Combinatorial Reliability-Based Optimization of Nonlinear Finite Element Model Using an Artificial Neural Network-Based Approximation

2020 ◽  
pp. 359-371
Author(s):  
Ondřej Slowik ◽  
David Lehký ◽  
Drahomír Novák
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Finite Element Model ◽  
Element Model ◽  
Nonlinear Finite Element ◽  
Artificial Neural ◽  
Reliability Based Optimization ◽  
Nonlinear Finite Element Model

Damage Assessment in Structures Using Incomplete Modal Data and Artificial Neural Network

2015 ◽  
Vol 15 (06) ◽  
pp. 1450087 ◽  
Author(s):  
Seyed Sina Kourehli
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Finite Element Model ◽  
Structural Damage ◽  
Element Model ◽  
Mass System ◽  
Modal Data ◽  
Artificial Neural ◽  
Propagation Network

This paper presents a novel approach for structural damage detection and estimation using incomplete noisy modal data and artificial neural network (ANN). A feed-forward back propagation network is proposed for estimating the structural damage location and severity. Incomplete modal data is used in the dynamic analysis of damaged structures by the condensed finite element model and as input parameters to the neural network for damage identification. In all cases, the first two natural modes were used for the training process. The present method is applied to three examples consisting of a simply supported beam, three-story plane frame, and spring-mass system. Also, the effect of the discrepancy in mass and stiffness between the finite element model and the actual tested dynamic system has been investigated. The results demonstrated the accuracy and efficiency of the proposed method using incomplete modal data, which may be noisy or noise-free.

Statistical Vibration Based Damage Identification Using Artificial Neural Network

2012 ◽  
Author(s):  
Norhisham Bakhary
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Monte Carlo ◽  
Finite Element Model ◽  
Element Model ◽  
Point Estimation ◽  
Structural Vibration ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Artificial Neural Network (ANN) telah digunakan dengan meluas bagi tujuan mengesan kerosakan dalam struktur menggunakan data–data mod dari gegaran. Walau bagaimanapun, ketidakpastian yang wujud dalam model unsur terhingga dan data dari lapangan yang tidak dapat dielakkan boleh menyebabkan kesilapan dalam meramalkan magnitud dan lokasi kerosakan. Dalam kajian ini kaedah statistik digunakan untuk mengambil kira ketidakpastian ini. ANN digunakan untuk meramalkan parameter–parameter kekukuhan dari frekuensi dan mod bentuk bagi sesebuah struktur. Untuk mengambil kira ketidakpastian dalam ramalan, kaedah statistik digunakan di mana kaedah Rossenblueth point estimation diperbandingkan dengan kaedah Monte Carlo diaplikasikan bagi mengambil kira ketidakpastian ini. Keputusan menunjukkan bahawa dengan mengambil kira ketidakpastian dalam membuat ramalan menggunakan ANN, kerosakan boleh diramalkan pada tahap keyakinan yang tinggi. Kata kunci: Artificial neural network; ketidakpastian; kesilapan rawak Artificial Neural Network (ANN) has been widely applied to detect damages in structures based on structural vibration modal parameters. However, uncertainties that inevitably exist in finite element model and measured vibration data might lead to false or unreliable prediction of structural damage. In this study, a statistical approach is proposed to include the effect of uncertainties in the ANN algorithm for damage prediction. ANN is used to predict the stiffness parameters of structures from measured structural vibration frequencies and mode shapes. Uncertainties in the measured data and finite element model of the structure are considered in the prediction. The statistics of the identified parameters are determined using Rossenblueth’s point estimation method and verified by Monte Carlo simulation. The results show that by considering these uncertainties in the ANN model, the damages can be detected with a higher confidence level. Key words: Artificial neural network; uncertainties; random error

The Flattening Behaviour of Angle Section Beams Subjected to Pure Bending

2014 ◽  
Vol 501-504 ◽  
pp. 2479-2483
Author(s):  
Wei Bin Yuan ◽  
Chang Yi Chen
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Analytical Solutions ◽  
Element Model ◽  
Nonlinear Finite Element ◽  
Experimental Results ◽  
Energy Methods ◽  
Pure Bending ◽  
Angle Section ◽  
Nonlinear Finite Element Model

The flattening behaviour of angle section beams subjected to pure bending is studied in this paper. Analytical solutions for static instabilities of angle section beams subjected to pure bending about its weak axis are derived using energy methods. Nonlinear finite element model using the code ANSYS is developed to simulate nonlinear snap-through instability of angle section beams under pure bending. The optimization assumption about flattening shape of the leg is proposed, through comparison of between the present solutions, experimental results, and the finite element results.

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