scholarly journals A Damage Prognosis Method of Girder Structures Based on Wavelet Neural Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Rumian Zhong ◽  
Zhouhong Zong ◽  
Jie Niu ◽  
Sujing Yuan
Keyword(s):  
Neural Networks ◽  
Health Monitoring ◽  
Model Updating ◽  
Element Model ◽  
Maximum Error ◽  
Beam Model ◽  
Wavelet Neural Networks ◽  
Damage Prognosis ◽  
Bridge Structures ◽  
Girder Bridge

Based on the basic theory of wavelet neural networks and finite element model updating method, a basic framework of damage prognosis method is proposed in this paper. Firstly, a damaged I-steel beam model testing is used to verify the feasibility and effectiveness of the proposed damage prognosis method. The results show that the predicted results of the damage prognosis method and the measured results are very well consistent, and the maximum error is less than 5%. Furthermore, Xinyihe Bridge in the Beijing-Shanghai Highway is selected as the engineering background, and the damage prognosis is conducted based on the data from the structural health monitoring system. The results show that the traffic volume will increase and seasonal differences will decrease in the next year and a half. The displacement has a slight increase and seasonal characters in the critical section of mid span, but the strain will increase distinctly. The analysis results indicate that the proposed method can be applied to the damage prognosis of girder bridge structures and has the potential for the bridge health monitoring and safety prognosis.

Damage quantification in foam core sandwich composites via finite element model updating and artificial neural networks

2020 ◽  
Vol 234 (21) ◽  
pp. 4288-4304
Author(s):  
Ali Mardanshahi ◽  
Masoud Mardanshahi ◽  
Ahmad Izadi
Keyword(s):  
Neural Networks ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Finite Element Model ◽  
Model Updating ◽  
Sandwich Structures ◽  
Element Model ◽  
Foam Core ◽  
Damage Quantification ◽  
Artificial Neural

The main idea of this paper is to propose a nondestructive evaluation (NDE) system for two types of damages, core cracking and skin/core debonding, in fiberglass/foam core sandwich structures based on the inverse eigensensitivity-based finite element model updating using the modal test results, and the artificial neural networks. First, the modal testing was conducted on the fabricated fiberglass/foam core sandwich specimens, in the intact and damaged states, and the natural frequencies were extracted. Finite element modeling and inverse eigensensitivity-based model updating of the intact and damaged sandwich structures were conducted and the parameters of the models were identified. Afterward, the updated finite element models were employed to generate a large dataset of the first five harmonic frequencies of the damaged sandwich structures with different damage sizes and locations. This dataset was adopted to train the machine learning models for detection, localization, and size estimation of the core cracking and skin/core debonding damages. A multilayer perceptron neural network classification model was used for detection of types of damages and also a multilayer perceptron neural network regression model was fitted to the dataset for automatically estimation of the locations and dimensions of damages. This intelligent system of damage quantification was also used to make predictions on real damaged specimens not seen by the system. The results indicated that the extracted natural frequencies from the accurate finite element model, in coordination with the experimental data, and using the artificial neural networks can provide an effective system for nondestructive evaluation of foam core sandwich structures.

Structural damage identification via physics-guided machine learning: a methodology integrating pattern recognition with finite element model updating

2020 ◽  
pp. 147592172092748 ◽  
Author(s):  
Zhiming Zhang ◽  
Chao Sun
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Finite Element ◽  
Finite Element Model ◽  
Health Monitoring ◽  
Damage Identification ◽  
Model Updating ◽  
Element Model ◽  
Data Driven ◽  
Finite Element Model Updating

Structural health monitoring methods are broadly classified into two categories: data-driven methods via statistical pattern recognition and physics-based methods through finite elementmodel updating. Data-driven structural health monitoring faces the challenge of data insufficiency that renders the learned model limited in identifying damage scenarios that are not contained in the training data. Model-based methods are susceptible to modeling error due to model idealizations and simplifications that make the finite element model updating results deviate from the truth. This study attempts to combine the merits of data-driven and physics-based structural health monitoring methods via physics-guided machine learning, expecting that the damage identification performance can be improved. Physics-guided machine learning uses observed feature data with correct labels as well as the physical model output of unlabeled instances. In this study, physics-guided machine learning is realized with a physics-guided neural network. The original modal-property based features are extended with the damage identification result of finite element model updating. A physics-based loss function is designed to evaluate the discrepancy between the neural network model output and that of finite element model updating. With the guidance from the scientific knowledge contained in finite element model updating, the learned neural network model has the potential to improve the generality and scientific consistency of the damage detection results. The proposed methodology is validated by a numerical case study on a steel pedestrian bridge model and an experimental study on a three-story building model.

Structural health monitoring and fatigue damage estimation using vibration measurements and finite element model updating

2018 ◽  
Vol 18 (4) ◽  
pp. 1189-1206 ◽  
Author(s):  
Dimitrios Giagopoulos ◽  
Alexandros Arailopoulos ◽  
Vasilis Dertimanis ◽  
Costas Papadimitriou ◽  
Eleni Chatzi ◽  
...  
Keyword(s):  
Finite Element ◽  
Structural Health Monitoring ◽  
Finite Element Model ◽  
Fatigue Damage ◽  
Health Monitoring ◽  
Model Updating ◽  
Element Model ◽  
Finite Element Model Updating ◽  
Damage Estimation ◽  
Vibration Measurements

Model Updating of a Steel Truss Based on Artificial Neural Networks

2011 ◽  
Vol 121-126 ◽  
pp. 1363-1366
Author(s):  
Shi Lei Zhang ◽  
Shao Feng Chen ◽  
Huan Ding Wang ◽  
Wei Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Model Updating ◽  
Back Propagation ◽  
Element Model ◽  
Steel Truss ◽  
Artificial Neural ◽  
The Finite Element Model

Based on the artificial neural network, the parameters of a steel truss are identified. And the finite element model of truss is corrected. In order to improve the efficiency of model updating by artificial neural networks, the momentum is introduced into the back propagation algorithm. Based on the theory of probability and mathematical statistics, the expectation confidence interval of the measured deflections and strains is obtained. In this way, the samples to train the neural network are optimized. The numerical results show that the back propagation neural network proposed on this paper is able to correct the finite element model of the truss effectively.

Finite Element Model Updating for Unsymmetrical Damping System with Genetic Algorithm

2012 ◽  
Vol 166-169 ◽  
pp. 2999-3003 ◽  
Author(s):  
Bao Qiang Zhang ◽  
Guo Ping Chen ◽  
Qin Tao Guo
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Finite Element Model ◽  
Model Updating ◽  
Element Model ◽  
Maximum Error ◽  
Optimization Method ◽  
Finite Element Model Updating ◽  
Modal Data ◽  
Complex Modal

Finite element model updating using incomplete complex modal data for unsymmetrical damping system with genetic algorithm is presented. The genetic algorithm method and finite element model updating based on optimization method using complex modal eigenvalue are introduced. The updating for simulation example about a flexible rotor system which is a typical unsymmetrical damping system is performed using bearing stiffness, bearing damping and diameter moment of inertia parameters. The results show that the maximum error of updated parameters is 0.15% and the objective function of genetic algorithm is 0.0081. The study demonstrates that the finite element model updating method using incomplete complex modal data with genetic algorithm is feasible and effective for unsymmetrical damping system.

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