A modified transmissibility indicator and Artificial Neural Network for damage identification and quantification in laminated composite structures

2020 ◽  
Vol 248 ◽  
pp. 112497 ◽  
Author(s):  
Roumaissa Zenzen ◽  
Samir Khatir ◽  
Idir Belaidi ◽  
Cuong Le Thanh ◽  
Magd Abdel Wahab
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Composite Structures ◽  
Damage Identification ◽  
Laminated Composite ◽  
Laminated Composite Structures ◽  
Artificial Neural ◽  
Identification And Quantification

Hierarchical development of training database for artificial neural network-based damage identification

2006 ◽  
Vol 76 (3) ◽  
pp. 224-233 ◽  
Author(s):  
Lin Ye ◽  
Zhongqing Su ◽  
Chunhui Yang ◽  
Zhihao He ◽  
Xiaoming Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Damage Identification ◽  
Artificial Neural

Identifying Damage of the Benchmark Structure by Using Artificial Neural Network Methods

2011 ◽  
Vol 219-220 ◽  
pp. 312-317 ◽  
Author(s):  
Bai Sheng Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Damage Identification ◽  
Damage Localization ◽  
Building Structures ◽  
Bp Network ◽  
Network Methods ◽  
Damage Extent ◽  
Artificial Neural ◽  
Set Up

This paper discusses the damage identification using artificial neural network methods for the benchmark problem set up by IASC-ASCE Task Group on Health Monitoring. A three-stage damage identification strategy for building structures is proposed. The BP network and PNN are employed for damage localization and BP network for damage extent identification. Four damage patterns (patterns i~iv) in Cases 1-6 are discussed. The comparison between BP network and PNN are carried out. The results show that PNN performs better than BP network in damage localization. The damage extent identification using BPN is successful even in Cases 2 and 5&6 in which the modeling error is quite large.

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