive scheme for practical applications with a promise of automation through a simple algorithm or a machine learning-based environment.
引用
收藏
页码:1787 / 1802
页数:16
相关论文
共 50 条
  • [1] Spectrum sensing based on cumulative power spectral density
    A. Nasser
    A. Mansour
    K. C. Yao
    H. Abdallah
    H. Charara
    EURASIP Journal on Advances in Signal Processing, 2017
  • [2] Spectrum sensing based on cumulative power spectral density
    Nasser, A.
    Mansour, A.
    Yao, K. C.
    Abdallah, H.
    Charara, H.
    EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING, 2017,
  • [3] MINIMUM DISTANCE DENSITY-BASED ESTIMATION
    CAO, R
    CUEVAS, A
    FRAIMAN, R
    COMPUTATIONAL STATISTICS & DATA ANALYSIS, 1995, 20 (06) : 611 - 631
  • [4] Quantifying fluctuations for dynamical power systems with stochastic excitations: A power spectral density-based method
    Qing, Xiangyun
    He, Wangli
    Zhou, Min
    Du, Wenli
    CHAOS, 2023, 33 (05)
  • [5] Fast density estimation for density-based clustering methods
    Cheng, Difei
    Xu, Ruihang
    Zhang, Bo
    Jin, Ruinan
    NEUROCOMPUTING, 2023, 532 : 170 - 182
  • [6] Mind the ground: A power spectral density-based estimator for all-terrain rovers
    Reina, Giulio
    Leanza, Antonio
    Milella, Annalisa
    Messina, Arcangelo
    MEASUREMENT, 2020, 151 (151)
  • [7] A Power Spectral Density-Based Method to Detect Tremor and Tremor Intermittency in Movement Disorders
    Luft, Frauke
    Sharifi, Sarvi
    Mugge, Winfred
    Schouten, Alfred C.
    Bour, Lo J.
    van Rootselaar, Anne-Fleur
    Veltink, Peter H.
    Heida, Tijtske
    SENSORS, 2019, 19 (19)
  • [8] A contact algorithm for density-based load estimation
    Bona, MA
    Martin, LD
    Fischer, KJ
    JOURNAL OF BIOMECHANICS, 2006, 39 (04) : 636 - 644
  • [9] A density-based similarity matrix construction for spectral clustering
    Beauchemin, Mario
    NEUROCOMPUTING, 2015, 151 : 835 - 844
  • [10] Spectral density-based statistical measures for image sharpness
    Zhang, NF
    Vladar, AE
    Postek, MT
    Larrabee, RD
    METROLOGIA, 2005, 42 (05) : 351 - 359