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Modality and intermittent search strategy in combination with an Improve Particle Swarm Optimization algorithm (IPSO) to detect damage structure via using vibration analysis basic principle of a decline stiffness matrix a structure is presented in the study as a new technique. Unlike an optimization problem using a simplistic algorithm application, the combination leads the promising results. Interestingly, the PSO algorithm solves the optimal problem around the location determined previously, whereas Eagle Strategy (ES) is the charging of locating the position in intermittent space for the PSO algorithm to search local. ES can be easy to deal with its problem via drastic support of Levy flight. As know that the PSO algorithm has a fast search speed, yet the accuracy of the PSO algorithm is not as good as expected in many problems. Meanwhile, the combination is powerful to solve two trouble (1. Avoiding local optimization, 2. Receiving the result more accurate). The paper compares the results obtained from the PSO algorithm with the combination of IPSO and ES in some problems, as well as between experiment and FEM to demonstrate the effectiveness as mentioned. In which natural frequencies is used as the objective function to solve this optimization problem. The results present that the combination of IPSO and ES is quite effective. [ABSTRACT FROM AUTHOR]

The strongest point about damage identification based on the dynamic measurements, is the ability to perform structural health evaluation globally. Researchers in the last few years payed more attention to damage indicators based on modal analysis using either frequencies, mode shapes, or Frequency Response Functions (FRFs). This paper presents a new application of damage identification in a cross-ply (0°/90°/0°) laminated composite plate based on Force Residual Method (FRM) damage indicator. Considering single and multiple damages with different damage levels. As well as investigating the SSSS and CCCC boundary conditions effect on the estimation accuracy. Moreover, a white Gaussian noise is introduced to test the challenges of the technique. The results show that the suggested FRM indicator provides accurate results under different boundary conditions, favouring the SSSS boundary condition than the CCCC for 3% noise. [ABSTRACT FROM AUTHOR]

The dynamic experimental and numerical analysis of cracked beams has been studied with the aim of quantifying the influence of depth crack on the dynamic response of steel beams. Artificial Neural Method ANN has been used where a numerical simulation was improved in Matlab. A finite element model has also been developed by using the Ansys software, and the obtained results were compared with exact crack length. The study takes into account different hidden layer values in order to determine the sensitivity of the predicted crack depth. The results show that the response of the beam (frequencies) is strongly related to the crack depth, which significantly affects the beam behavior, especially when the crack is very deep where the ANN allows us to identify the crack in lower computational time. Based on the provided results, we can detect that the effect of hidden layer size can affect the results. [ABSTRACT FROM AUTHOR]

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]

In this paper, the initial and maximum load was studied using the Finite Element Modeling (FEM) analysis during impact testing (CVN) of pipeline X70 steel. The Gurson-Tvergaard-Needleman (GTN) constitutive model has been used to simulate the growth of voids during deformation of pipeline steel at different temperatures. FEM simulations results used to study the sensitivity of the initial and maximum load with GTN parameters values proposed and the variation of temperatures. Finally, the applied artificial neural network (ANN) is used to predict the initial and maximum load for a given set of damage parameters X70 steel at different temperatures, based on the results obtained, the neural network is able to provide a satisfactory approximation of the load initiation and load maximum in impact testing of X70 Steel. [ABSTRACT FROM AUTHOR]