Application of structural damage diagnosis using density based outlier detection

Author(s):  
Rui Li ◽  
De Jie Yu
Author(s):  
Amir Mosavi

The loss of integrity and adverse effect on mechanical properties can be concluded as attributing miro/macro-mechanics damage in structures, especially in composite structures. Damage as a progressive degradation of material continuity in engineering predictions for any aspects of initiation and propagation requires to be identified by a trustworthy mechanism to guarantee the safety of structures. Besides the materials design, structural integrity and health are usually prone to be monitored clearly. One of the most powerful methods for the detection of damage is machine learning (ML). This paper presents the state of the art of ML methods and their applications in structural damage and prediction. Popular ML methods are identified and the performance and future trends are discussed.


2001 ◽  
Vol 23 (4) ◽  
pp. 281-295 ◽  
Author(s):  
Gongkang Fu ◽  
Adil G. Moosa

2005 ◽  
Vol 131 (10) ◽  
pp. 1036-1045 ◽  
Author(s):  
Tian-Wei Ma ◽  
Henry T. Yang ◽  
Chih-Chen Chang

2011 ◽  
Vol 7 (5) ◽  
pp. 353-367 ◽  
Author(s):  
Tao Liu ◽  
AiQun Li ◽  
YouLiang Ding ◽  
DaLiang Zhao ◽  
ZhiJun Li

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