Techno Press
Techno Press

Smart Structures and Systems   Volume 18, Number 6, December 2016, pages 1233-1250
DOI: http://dx.doi.org/10.12989/sss.2016.18.6.1233
 
Damage detection of plate-like structures using intelligent surrogate model
Peyman Torkzadeh, Hamed Fathnejat and Ramin Ghiasi

 
Abstract     [Full Text]
    Cracks in plate-like structures are some of the main reasons for destruction of the entire structure. In this study, a novel two-stage methodology is proposed for damage detection of flexural plates using an optimized artificial neural network. In the first stage, location of damages in plates is investigated using curvature-moment and curvature-moment derivative concepts. After detecting the damaged areas, the equations for damage severity detection are solved via Bat Algorithm (BA). In the second stage, in order to efficiently reduce the computational cost of model updating during the optimization process of damage severity detection, multiple damage location assurance criterion index based on the frequency change vector of structures are evaluated using properly trained cascade feed-forward neural network (CFNN) as a surrogate model. In order to achieve the most generalized neural network as a surrogate model, its structure is optimized using binary version of BA. To validate this proposed solution method, two examples are presented. The results indicate that after determining the damage location based on curvature-moment derivative concept, the proposed solution method for damage severity detection leads to significant reduction of computational time compared with direct finite element method. Furthermore, integrating BA with the efficient approximation mechanism of finite element model, maintains the acceptable accuracy of damage severity detection.
 
Key Words
    damage detection; flexural plate structure; bat algorithm; curvature-moment derivative; optimized cascade feed-forward neural network
 
Address
Peyman Torkzadeh and Ramin Ghiasi: Department of Civil Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
Hamed Fathnejat: Department of Civil Engineering, Graduate University of Advanced Technology, Kerman, Iran
 

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