scholarly journals Time-Varying Wind Load Identification Based on Minimum-Variance Unbiased Estimation

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Huili Xue ◽  
Kun Lin ◽  
Yin Luo ◽  
Hongjun Liu
Keyword(s):  
Damping Ratio ◽  
Estimation Method ◽  
Minimum Variance ◽  
Wind Load ◽  
Ar Model ◽  
Unbiased Estimation ◽  
Recursive Identification ◽  
Time Varying ◽  
Building Structures ◽  
Shear Building

A minimum-variance unbiased estimation method is developed to identify the time-varying wind load from measured responses. The formula derivation of recursive identification equations is obtained in state space. The new approach can simultaneously estimate the entire wind load and the unknown structural responses only with limited measurement of structural acceleration response. The fluctuating wind speed process is investigated by the autoregressive (AR) model method in time series analysis. The accuracy and feasibility of the inverse approach are numerically investigated by identifying the wind load on a twenty-story shear building structure. The influences of the number and location of accelerometers are examined and discussed. In order to study the stability of the proposed method, the effects of the errors in crucial factors such as natural frequency and damping ratio are discussed through detailed parametric analysis. It can be found from the identification results that the proposed method can identify the wind load from limited measurement of acceleration responses with good accuracy and stability, indicating that it is an effective approach for estimating wind load on building structures.

scholarly journals Wind Load and Structural Parameters Estimation from Incomplete Measurements

2019 ◽  
Vol 2019 ◽  
pp. 1-20
Author(s):  
Huili Xue ◽  
Hongjun Liu ◽  
Huayi Peng ◽  
Yin Luo ◽  
Kun Lin
Keyword(s):  
Structural State ◽  
Structural Parameters ◽  
State Parameter ◽  
Minimum Variance ◽  
Wind Load ◽  
Parameters Estimation ◽  
Unbiased Estimation ◽  
Building Structure ◽  
Input Estimation ◽  
Shear Building

The extended minimum variance unbiased estimation approach can be used for joint state/parameter/input estimation based on the measured structural responses. However, it is necessary to measure the structural displacement and acceleration responses at each story for the simultaneous identification of structural parameters and unknown wind load. A novel method of identifying structural state, parameters, and unknown wind load from incomplete measurements is proposed. The estimation is performed in a modal extended minimum variance unbiased manner, based on incomplete measurements of wind-induced structural displacement and acceleration responses. The feasibility and accuracy of the proposed method are numerically validated by identifying the wind load and structural parameters on a ten-story shear building structure with incomplete measurements. The effects of crucial factors, including sampling duration and the number of measurements, are discussed. Furthermore, the practical application of the developed inverse method is evaluated based on wind tunnel testing results of a 234 m tall building structure. The results indicate that the structural state, parameters, and unknown wind load can be identified accurately using the proposed approach.

scholarly journals Bayes Estimator as a Function of Some Classical Estimator

2010 ◽  
Vol 16 (58) ◽  
Author(s):  
Mohammed S. Abd-alrazak Mohammed S. Abd-alrazak ◽  
S. H. A, Al-Jasim S. H. A, Al-Jasim
Keyword(s):  
Parameter Estimation ◽  
Prior Distribution ◽  
Pareto Distribution ◽  
Minimum Mean Square Error ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Random Variable ◽  
Minimum Variance ◽  
Bayes Estimator ◽  
Unbiased Estimation

Maximum likelihood estimation method, uniformly minimum variance unbiased estimation method and minimum mean square error estimation, as classical estimation procedures, are frequently used for parameter estimation in statistics, which assuming the parameter is constant , while Bayes method assuming the parameter is random variable and hence the Bayes estimator is an estimator which minimize the Bayes risk for each value the random observable and for square error lose function the Bayes estimator is the posterior mean. It is well known that the Bayesian estimation is hardly used as a parameter estimation technique due to some difficulties to finding a prior distribution. The interest of this paper is that whether above classical estimators of the parameter for a particular probability distribution can be obtained from Bayes estimator is determined. In this analysis one-parameter Pareto distribution is used to examine the relationship between Bayesian and classical estimators. Considering improper prior distribution for shape parameter of the Pareto distribution of the first kind with known scale parameter which equals one, we have tried to show how the classical estimators can be obtain from Bayes estimator for various choices of hyper parameters of the prior function.    

scholarly journals Output-Only Damage Detection of Shear Building Structures Using an Autoregressive Model-Enhanced Optimal Subpattern Assignment Metric

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2050
Author(s):  
Liu Mei ◽  
Huaguan Li ◽  
Yunlai Zhou ◽  
Dawang Li ◽  
Wujian Long ◽  
...  
Keyword(s):  
Dynamic Systems ◽  
Autoregressive Model ◽  
Structural Damage ◽  
Damping Ratio ◽  
Autoregressive Models ◽  
Mode Shapes ◽  
Modal Parameters ◽  
Building Structures ◽  
Output Only ◽  
Shear Building

This paper proposes a novel output-only structural damage indicator by incorporating the pole-based optimal subpattern assignment distance with autoregressive models to localize and relatively assess the severity of damages for sheared structures. Autoregressive models can model dynamic systems well, while their model poles can represent the state of the dynamic systems. Structural damage generally causes changes in the dynamic characteristics (especially the natural frequency, mode shapes and damping ratio) of structures. Since the poles of the autoregressive models can solve the modal parameters of the structure, the poles have a close relationship with the modal parameters so that the changes in the poles of its autoregressive model reflect structural damages. Therefore, we can identify the damage by tracking the shifts in the dynamic system poles. The optimal subpattern assignment distance, which is the performance evaluator in multi-target tracking algorithms to measure the metric between true and estimated tracks, enables the construction of damage sensitive indicator from system poles using the Hungarian algorithm. The proposed approach has been validated with a five-story shear-building using numerical simulations and experimental verifications, which are subjected to excitations of white noise, El Centro earthquake and sinusoidal wave with frequencies sweeping, respectively; the results indicate that this approach can localize and quantify structural damages effectively in an output-only and data-driven way.

scholarly journals An Effective Adaptive Combination Strategy for Distributed Learning Network

2021 ◽  
Vol 11 (12) ◽  
pp. 5723
Author(s):  
Chundong Xu ◽  
Qinglin Li ◽  
Dongwen Ying
Keyword(s):  
Minimum Variance ◽  
Iteration Scheme ◽  
Estimation Problem ◽  
Lms Algorithm ◽  
Unbiased Estimation ◽  
Mean Square ◽  
Least Mean Square ◽  
Combination Strategy ◽  
Adaptive Combination ◽  
Diffusion Networks

In this paper, we develop a modified adaptive combination strategy for the distributed estimation problem over diffusion networks. We still consider the online adaptive combiners estimation problem from the perspective of minimum variance unbiased estimation. In contrast with the classic adaptive combination strategy which exploits orthogonal projection technology, we formulate a non-constrained mean-square deviation (MSD) cost function by introducing Lagrange multipliers. Based on the Karush–Kuhn–Tucker (KKT) conditions, we derive the fixed-point iteration scheme of adaptive combiners. Illustrative simulations validate the improved transient and steady-state performance of the diffusion least-mean-square LMS algorithm incorporated with the proposed adaptive combination strategy.

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