Journal Article10.1007/S12065-021-00652-4
Inverse problem based multiobjective sunflower optimization for structural health monitoring of three-dimensional trusses
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TL;DR: In this paper, an inverse damage identification problem is formulated and solved in order to identify damages in large-scale lattice-type structures, and the direct problem is numerically formulated using finite element method considering a 72-bar truss where the modal response is obtained.
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Abstract: Truss-type structures are widely used in engineering, with several applications in different sectors such as construction, aeronautics/aerospace, telecommunications and energy fields. In all these situations they are generally large-scale structures, posing difficulties to take advantage of some direct inspection techniques to locate and identify structural damage. In case these inspections are not performed properly, the likelihood of occurrence of accidents will be very high. In this sense, structural health monitoring techniques based on the use of optimization algorithms appear as a promising and non-destructive methodology. In this study, an inverse damage identification problem is formulated and solved in order to identify damages in large-scale lattice-type structures. The direct problem is numerically formulated using finite element method considering a 72-bar truss where the modal response is obtained. A recent new metaheuristic SunFlower Optimization algorithm is used to solve the inverse damage problem formulated in terms of multiple damage sites and two independent objective functions (based on natural frequencies and mode shapes). Numerical results have shown that the inclusion of mode shapes in a multiobjective formulation improves the ability to accurately identify the damage in terms of its location and severity. The multi-object SFO algorithm showed results strictly superior to the NSGAII.
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Citations
Review of Machine-Learning Techniques Applied to Structural Health Monitoring Systems for Building and Bridge Structures
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Delamination identification in sandwich composite structures using machine learning techniques
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IoMT-based smart healthcare monitoring system using adaptive wavelet entropy deep feature fusion and improved RNN
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TL;DR: A new smart healthcare system with the help of IoT devices is suggested, where the proposed optimized RNN with an advanced feature set supersedes the aforementioned techniques.
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The influence of delamination parameters on the wavelet based damage index in CFRP structures
Guilherme Antônio Oliver,João Luiz Junho Pereira,Matheus Brendon Francisco,Guilherme Ferreira Gomes +3 more
TL;DR: In this paper , the authors performed a substantial statistical analysis on the effects of damage characteristics, such as position and severity, on a specific damage metric composed of coefficients obtained from the discrete wavelet transform, and the results of the analysis serve as the basis for developing more sophisticated and optimized damage-identifying methods in structural health monitoring.
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Multi‐objective sunflower optimization: A new hypercubic meta‐heuristic for constrained engineering problems
TL;DR: The Multi-objective Sunflower Optimization (MOSFO) as discussed by the authors is a meta-heuristic inspired by the phototropic life cycle of sunflowers around the sun.
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