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Metaheuristic algorithms have known vast development in recent years. And their applicability in engineering projects is constantly growing; however, their deferent exploration and exploitation techniques cause the engineering problems to favor some algorithms over others. This paper studies damage identification in steel plates using Frequency Response Function (FRF) damage indicator to detect and localize the healthy and damaged structure. The study is formulated as an inverse analysis, investigating the performance of three new metaheuristic algorithms of Wild Horse Optimizer (WHO), Harris Hawks Optimization (HHO), and Arithmetic Optimization Algorithm (AOA). The objective function is based on measured and calculated FRF damage indicators. The results showed that the case of four damages with different damage severity levels presented a good challenge where the HWO algorithm was shown to have the best performance. Both in convergence speed and CPU time. [ABSTRACT FROM AUTHOR]

Highlights • Single and multiple cracks identification in plate structures. • Proper Orthogonal Decomposition (POD). • Radial Basis Functions (RBF). • eXtended IsoGeometric Analysis (XIGA). Abstract An inverse algorithm based on Proper Orthogonal Decomposition (POD) and Radial Basis Functions (RBF) for single and multiple cracks identification in plate structures is presented. The inverse analyses combine experimental fracture mechanics tests with numerical models based on the eXtended IsoGeometric Analysis (XIGA) method. The proposed technique identifies the location of cracks in a plate using static strains as input data. Simple and powerful algorithms, namely Jaya and Cuckoo search algorithms are coupled with the proposed XIGA-POD-RBF technique. The objective function is expressed as the difference between the strains of the actual cracks and those of the estimated ones. It is found that the proposed methodology is able to accurately estimate the cracks' locations with high accuracy and much faster than other techniques used in the literature. The stability of the identification algorithm based on Jaya is tested against measurement uncertainty by introducing a white Gaussian noise in the input data. The results show high stability for noise levels up to 4%. [ABSTRACT FROM AUTHOR]

In this paper, a new hybrid YUKI-ANN is implemented for Structural Health Monitoring (SHM) of laminated composite plates. A finite element model is constructed and used to identify damage at five randomly selected elements in a laminated composite plate. The process of damage identification is divided into two main steps, namely damage localization and quantification. During the localization step, damaged elements are identified using a damage indicator based on Modal Strain Energy change ratio (MSEcr). After excluding the healthy elements, the level of damage is predicted using ANN modified with four optimization algorithms: Arithmetic Optimizing Algorithm (AOA), Balancing Composite Motion Optimization (BCMO), Particle Swarm Optimization (PSO), and YUKI algorithm. The performance of these optimization algorithms is evaluated, and it is found that the YUKI algorithm (YA) outperforms AOA, BCMO, and PSO algorithms without exception. YA gives better predictions with lower errors when compared to PSO and BCMO algorithms with equivalent computational time. In some cases, AOA provides slightly better predictions than YA, but the computational time for these predictions is eight times more that of YA. If both performance and efficiency are considered, YA seems to be the best choice. Find the MATLAB code for YUKI-ANN at https://github.com/Brahim-Benaissa/YUKI_ANN. [ABSTRACT FROM AUTHOR]

This paper presents an approach of inverse damage detection and localization based on model reduction. The problem is formulated as an inverse problem where an optimization algorithm is used to minimize the cost function expressed as the normalized difference between a frequency vector of the tested structure and its numerical model. A finite element model of bi-dimensional monolithic composite beam reinforced by a graphite-epoxy is used to define a numerical model of the tested structure in which different scenarios of damage are considered by stiffness reduction. Then, calculations are made on a reduced model built by the technique of proper orthogonal decomposition coupled by radial basis functions. The accuracy of the method is verified through different damage configurations. The results show that the developed algorithm is a feasible methodology of predicting damage in short computing time and with high accuracy. The effect of noise on the accuracy of the results is investigated in some cases for the structure under consideration. [ABSTRACT FROM AUTHOR]

• CPU Time for XIGA and XFEM based on inverse problem. • Jaya and PSO for crack idetification. • XIGA is much better than XFEM. • The objective of NURBS order for best convergence and fast simulation is provided. In this paper, a creative and intelligent approach based on an inverse problem that accurately predicts crack location in plate structures is presented. The eXtended Finite Element (XFEM) and the eXtended IsoGeometric Analysis (XIGA) are combined with two optimization techniques, namely Particle Swarm Optimization (PSO) and Jaya algorithm to predict the crack location. The superiority of XIGA is demonstrated by using various NURBS orders to reduce the number of elements, provide fast simulation and achieve best convergence compared with XFEM. Four numerical-optimization techniques are considered in this paper, namely XFEM-Jaya, XIGA-Jaya, XFEM-PSO and XIGA-PSO. In the optimization techniques, the objective function minimizes the difference between the calculated and measured displacements and strains. Convergence studies for various positions of a crack and a hole in plates are performed and the results show that Jaya algorithm significantly performs more accurate and faster than PSO. In addition, the proposed techniques are validated using experimental data and another numerical-optimization technique, i.e. XFEM coupled with Genetic Algorithm (GA), presented in literature. The comparisons show that XIGA-Jaya performs the best of all considered techniques. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a Structural Health Monitoring (SHM) technique for damage identification in beam-like and truss structures using Frequency Response Function (FRF) data coupled with optimization techniques is presented. Genetic Algorithm (GA) and Bat Algorithm (BA) are used to estimate the location and severity of damage. The damage in the structures is simulated by reduction in rigidity of specific members. Both optimization techniques are coupled with modelled structures using Finite Element Method (FEM). The approach is based on minimizing an objective function by comparing measured and calculated FRFs. The results show that better accuracy is obtained using BA than using GA in terms of precision and computational time. Furthermore, it is found that the proposed approach provides faster solution than other approaches in the literature. [ABSTRACT FROM AUTHOR]

This work presents an experimental and a numerical studies on the effect of the phenomenon of porosity on the mechanical properties of Glass Fiber Reinforced Polymer (GFRP). In a first part, material elaboration, as well as its characterization using a three-point bending test to extract the basic mechanical properties of the material, is considered. In a second part, a finite element model is created to simulate the problem of air bubbles broadly. Several cases of different shapes and sizes are simulated. The results show a significant effect on the reduction of load in both tensile and bending cases as the size of the bubbles increases. Furthermore, the second part includes the application of the Artificial Neural Network-Enhanced Jaya Algorithm (ANN-E JAYA) to predict the reduction of the tensile load as a function of different crack lengths obtained from an Extended Finite Element Method (XFEM) model. Next, to verify the accuracy of provided application , a comparison is made with two other applications such as Artificial Neural Network-Jaya Algorithm (ANN-JAYA) and Artificial Neural Network-Particle Swarm Optimization (ANN-PSO). The results of the three algorithms show good convergence, with a slight increase in accuracy for ANN-E JAYA. MATLAB code and data used in this article can be found at https://github.com/Samir-Khatir/GFRP-ANN-E-JAYA.git. [ABSTRACT FROM AUTHOR]