INFLUENCE OF MEASUREMENT ERRORS ON STRUCTURAL DAMAGE IDENTIFICATION USING ARTIFICIAL NEURAL NETWORKS

2000 ◽  
Vol 1 (3) ◽  
pp. 291
Author(s):  
Bai-sheng WANG
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Measurement Errors ◽  
Structural Damage ◽  
Damage Identification ◽  
Structural Damage Identification ◽  
Artificial Neural

Structural Damage Detection Using FRF Data, 2D-PCA, Artificial Neural Networks and Imperialist Competitive Algorithm Simultaneously

2017 ◽  
Vol 17 (07) ◽  
pp. 1750073 ◽  
Author(s):  
Faramarz Khoshnoudian ◽  
Saeid Talaei ◽  
Milad Fallahian
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Structural Damage ◽  
Damage Identification ◽  
Damage Index ◽  
Imperialist Competitive Algorithm ◽  
Damage Scenarios ◽  
Competitive Algorithm ◽  
Artificial Neural ◽  
Unknown Damage

In this study, a promising pattern recognition based approach is introduced for structural damage identification using the measured dynamic data. The frequency response function (FRF) is preferably employed as the input of the proposed algorithm since it contains the most information of structural dynamic characteristics. The 2D principal component analysis (2D-PCA) is used to reduce the large size of FRFs data. The output data generated by the 2D-PCA are used to extract the damage indexes for each of the damage scenarios. A dataset of all probable damage indexes is provided; of which 30% are selected to form the train dataset and to be compared with the unknown damage index for an unidentified state of the structure. The sum of absolute errors (SAE) are calculated between the unknown damage index and the selected indexes from the dataset; of which the minimum refers to the most similar damage condition to the unknown one. The artificial neural networks (ANNs) are used to form a smooth function of the SAEs and the imperialist competitive algorithm (ICA) is utilized to minimize this function in order to find the location and severity of the damages of the unknown state of the structure. To validate the proposed method, the damage identification of a truss bridge structure and a two-story frame structure is conducted by considering all the single damage cases as well as multi damage scenarios. In addition, the robustness of the proposed method to measurement noise up to 20% is thoroughly investigated.

Bridge Damage Identification Using Artificial Neural Networks

2018 ◽  
Vol 23 (11) ◽  
pp. 04018084 ◽  
Author(s):  
Jordan C. Weinstein ◽  
Masoud Sanayei ◽  
Brian R. Brenner
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Damage Identification ◽  
Bridge Damage ◽  
Artificial Neural

Artificial Neural Networks for Structural Health Monitoring of Helicopter Harsh Landings

2013 ◽  
Vol 390 ◽  
pp. 192-197
Author(s):  
Giorgio Vallone ◽  
Claudio Sbarufatti ◽  
Andrea Manes ◽  
Marco Giglio
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Numerical Models ◽  
Damage State ◽  
Operational Conditions ◽  
Structural Health ◽  
Artificial Neural

The aim of the current paper is to explore fuselage monitoring possibilities trough the usage of Artificial Neural Networks (ANNs), trained by the use of numerical models, during harsh landing events. A harsh landing condition is delimited between the usual operational conditions and a crash event. Helicopter structural damage due to harsh landings is generally less severe than damage caused by a crash but may lead to unscheduled maintenance events, involving costs and idle times. Structural Health Monitoring technologies, currently used in many application fields, aim at the continuous detection of damage that may arise, thereby improving safety and reducing maintenance idle times by the disposal of a ready diagnosis. A landing damage database can be obtained with relatively little effort by the usage of a numerical model. Simulated data are used to train various ANNs considering the landing parameter values as input. The influence of both the input and output noise on the system performances were taken into account. Obtained outputs are a general classification between damaged and undamaged conditions, based on a critical damage threshold, and the reconstruction of the fuselage damage state.

scholarly journals Prediction of seismic-induced structural damage using artificial neural networks

2009 ◽  
Vol 31 (2) ◽  
pp. 600-606 ◽  
Author(s):  
Oliver Richard de Lautour ◽  
Piotr Omenzetter
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Structural Damage ◽  
Artificial Neural
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