A Statistical Model Updating Method of Beam Structures with Random Parameters under Static Load

Wu, Zhongchen; Huang, Bin; Li, Yejun; Pu, Wuchuan

doi:10.3390/app7060601

Cited by 6 publications

(6 citation statements)

References 31 publications

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“…From the process of the above derivation, it is found that no restriction is imposed on the probability distribution type of k 0i , k di , and α i . Usually, the probability distribution of structural parameters in the initial model is assumed to be symmetric, so that the damage index of a structural element at intact status can be regarded as a random variable with zero mean [47]. Then, its corresponding damage probability P i d is 0.5.…”

Section: Probability-based Damage Identificationmentioning

confidence: 99%

A Novel Stochastic Approach for Static Damage Identification of Beam Structures Using Homotopy Analysis Algorithm

Huang

Tee

et al. 2021

Sensors

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This paper proposes a new damage identification approach for beam structures with stochastic parameters based on uncertain static measurement data. This new approach considers not only the static measurement errors, but also the modelling error of the initial beam structures as random quantities, and can also address static damage identification problems with relatively large uncertainties. First, the stochastic damage identification equations with respect to the damage indexes were established. On this basis, a new homotopy analysis algorithm was used to solve the stochastic damage identification equations. During the process of solution, a static condensation technique and a L1 regularization method were employed to address the limited measurement data and ill-posed problems, respectively. Furthermore, the definition of damage probability index is presented to evaluate the possibility of existing damages. The results of two numerical examples show that the accuracy and efficiency of the proposed damage identification approach are good. In comparison to the first-order perturbation method, the proposed method can ensure better accuracy in damage identification with relatively large measurement errors and modelling error. Finally, according to the static tests of a simply supported concrete beam, the proposed method successfully identified the damage of the beam.

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Section: Probability-based Damage Identificationmentioning

confidence: 99%

A Novel Stochastic Approach for Static Damage Identification of Beam Structures Using Homotopy Analysis Algorithm

Huang

Tee

et al. 2021

Sensors

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“…However, the normal distributed-based unbiased estimation can always maintain an excellent estimation performance. (25) From the operation mentioned above, the interval bounds of experimental observations can be estimated, which will be more practical for the engineering system with limited experimental measurements compared to the traditional extremum interval method.…”

Section: Numerical Calculationmentioning

confidence: 99%

“…For the practical engineering system, the process of parameter identification is considered to be more necessary to quantify these uncertainties, which has been extensively researched [22][23][24][25]. These methods are mainly divided into two types: probabilistic and non-probabilistic.…”

Section: Introductionmentioning

confidence: 99%

Interval Identification of Thermal Parameters Using Trigonometric Series Surrogate Model and Unbiased Estimation Method

Wang

Zhao

2020

Applied Sciences

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Metal-foam materials have been applied in many engineering fields in virtue of its high specific strength and desirable of thermodynamic properties. However, due to the inherent uncertainty of its attribute parameters, reliable analysis results are often ambiguous to obtain accurately. To overcome this drawback, this paper proposes a novel interval parameter identification method. Firstly, a novel modelling methodology is proposed to simulate the geometry of engineering metal foams. Subsequently, the concept of intervals is introduced to represent the uncertainty relationship between variables and responses in heat transfer systems. To improve computational efficiency, a novel augmented trigonometric series surrogate model is constructed. Moreover, unbiased estimation methods based on different probability distributions are presented to describe system measurement intervals. Then, a multi-level optimization-based identification strategy is proposed to seek the parameter interval efficiently. Eventually, an engineering heat transfer system is given to verify the feasibility of the proposed parameter identification method. This method can rapidly identify the unknown parameters of the system. The identification results demonstrate that this interval parameter identification method can quantify the uncertainty of a metal-foam structure in engineering heat transfer systems efficiently, especially for the actual case without sufficient measurements.

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“…Several current studies have used the static behavior of the structure to update the FE model. For example, updating the beam model using random parameters under static stress [1], updating inaccurate parameters relying on the deformation of structures under static loads [2,3], and updating the FE model for the highway bridge by combining dynamic-static long-gauge strain responses [4]. In addition, some advanced FE models were proposed to deal with particular types of composite structures [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Finite Element Model Updating for Composite Plate Structures Using Particle Swarm Optimization Algorithm

et al. 2023

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In the Architecture, Engineering, and Construction (AEC) industry, particularly civil engineering, the Finite Element Method (FEM) is a widely applied method for computational designs. In this regard, computational simulation has increasingly become challenging due to uncertain parameters, significantly affecting structural analysis and evaluation results, especially for composite and complex structures. Therefore, determining the exact computational parameters is crucial since the structures involve many components with different material properties, even removing some additional components affects the calculation results. This study presents a solution to increase the accuracy of the finite element (FE) model using a swarm intelligence-based approach called the particle swarm optimization (PSO) algorithm. The FE model is created based on the structure’s easily observable characteristics, in which uncertainty parameters are assumed empirically and will be updated via PSO using dynamic experimental results. The results show that the finite element model achieves high accuracy, significantly improved after updating (shown by the evaluation parameters presented in the article). In this way, a precise and reliable model can be applied to reliability analysis and structural design optimization tasks. During this research project, the FE model considering the PSO algorithm was integrated into an actual bridge’s structural health monitoring (SHM) system, which was the premise for creating the initial digital twin model for the advanced digital twinning technology.

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A Statistical Model Updating Method of Beam Structures with Random Parameters under Static Load

Cited by 6 publications

References 31 publications

A Novel Stochastic Approach for Static Damage Identification of Beam Structures Using Homotopy Analysis Algorithm

A Novel Stochastic Approach for Static Damage Identification of Beam Structures Using Homotopy Analysis Algorithm

Interval Identification of Thermal Parameters Using Trigonometric Series Surrogate Model and Unbiased Estimation Method

Finite Element Model Updating for Composite Plate Structures Using Particle Swarm Optimization Algorithm

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