Asset Details
Do you wish to reserve the book?
Damage detection in anisotropic-laminated composite beams based on incomplete modal data and teaching–learning-based optimization
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Damage detection in anisotropic-laminated composite beams based on incomplete modal data and teaching–learning-based optimization
Do you wish to request the book?
Damage detection in anisotropic-laminated composite beams based on incomplete modal data and teaching–learning-based optimization
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Damage detection in anisotropic-laminated composite beams based on incomplete modal data and teaching–learning-based optimization
2022
Overview
This study presents an efficient approach for the detection of damages in laminated composite beams with arbitrary lay-up. The approach uses the finite element model updating based on limited vibration data and a metaheuristic optimization algorithm. To this aim, a thirteen degrees-of-freedom (DOFs) beam finite element (FE) model is employed for numerical simulation of the actual structure. The Guyan condensation method is employed for model-order reduction to simulate the limited number of sensor data. The damage detection problem is defined as an unconstrained optimization problem. The objective function to be minimized is formulated using the objective function constructed as a weighted linear combination of the root-mean-square error in the frequencies and the error in the correlation between two mode shapes, which is represented by Modal Assurance Criterion (MAC). Teaching–Learning-Based Optimization (TLBO) is used as a metaheuristic tool for optimization. The proposed method is verified by four examples. A parametric study on anisotropic-laminated composite beams with cantilevered and clamped end conditions under three assumed damage scenarios is conducted to show the efficacy of the proposed method. The results indicate that the proposed method can identify single and multiple damages in anisotropic-laminated composite beams with adequate precision and outperforms the other algorithms in terms of accuracy and computational cost.
Publisher
Springer Berlin Heidelberg,Springer Nature B.V
