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- 저자 : R. Capozucca (검색결과 2 건)
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Mechanical characterization of an epoxy panel reinforced by date palm petiole particle
A. Bendada,D. Boutchicha,S. Khatir,E. Magagnini,R. Capozucca,M. Abdel Wahab 국제구조공학회 2020 Steel and Composite Structures, An International J Vol.35 No.5
The past years were marked by an increase in the use of wood waste in civil and mechanical constructions. Date palm waste remains also one of the most solicited renewable and recyclable natural resources in the composition of composite materials. In Algeria, a great amount of this type of plant wastes accumulates every year. In order to make use of this waste, a new wood-epoxy composite material based on date palm petiole particleboard is developed. It makes use of date palm petiole particleboard as reinforcement and epoxy resin as matrix. The size of the particles reinforcement are between 1~3 mm and proportion of reinforcement used is 37%. In this work, experimental and numerical studies are conducted in order to characterize the wood fibre-epoxy plates. Firstly, experimental modal analysis test was carried out to determine Young’s modulus of the elaborated material. Then, in order to validate the results, compression test was conducted. Furthermore, additional information about the shear modulus of this material is obtained by performing an experimental modal analysis to extract the first torsional mode. Moreover, a finite element model is developed using ANSYS software to simulate the vibration behaviour of the plates. The results show a good agreement with the experimental modal analysis, which confirms the values of Young’s modulus and shear modulus.
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An efficient hybrid TLBO-PSO-ANN for fast damage identification in steel beam structures using IGA
S. Khatir,T. Khatir,D. Boutchicha,C. Le Thanh,H. Tran-Ngoc,T.Q. Bui,R. Capozucca,M. Abdel Wahab 국제구조공학회 2020 Smart Structures and Systems, An International Jou Vol.25 No.5
The existence of damages in structures causes changes in the physical properties by reducing the modal parameters. In this paper, we develop a two-stages approach based on normalized Modal Strain Energy Damage Indicator (<i>nMSEDI</i>) for quick applications to predict the location of damage. A two-dimensional IsoGeometric Analysis (2D-IGA), Machine Learning Algorithm (MLA) and optimization techniques are combined to create a new tool. In the first stage, we introduce a modified damage identification technique based on frequencies using <i>nMSEDI</i> to locate the potential of damaged elements. In the second stage, after eliminating the healthy elements, the damage index values from <i>nMSEDI</i> are considered as input in the damage quantification algorithm. The hybrid of Teaching-Learning-Based Optimization (TLBO) with Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) are used along with nMSEDI. The objective of TLBO is to estimate the parameters of PSO-ANN to find a good training based on actual damage and estimated damage. The IGA model is updated using experimental results based on stiffness and mass matrix using the difference between calculated and measured frequencies as objective function. The feasibility and efficiency of nMSEDI-PSO-ANN after finding the best parameters by TLBO are demonstrated through the comparison with nMSEDI-IGA for different scenarios. The result of the analyses indicates that the proposed approach can be used to determine correctly the severity of damage in beam structures.
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