Identification of wind loads on super-tall buildings by Kalman filter

Zhi, Lunhai; Yu, Pan; Li, Qiusheng; Chen, Bin; Ming-xin, Fang

doi:10.1016/j.compstruc.2018.07.002

Cited by 27 publications

(9 citation statements)

References 24 publications

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“…Subsequently, the Kalman filterbased method was used to estimate the modal wind load of structures with tuned mass dampers under typhoon action [8]. Zhi et al [9][10][11] regarded the fluctuating wind load as zero-mean Gaussian white noise, and successfully identified the unknown fluctuating wind load of the structure by using continuous Kalman, discrete Kalman and Taylor polynomial expansion methods. It is a good method to assume the fluctuating wind load as zero-mean white noise or random walk process, but in practical engineering, it is difficult to distinguish the fluctuating wind response from the observed sensor response for wind load calculation.…”

Section: Introductionmentioning

confidence: 99%

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Identification of Wind Loads on Structures Based on Modal Kalman Filter with Unknown Inputs

2022

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Wind loads on structures are difficult to directly measure, so it is practical to identify structural wind loads based on the measurements of structural responses. However, this inversed problem is challenging compared with conventional load identification as wind loads are time-space coupled and spatially distributed dynamic loads on structures. An improved method is proposed for identifying wind loads on structures using only partial measurements of structural acceleration responses in this paper. First, the wind loads on a structure are decomposed by proper orthogonal decomposition as a series of time-space decoupled sub-distributed dynamic loads with independent basic spatial distribution functions and time history functions. Herein, structural modes are adopted as the basic spatial distribution functions and structural modes of discretized and continuous structural systems are investigated. Then, a history function of the decomposed wind load is identified in the modal domain based on modal Kalman filter with unknown inputs, which is proposed by the authors. Finally, the distributed wind loads are reconstructed for discrete or continuous structural systems. The feasibility of the proposed algorithm is verified by two numerical examples of identification of wind loads on a discrete shear frame and a wind turbine tower, respectively.

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Section: Introductionmentioning

confidence: 99%

“…Currently, in the study of identifying wind loads, some studies [9][10][11] assume that the wind load was zero mean white noise or random walk process, and the influence of the average wind on the structure was ignored. Therefore, it is necessary to identify both fluctuating and mean winds.…”

Section: Introductionmentioning

confidence: 99%

Identification of Wind Loads on Structures Based on Modal Kalman Filter with Unknown Inputs

2022

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“…Load identification methods can be roughly divided into three categories: time domain, frequency domain, and modern intelligence [6][7][8]. Time domain method includes series expansion method, Kalman filter method, and inverse system method [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Single Pick Cutting Rock Load Identification Based on Improved Regularization Method

Li¹,

Zhai²,

Wang³

et al. 2021

Shock and Vibration

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To explore the relationship between the cutting vibration and the cutting load of a single pick, this paper studied a new method for a single pick cutting rock load identification. This paper improved the low accuracy problem of the regularization method in the inverse process of frequency response function in the traditional load identification method by introducing a filter operator. By combining the inverse pseudoexcitation method and the improved regularization method, the identification of the load dependent on the vibration signal was realized. A single pick cutting rock test equipment was built, which could simulate the actual working conditions of pick cutting rock in the underground or tunnel. By changing cutting speed, cutting angle, cutting spacing, and cutting depth of the single pick, the change trends of real cutting load and identification load were obtained. The load identification method proposed in this paper was consistent with the change trend of the real load under the single pick cutting state. Therefore, the method had good recognition accuracy and the maximum load recognition error was 17.35%. Compared with other traditional load identification methods, the identification error was reduced by a maximum of 1.98%. This method can identify the cutting load of single pick and modify the morbidity problem of frequency response function matrix. The method has a better recognition effect on the cutting load of the pick than the traditional recognition methods. The research could benefit the design of the cutting system and the arrangement of the pick on the coal mine or tunneling machinery.

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“…Dynamic analyses are commonly suggested in tall and slender structures subjected to vibrations induced by wind loads, 12–15 such as in the Taipei 101 Tower skyscraper, where there are natural events such as earthquakes and hurricanes. 16 To analyze the inelastic responses of wind-excited tall buildings, researchers 17,18 perform a comparative analysis for the differences between elastic and inelastic responses through two statistical linearization approaches.…”

Section: Introductionmentioning

confidence: 99%

Enriched finite element modeling in the dynamic analysis of plane frame subject to random loads

Hsu

Deitos

2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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This work contributes to the generalized finite element approach in free vibration, dynamic elastic, and elastoplastic analysis of plane frame subjected to random excitation generated by the wind action. The wind velocity is modeled mathematically by using power spectral density method in combination with Shinozuka’s model, along with the commonly employed wind spectra. From these spectra, the dynamic wind loading is determined from the sum of the mean and floating wind velocities. The governing equation is formulated by Euler–Bernoulli beam theory, and it is discretized by using the enriched beam element. The enrichment is done by employing enriched finite element shape function to construct the enriched mathematical space. This strategy is constituted by the enrichment space, which is constructed by trigonometric functions, and the conventional space, which is constructed by conventional two-node Lagrange–Hermite shape function. The time increment procedure is carried out by Hilber-Hughes-Taylor algorithm and the material nonlinearity is modeled by von Mises isotropic hardening model, solved by the Newton–Raphson algorithm. A flowchart is presented to summarize the proposed numerical modeling procedure. Finally, several applications are presented, and the results obtained by the generalized finite element method are compared with those obtained by conventional beam element. Natural frequencies are determined in a one-story plane frame and are compared with reference results. The relative error in displacement is determined in h-refine strategy for quadratic beam element (FEM3), while the generalized finite element method adopts the enrichment increment strategy. The results demonstrate the competitiveness and numerical stability of generalized finite element method in this type of application. Even in comparison to the quadratic beam element, the generalized finite element method presents good performance and accuracy in numerical modeling.

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Identification of wind loads on super-tall buildings by Kalman filter

Cited by 27 publications

References 24 publications

Identification of Wind Loads on Structures Based on Modal Kalman Filter with Unknown Inputs

Identification of Wind Loads on Structures Based on Modal Kalman Filter with Unknown Inputs

Single Pick Cutting Rock Load Identification Based on Improved Regularization Method

Enriched finite element modeling in the dynamic analysis of plane frame subject to random loads

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