Nonlinear structural joint model updating based on instantaneous characteristics of dynamic responses

2016 ◽  
Vol 76-77 ◽  
pp. 476-496 ◽  
Author(s):  
Zuo-Cai Wang ◽  
Yu Xin ◽  
Wei-Xin Ren
Keyword(s):  
Model Updating ◽  
Joint Model ◽  
Dynamic Responses ◽  
Nonlinear Structural ◽  
Structural Joint

scholarly journals Nonlinear joint model updating using static responses

2016 ◽  
Vol 8 (12) ◽  
pp. 168781401668265 ◽  
Author(s):  
Ping-Ping Yuan ◽  
Zuo-Cai Wang ◽  
Wei-Xin Ren ◽  
Xia Yang
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Objective Function ◽  
Model Updating ◽  
Nonlinear Behavior ◽  
Element Model ◽  
Joint Model ◽  
Joint System ◽  
Structural Joint ◽  
Nonlinear Joint

Nonlinear behavior is often observed in structural joint system due to external loads. A new technique of nonlinear structural joint model updating with static load test results is proposed in this article to investigate the actual behavior of a joint system. To calibrate the nonlinear parameters of the structural joint system, an appropriate finite element model is first established to characterize the complex nonlinear behavior caused by the joint connections. Combined with the sensitivity analysis, the parameters that describe the nonlinear behavior of the joint connections are selected as the parameters to be updated. Subsequently, an objective function is created in accordance with the residual between experimentally measured static deflections and analytically calculated static deflections through finite element model. The objective function is then optimized to obtain the proper values of the nonlinear force–displacement parameters with the regular simulated annealing algorithm. To validate the efficiency of this updating approach, two numerical examples under static concentrated loads are conducted. The obtained results indicate that the nonlinear joint model parameters can be successfully updated, and the updated new model can further forecast the true deflections of the nonlinear structure with good accuracy and stability.

Separating the Contributions in Localization Methods: A Technique to Enhance Identification of Damping Related Parameters

1999 ◽  
Author(s):  
Hervé Algrain ◽  
Calogero Conti ◽  
Pierre Dehombreux
Keyword(s):  
Finite Element ◽  
Model Updating ◽  
Element Model ◽  
Dynamic Responses ◽  
Finite Element Model Updating ◽  
Global Localization ◽  
Localization Method ◽  
The Family ◽  
Family Based ◽  
The Stability

Abstract Finite Element Model Updating has for objective to increase the correlation between the experimental dynamic responses of a structure and the predictions from a model. Among different initial choices, these procedures need to establish a set of representative parameters to be updated in which some are in real error and some are not. It is therefore important to select the correct properties that have to be updated to ensure that no marginal corrections are introduced. In this paper the standard localization criteria are presented and a technique to separate the global localization criteria in family-based criteria for damped structures is introduced. The methods are analyzed and applied to both numerical and experimental examples; a clear enhancement of the results is noticed using the family-based criteria. A simple way to qualify the stability of a localization method to noise is presented.

scholarly journals Damage Diagnosis in 3D Structures Using a Novel Hybrid Multiobjective Optimization and FE Model Updating Framework

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Nizar Faisal Alkayem ◽  
Maosen Cao ◽  
Minvydas Ragulskis
Keyword(s):  
Structural Damage ◽  
Model Updating ◽  
Complex Problem ◽  
Dynamic Responses ◽  
Mode Shapes ◽  
Fe Model ◽  
Modal Strain Energy ◽  
3D Structures ◽  
Multiobjective Particle Swarm Optimization ◽  
Damage Tracking

Structural damage detection is a well-known engineering inverse problem in which the extracting of damage information from the dynamic responses of the structure is considered a complex problem. Within that area, the damage tracking in 3D structures is evaluated as a more complex and difficult task. Swarm intelligence and evolutionary algorithms (EAs) can be well adapted for solving the problem. For this purpose, a hybrid elitist-guided search combining a multiobjective particle swarm optimization (MOPSO), Lévy flights (LFs), and the technique for the order of preference by similarity to ideal solution (TOPSIS) is evolved in this work. Modal characteristics are employed to develop the objective function by considering two subobjectives, namely, modal strain energy (MSTE) and mode shape (MS) subobjectives. The proposed framework is tested using a well-known benchmark model. The overall strong performance of the suggested method is maintained even under noisy conditions and in the case of incomplete mode shapes.

Efficient joint model learning, segmentation and model updating for visual tracking

2022 ◽  
Author(s):  
Wei Han ◽  
Chamara Kasun Liyanaarachchi Lekamalage ◽  
Guang-Bin Huang
Keyword(s):  
Visual Tracking ◽  
Model Updating ◽  
Joint Model ◽  
Model Learning

Experimental study of kriging-based crack identification in plate structure

2016 ◽  
Vol 231 (17) ◽  
pp. 3118-3129 ◽  
Author(s):  
Haiyang Gao ◽  
Xiaofei Hu ◽  
Fang Han ◽  
Xinming Li ◽  
Jungang Zhang
Keyword(s):  
Surrogate Model ◽  
Crack Detection ◽  
Model Updating ◽  
Computational Cost ◽  
Measurement Noise ◽  
Dynamic Responses ◽  
Detection Methods ◽  
Crack Identification ◽  
Kriging Surrogate Model ◽  
Plate Structure

One of the major issues that existing crack identification methods utilizing dynamic responses are facing is the limitation of engineering feasibility. How to suppress the effect of measurement noise and improve the identification accuracy is still challenging. In this work, an effective method is proposed to identify the size of an arbitrary internal crack in plate structure based on a Kriging surrogate model, and a series of laboratory tests are designed to verify the practicability of this strategy. The initial Kriging surrogate model is constructed by samples of crack parameters (tip locations) and corresponding root mean square (RMS) of random responses as the inputs and outputs, respectively. To further improve the surrogate accuracy and reduce computational cost during the inverse problem, an optimal point-adding process for Kriging model updating is then carried out. Experimental results of crack identification in a cantilever plate indicate that the proposed method can be an alternative to conventional crack detection methods even in the presence of measurement noise and modeling errors.

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