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This paper is concerned with the optimal design of axial probes, commonly used in the Non-Destructive Testing (NDT) of tube boiling in steam generators. The goal is to improve the low-frequency Foucault-current imaging of these deposits by designing a novel probe. The approach uses a combination of an inverse problem solver with global optimization to find the optimal probe characteristics by minimizing a function of merit defined using image processing techniques. The evaluation of the function of merit is computationally intensive and a surrogate optimization approach is used, incorporating a multi-particle search algorithm. The proposed design is validated through numerical experiments and aims to improve the accuracy and efficiency of identifying deposits in steam generator tubes. [ABSTRACT FROM AUTHOR]

• An ameliorated Golden jackal optimization (AGJO) is proposed using three optimization strategies. • Three numerical experiments and six engineering problems are verified. • The GJO's exploration and exploitation abilities are completely enhanced by AGJO. • The optimization performance of AGJO is verified compared to other algorithms. • Engineering problems show AGJO's strong ability to solve real problems. Golden jackal optimization (GJO), a lately published meta-heuristic optimization algorithm, is inspired by the foraging behavior of pairs of golden jackals and shows an acceptable optimization performance. However, GJO exists a shortage in balancing exploration and exploitation, as it completely focuses on exploitation in the later iterations. A new variant of GJO, named Ameliorated Golden jackal optimization (AGJO), is proposed in this study. Three strategies are employed in AGJO to alleviate the imbalance between the exploration and exploitation of GJO: the enhanced movement strategy, the global search strategy, and the multi-angle position update strategy for prey. An environmental disturbance factor is added to the third strategy to strengthen GJO's ability to evade the local optimal solution. The performance of AGJO is tested and compared with GJO and seven well-known meta-heuristic algorithms for 23 classical benchmark functions, CEC 2017 and the first ten functions of CEC 2006 with constaints. These results show that AGJO performs better over 90% of these functions compared to GJO. Also, AGJO is also highly competitive in terms of optimization capability and convergence speed compared with other algorithms. Finally, AGJO is applied to optimize engineering problems, including five classical engineering design problems with constraints and a displacement prediction problem of composite pipes. Results show that AGJO is a potential algorithm for solving these real problems rather than GJO and other algorithms. [ABSTRACT FROM AUTHOR]

Structural damage detection using wavelet transform is effective if a suitable wavelet function is selected. However, the selection of the appropriate wavelet function can be a challenging task. This paper introduces a novel and efficient criterion for selecting the proper wavelet function. It is emphasized that there is a relationship between the accuracy of damage detection using wavelet detail coefficients and the correctness of the approximation of the original function. Accordingly, we define a ratio function and then a novel wavelet selection criterion called WSC in this study. After applying the proposed WSC criterion for 128 wavelet functions, it is found that the wavelet functions selected via the proposed criterion (both in numerical and experimental scenarios) can detect the position of damages in the beam structure with high accuracy. [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]

Structures and components need to be constantly monitored to ensure good service life and avoid failures. Structural health monitoring is a wide-ranging concept that tries to ensure the safety of components and structures. Vibration responses of small and large structures contain information about these damages. In the present study, this information is extracted and used to classify the location and severity of cracks. For this purpose, an FE model of Glass Fibre Reinforced Plastic (GFRP) free-free beam is created for beams without any crack and beams with three crack locations and three crack severities. Finite Element (FE) model is validated using modal analysis and is used to generate vibration responses from simulated hammer strikes at different locations on the beam. The vibration data consists of responses of beams that have only one crack present at a time and when two cracks are present. A 1D-CNN network is trained on the generated vibrational responses to classify damage labels assigned for each crack location and crack severity. The network was tested using datasets containing only single crack instances and only dual crack instances. The trained networks were found to be 95% and 93% accurate in determining the damage class respectively. Furthermore, datasets of these two instances combined, containing responses when single cracks and dual cracks are present, were also investigated. These networks had an accuracy of 92%. Hence, in all these instances, the 1D-CNN network classifies the healthy and damaged conditions satisfactorily. There are two advantages of using such a technique. Firstly, this approach simplifies the two-step approach: one for feature extraction and another one for damage prediction into a single step. Secondly, the use of raw vibration data for damage prediction means that real-time damage detection is possible. [ABSTRACT FROM AUTHOR]

In practical applications of wavelet transform, engineers and practitioners encounter challenges that arise due to the disparity between wavelet theory, which deals with continuous functions, and the digital nature of signals in engineering contexts. In particular, wavelet transform theory does not consider the effect of changes in digital signals on the result of the wavelet transform. This paper emphasizes the influence of the type of digital signals on the accuracy of wavelet transform in engineering applications and proposes an efficient wavelet function based on the derivative of the signal for better damage detection in beam structures. For this purpose, the obtained signals from the mode shapes of the steel beam are used to examine the efficiency of the proposed derivative-based wavelet transform. The effects of changes in boundary conditions, location of damage, and level of damage on the performance of the proposed method, are evaluated. Findings show that when we use the derivate of the signal in the wavelet transform, the location of damage in all damage scenarios is detected with high accuracy. This research demonstrates the importance of the type of signal used in the wavelet transform for enhancing the precision of fault and damage detection in signals. [ABSTRACT FROM AUTHOR]

This paper presents the effectiveness of a hybrid YUKI-RANDOM-FOREST, Particle Swarm Optimization-YUKI (PSO-YUKI), and balancing composite motion optimization algorithm (BCMO) based on artificial neural networks (ANN) for the best prediction of patch design considering the maximum principal stress. The study compares the maximum principal stress in a damaged pipe under different composite patch designs. Robust models have been developed and utilized in various applications. The research investigates the influence of cracks on the mechanical characteristics of API X70 steel in a test pipe under critical pressure. The numerical model employs the extended finite element method (XFEM) to simulate notches. Extending the optimization technique, the study examines the effect of crack presence in a pipeline section under internal pressure without and with composite repairs on the maximum principal stress. The sensitivity of stress is analyzed with respect to the design parameters of the composite patch. Finally, YUKI-RANDOM-FOREST, NN-PSO-YUKI, and NN-BCMO, with different parameters and hidden layer sizes are employed to predict the maximum principal stress under different composite patch designs, and yielding minimal error. Once the database was built, our model was prepared to predict various situations at the composite patch level. Compared to other methods, the obtained results with hybrid YUKI-RANDOM-FOREST are effective. The investigation technique is relevant to real-world engineering applications, structural safety control, and design processes. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a two-stage approach for damage assessment in beam-like structures using two-dimensional Isogeometric Analysis (IGA) and Finite Element Method (FEM) combined with optimization techniques. In the first stage, the Local Frequencies Change Ratio (LFCR) indicator and a newly developed damage indicator based on normalized Modal Strain Energy Indicator (nMSEDI) are introduced to locate effectively the potential damaged elements. In order to verify nMSEDI, different scenarios based on single and multiple damages are studied using numerical experiments. In the second stage, the Teaching-Learning-Based Optimization Algorithm (TLBO) is utilized and its performance is compared with that of Particle Swarm Optimization (PSO) and Bat Algorithm (BA). The three optimizations techniques are combined with IGA using nMSEDI as objective function. In addition, experimental vibration tests using laboratory steel been are conducted to validate the proposed technique. The obtained results clearly indicate that the proposed approach can be used to determine accurately and efficiently both damage location and severity in beam-like structures. [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]

Fretting fatigue is a destructive failure that usually occurs in the parts or components in the aviation field. One of the important research topics in fretting fatigue is the prediction of crack initiation location. This paper proposed a new prediction methodology of crack initiation location, which is a deep learning model based on the statistical bootstrapping technique and finite element method (FEM). In this work, two kinds of deep learning models, namely Model-I and Model-II, are established based on different input features, and with the crack initiation location as output feature. The input features of Model-I are the contact load, the tangential load, the cyclic axial stress, and the radius of the fretting pad. In Model-II, one additional feature, namely the half contact-width, is considered. Firstly, three different normalization methods (Max-min, Zero-mean, and Nonlinear) are compared. It is found that the prediction of deep learning models based on the Zero-mean is the most accurate and stable. Then, through the study of sample size reliability, it is found that the prediction accuracy gradually increases initially then becomes stable with the increase of the sampling number. The estimated intervals also gradually become narrowed and then stable. Further, another highlight from the comparison between the performance of Model-I and Model-II is that with the physical-mechanical reasoning factor as additional input features, a more stable and accurate estimated crack initiation location can be obtained from deep learning models. The range of estimated intervals is also effectively narrowed. As an alternative to FEM, the computational efficiency of deep learning is at least 30% higher. The range of estimated intervals for crack initiation location are limited to 250 μ m and the errors between predictions and target values are lower than 5%. Therefore, the deep learning method proposed in this paper can effectively and efficiently predict fretting fatigue crack initiation location. [ABSTRACT FROM AUTHOR]

In this paper, a technique is presented for the detection and localization of an open crack in beam-like structures using experimentally measured natural frequencies and the Particle Swarm Optimization (PSO) method. The technique considers the variation in local flexibility near the crack. The natural frequencies of a cracked beam are determined experimentally and numerically using the Finite Element Method (FEM). The optimization algorithm is programmed in MATLAB. The algorithm is used to estimate the location and severity of a crack by minimizing the differences between measured and calculated frequencies. The method is verified using experimentally measured data on a cantilever steel beam. The Fourier transform is adopted to improve the frequency resolution. The results demonstrate the good accuracy of the proposed technique. [ABSTRACT FROM AUTHOR]

This paper presents a new damage detection and localization technique based on the changes in vibration parameters using BAT and Particle Swarm Optimization algorithm. The finite element method is used to apply damage at specific element(s) of the considered beams. The damage is represented by a reduction in Young's modulus and the identification of damage is formulated as an optimization problem using objective function based on Modal Scale Factor and changes in natural frequencies. A procedure for detecting and locating damage of beam-like structures based on BAT algorithm is used. This approach presents a method that can be used to detect the single and multiple-damage positions and the rate of damage in structural elements with high accuracy after the first iteration. The results obtained using BAT algorithm are compared to those obtained using Particle Swarm Optimization Algorithm. By taking noise into account in the damage detection and localization problem, it is shown that our approach based on BAT algorithm can detect the damage locations with high accuracy. [ABSTRACT FROM AUTHOR]

Fretting fatigue is a common type of problem in the aviation and other engineering fields. Due to its multiaxial characteristics, it leads to a shorter overall fatigue life compared to plain fatigue conditions. Fretting fatigue crack initiation lifetime is a crucial part of the total lifetime, and the currently dominant method for research on fatigue behavior is the combination of the theoretical and numerical models. With the advent of the era of data science, machine learning has been widely used to predict fatigue behavior, but there are no many applications in the field of fretting fatigue. This paper proposed an improved Artificial Neural Network (ANN) using Balancing Composite Motion Optimization (BCMO) for quick prediction of fretting fatigue crack initiation lifetime. Physical-mechanical reasoning parameters, axial stress amplitude, shear stress amplitude, half contact width, and half stick zone width are considered as input parameters, and fretting fatigue crack initiation lifetime is set as the output feature. The main aim of BCMO is to improve the robustness of the ANN based on the influential parameters, namely bias and weight. The provided results using ANN-BCMO are robust compared to ANN and traditional techniques from the literature. The Matlab code of improved ANN using BCMO can be found at https://github.com/Samir-Khatir/BCMO-ANN.git [ABSTRACT FROM AUTHOR]

This paper presents a methodology for damage detection and localization in composite beams using vibration data, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The data was acquired by developing a program that performs dynamic analysis of unidirectional graphite-epoxy composite beams based on the Finite Element Method (FEM). The objective function makes use of natural frequencies and Modal Assurance Criterion. The proposed methodology is validated using numerically simulated data and experimental data. A comparative study between the performances of PSO and GA in detecting multiple and single damage scenarios is carried out. Then, the effect of noise is investigated by taking different noise levels in the modal data. It appears that the noise has a negligible effect on the performance of the presented approaches. [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 a new damage detection and localization technique based on the changes in vibration parameters using BAT and Particle Swarm Optimization algorithm. The finite element method is used to apply damage at specific element(s) of the considered beams. The damage is represented by a reduction in Young's modulus and the identification of damage is formulated as an optimization problem using objective function based on Modal Scale Factor and changes in natural frequencies. A procedure for detecting and locating damage of beam-like structures based on BAT algorithm is used. This approach presents a method that can be used to detect the single and multiple-damage positions and the rate of damage in structural elements with high accuracy after the first iteration. The results obtained using BAT algorithm are compared to those obtained using Particle Swarm Optimization Algorithm. By taking noise into account in the damage detection and localization problem, it is shown that our approach based on BAT algorithm can detect the damage locations with high accuracy. [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]

• Improved GWO using PSO. • Inverse problem using GWO and IGWO. • Crack identification. • Experimental validation for crack identification. This paper introduces an inverse problem for crack identification in two-dimensional structures using eXtend Finite Element Method (XFEM) associated with original Grey Wolf Optimization (GWO) and improved GWO using Particle Swarm Optimization (PSO) (IGWO). Static analysis with different boundary conditions and experimental modal analysis of cracked plates with varying crack length, positions, and orientation are used to test the accuracy of IGWO compared with the original GWO. The objective function is based on vertical measured strain and is computed at each iteration. The obtained results indicate that IGWO provides more accurate results than GWO based on convergence study and the error between exact and estimated results. Next, another application based on dynamic experimental cracked plates is used to improve Artificial Neural Network (ANN) parameters using GWO and IGWO. The frequencies and crack lengths are used as input and output for vertical and horizontal cracks in the plates. Thus, the model can be used for the prediction of crack length. IGWO can select the best parameters for better prediction compared with GWO. [ABSTRACT FROM AUTHOR]

Theoretical research has numerous challenges, particularly about modeling structures, unlike experimental analysis, which explores the mechanical behavior of complex structures. Therefore, this study suggests a new model for functionally graded shell structures called "Tri-coated FGM" using a spatial variation of material properties to investigate the free vibration response incorporating the porosities and microstructure-dependent effects. Two types of tri-coated FG shells are investigated, hardcore and softcore FG shells, and five distribution patterns are proposed. A novel modified field of displacement is proposed by reducing the number of variables from five to four by considering the shear deformation effect. The shell is rested on a viscoelastic Winkler/Pasternak foundation. An analytical solution based on the Galerkin approach is developed to solve the equations of motion derived by applying the principle of Hamilton. The proposed solution is addressed to study different boundary conditions. The current study conducts an extensive parametric analysis to investigate the influence of several factors, including coated FG nanoshell types and distribution patterns, gradient material distribution, porosities, length scale parameter (nonlocal), material scale parameter (gradient), nanoshell geometry, and elastic foundation variation on the fundamental frequencies. The provided results show the accuracy of the developed technique using different boundary conditions. [ABSTRACT FROM AUTHOR]

• Enhanced flexibility indicator for damage detection. • Atom Search Optimization for damage quantification. • Complex planar truss and space truss dome structures. • Damage assessment in Canton Tower. In this paper, an enhanced damage indicator using a flexibility index is presented and applied to different complex structures to predict the exact location of damage. Finite Element Method (FEM) is used to model three complex structures, namely a 37-bar planar truss, a 52-bar planar truss, and a 52-bar space truss (Dome structure) to study the effectiveness of the proposed indicator. Single and multiple damage scenarios with different damage levels are considered. The results show that the proposed indicator provides an accurate location of damage. Next, to quantify the damage and assess its severity, two optimization techniques, namely Atom Search Optimization (ASO) and Salp Swarm Optimizer (SSA), which are recently invented, are used to solve an inverse problem. The objective function is based on the measured and calculated enhanced damage indicators. Both optimization techniques provide good results, however the convergence performance and CPU time are better for ASO than SSA. Finally, the proposed approach is tested using a benchmark structure, namely a high-rise tower (Guangzhou TV Tower) to predict the damage location at different floors. The results indicate that the proposed methodology is accurate and fast to predict single and multiple damages. [ABSTRACT FROM AUTHOR]

In this paper, two-stage approaches are proposed to study damage detection, localization and quantification in Functionally Graded Material (FGM) plate structures. Metal and Ceramic FGM plates are considered using three different composite materials: Al/Al 2 O 3 , Al/ZrO 2-1 , and Al/ZrO 2-2. The FGM plates are modelled using IsoGeometric Analysis (IGA), which is more efficient than the classical Finite Element Method (FEM). Using a power-law distribution of the volume fractions of the plate constituents, the material properties of the plates are expected to vary continuously through their thickness. Improved damage indicator based on Frequency Response Function (FRF) is employed to predict the damaged elements in the first stage. A robust and efficient Improved Artificial Neural Network using Arithmetic Optimization Algorithm (IANN-AOA) is implemented for damage quantification problem in the second stage. The main idea is based on eliminating the healthy elements from the numerical model by the improved indicator. Next, collected data from damaged element based on damage index of an improved indicator is used as input and damage level as output. To prove the robustness of IANN-AOA, a Balancing Composite Motion Optimization (BCMO) is considered to improve ANN (IANN-BCMO) and is used for comparison. The results show that the improved indicator can predict the damaged elements with high precision. For damage quantification, IANN-AOA provides more accurate results than IANN-BCMO. [ABSTRACT FROM AUTHOR]

• A novel Enhancing Particle Swarm Optimization Algorithm (EHVPSO) for damage identification in structures. • Damage assessment of Electric Power Transmission with 44.05 m height. • Open Application Programming Interface (OAPI) source code making use of SAP2000 and MATLAB. • High accuracy and reliability of the results obtained using EHVPSO. In this paper, a novel enhanced Particle Swarm Optimization (PSO) algorithm is introduced for solving damage identification problems. For the first time, the algorithm is applied to a complex structure, namely an Electric Power Transmission with 44.05 m height. The process of structural damage assessment is implemented using SAP2000 commercial software combined with MATLAB. Using the Open Application Programming Interface (OAPI) source code, which is available in SAP2000, a strong MATLAB environment program has been developed in this research. This program can allow the user to adjust the initial model's parameters in SAP2000 to create a continuous two ways data exchange between SAP2000 and MATLAB. Thus, the process of detecting the location and level of damage in the structure is performed by applying a new version of PSO, namely Enhancing Particle Swarm Optimization Algorithm (EHVPSO), using stochastic parameters. The key factor in the EHVPSO algorithm is to introduce two novel equations. The first equation is used to control the convergence rate in each movement of particle i th , and the second equation is used to control the balance between local optimal value and global optimal value. The results demonstrate that the proposed algorithm can detect damage with very high accuracy and reliability. [ABSTRACT FROM AUTHOR]

Fretting happens when there is an oscillatory displacement between two contact parts. In gross slip regime, wear is a dominant failure mode, which has a detrimental effect on the contact parts. To understand the wear behaviour in the fretting process, many experiments and simulations have been done. This phenomenal analysis is time-consuming, especially when fretting wear investigation is required under certain loading cases or during certain number of cycles. Under these conditions, fast prediction tools provide an alternative for this kind of problem. In this paper, a model reduction based on Proper Orthogonal Decomposition with Radial Basis Function (POD-RBF) is used to predict the wear characteristics of specimens under different slip amplitudes and at different fretting wear cycles based on simulation results. To demonstrate the advantages and disadvantages of POD-RBF, Grey Model and Curve Fitting are used to verify POD-RBF for one-parameter prediction, while Artificial Neural Network is applied to verify POD-RBF for three-parameter prediction. The results show that the approach of POD-RBF can predict the wear characteristics under different slip amplitudes and normal loads at different cycles with high accuracy and high computational efficiency. • Predictive model for fretting wear characteristics. • Proper Orthogonal Decomposition with Radial Basis Function (POD-RBF). • Grey Model, Curve Fitting Artificial Neural Network are used to verify the results. • POD-RBF provides high computational efficiency. [ABSTRACT FROM AUTHOR]

Recently, more attention has been paid to Artificial Neural Network (ANN) in the field of damage identification of engineering structures based on modal analysis. This paper proposes a new modified damage indicator, using transmissibility technique to improve Local Frequency Response Ratio (LFCR), combined with ANN. The main objective of the proposed damage indicator is to reduce the number of collected data for fast prediction and with higher accuracy instead of collecting all modal analysis data, i.e. natural frequencies, damping ratios, and mode shapes, or using inverse analysis for damage quantification. The suggested approach is tested using three layers laminated cross-ply [0°/90°/0°] composite beam and plate having single and multiple damage(s). The reliability and accuracy of the proposed application are demonstrated by predicting the severity of damages in the considered composite structures after analysing four damage scenarios. [ABSTRACT FROM AUTHOR]

A simple, yet powerful, new technique based on Artificial Neural Network (ANN) combined with Particle Swarm Optimization (PSO) for damage quantification in laminated composite plates using Cornwell indicator (CI) is proposed. The analysis is performed in two stages. In the first stage, IsoGeometric Analysis (IGA) is formulated for square laminated composite plates having three layers [ 0 ° / 90 ° / 0 ° ]. In the second stage, IGA model is coupled with PSO for damage quantification using an inverse problem approach and CI as an objective function to minimize the difference between calculated and measured values. This paper aims to assess the application of ANN-PSO for damage quantification in composite structures in order to achieve efficient computational time. IGA is used as modelling technique, CI is used as input data, whereas damage locations and severities are used as output data. The result indicates that high accuracy of damage quantification is achieved using ANN-PSO-IGA-CI. Furthermore, it is demonstrated that huge saving in computational time is achieved when using ANN-PSO-IGA-CI compared with PSO-IGA-CI. [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]

Metal/ceramic functionally graded materials (FGMs) have been increasingly used for impact-resistant applications because of their ability to combine the strength of both components. However, understanding the local response of FGMs under ballistic impact conditions remains a complex nonlinear problem. Moreover, performing experimental investigations is difficult due to technical limitations in measuring critical parameters such as stress, strain, and pressure. That is why research in this field also concentrates on modeling methodologies, such as numerical simulations. In this study, a finite element model (FEM) was implemented to investigate the behavior of a particular metal/ceramic-based FGM impacted with fragment-simulating projectiles (FSPs). The studied FGMs, exhibiting an elastoplastic behavior, were composed of aluminum (Al) and silicon carbide (SiC). The ceramic volume fraction (Vc) varies according to a power-law distribution, through the thickness. Their effective material properties were evaluated using a homogeneization-based self-consistent method. FGM's dynamic behavior was described using the dynamic Tamura-Tomota-Ozawa model (DTTO). The numerical simulations were in good correlation with experimental results. The importance of the DTTO model's introduction and the calibration of the plastic strain criterion in the failure modeling of FGMs were highlighted. In addition, it was observed that the variation in the composition exponent and grading continuity of mechanical properties has a significant effect on the predicted ballistic limit. It was finally noted that a linearly-composed 5-layerbased specimen exhibited a higher level of ballistic resistance. [ABSTRACT FROM AUTHOR]

This work proposes a novel technique to conduct back‐analysis of lateral displacement of deep cement mixing (DCM) columns in deep excavation construction. For the first time, we propose a process to investigate both soil and underground structure end‐to‐end automatically. The novel technique is a complex combination of three crucial factors: (1) a nature‐inspired optimization algorithm (O), (2) a three‐dimensional PLAXIS Geotechnical Engineering software (P) and (3) a modern Python language programming (P), hereinafter called the a nature‐inspired optimization algorithm (O), a three‐dimensional PLAXIS Geotechnical Engineering software (P) and a modern Python language programming (P) (OPP) technique. The novel meta‐heuristic algorithm simulated the co‐evolved partnership behavior of shrimps and goby fishes, termed Shrimp and Goby Association (SGA), which plays an important role in complex analyses. A series of exams to determine the SGA's performance is conducted on 38 benchmark test functions (IEEE Congress on Evolutionary Computation 2017 and 2019) and three real‐world engineering design problems to showcase its applicability. The metaheuristic and PLAXIS 3D analysis work well together, which makes the back‐analysis technique powerfully to determinate stiffness parameters instead of the traditional approaches. Based on the optimized parameters, the lateral deflection of DCM and soil are well predicted for excavation. This study proposes a technique to estimate efficiently the stiffness parameter for very soft soil. As a consequence of the optimization process, an equation to determine the stiffness parameter of DCM columns from laboratory test is also proposed. Based on the obtained results, this research provides a comprehensive methodology for predicting risk, enhancing safety, saving time and money, and effectively designing and constructing underground structures. [ABSTRACT FROM AUTHOR]

This paper presents an advanced numerical model based on Carrera unified formulation (CUF) and isogeometric analysis (IGA), the size-dependent finite element unified formulation model is proposed to investigate the static bending and free vibration of laminated composite nano-plate. The CUF type of trigonometric function with nine degrees of freedom is used to simulate the displacement fields of laminated nano-plate. The size effect of nano-plate structures is included through the Eringen’s nonlocal elastic theory. The size-dependent governing equations for static bending and free vibration of laminated are established based on CUF, IGA and nonlocal theory. The correctness of the presented numerical model is verified by comparison with existing solutions. Furthermore, with the addition of a nonlocal effect, the plate’s stiffness decreases correlating to an increase in nonlocal parameter. Through different calculations, the changes in the static and free vibration responses of laminated composite nano-plate are effected by nonlocal parameter, plate length-to-thickness ratio, and boundary conditions including Young’s modulus ratio. [ABSTRACT FROM AUTHOR]

In this paper, a new method in forecasting the horizontal displacement of diaphragm wall (D.W.) for high-rise buildings is introduced. A new stochastic optimizer, called Planet Optimization Algorithm (P.O.A.), is employed to assess how proper finite element (F.E.) simulation is against field data. The process is adopted for a real phased excavation measured at the field. To automatically run the iterative optimization tasks, a source code is constructed directly in the Geotechnical Engineering Software (PLAXIS) by using Python to ensure that the operation between optimization algorithm and F.E. simulations are smooth to guarantee the accuracy of the complex calculation for the soil problem. The proposed process consists of two steps. (1) The parameters will be optimized at the early phases of the excavation. (2) The responses of D.W. displacements are forecasted at the subsequent phases. The aim of the process is to predict the displacements of D.W. of the building from the result of the nearby excavation or to provide early warning about the risks of excavation that may happen under vital phases. The proposed procedure also provides an effective method for optimization-based soil parameters updating in real engineering practice. [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]

In this paper, a new experimental study of the bending static and fatigue behaviors of a composite material reinforced with 40% by mass of short glass fibers (type E) and polypropylene matrix is presented. The composite material is obtained in the form of plates by an injection process, which inevitably affects the distribution of the fibers and therefore the behavior of the material studied. To do this, several techniques are implemented on specimens by cutting them in transverse and longitudinal directions. The effect of aging in distilled water at 40℃ on the mechanical characteristics is studied under static and fatigue loading conditions. The static tests, three-point flexure up to failure, allow us to choose the levels of stress for the fatigue tests. The endurance curves as a function of the number of cycles are plotted by adapting the end-of-test criteria N5, N10, and N20, which represent a rigidity drop of 5%, 10%, and 20%, respectively. An interpretation of the Wöhler curve equations defined for the end-of-test criteria allows defining the kinetics of material damage. The results highlighted the influence of distilled water on the mechanical behavior and the lifetime of the material. We also perform macroscopic observations of fracture and microscopic facies in order to identify the damage mechanisms of the composite material. [ABSTRACT FROM AUTHOR]

• A new prediction method for fretting fatigue crack initiation lifetime. • A combination of deep neural network, Findley parameter and finite element method. • Accurate prediction of fretting fatigue lifetime. In the aviation field, the crack initiation stage could be the dominant one of the whole damage failure process of components subjecting to fretting fatigue. Thus, getting an accurate prediction of the crack initiation lifetime become an important topic in research of fretting fatigue problems. In this work, a new prediction method for fretting fatigue crack initiation lifetime is proposed, which is based on Deep Neural Network (DNN), Findley Parameter (FP) and Finite Element Method (FEM). Input features are shear stress amplitude and maximum normal stress in the critical plane, and crack initiation lifetime is set as the output target. Dropout and bootstrapping are considered to improve the performance of DNN. Through comparative analysis, it is shown that this new proposed approach can effectively predict the fretting fatigue crack initiation lifetime. The prediction accuracy and stability are greatly improved compared with the theoretical formulation based on FP. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we investigate the effect of hygrothermal aging in different media on the mechanical behaviour of mixed short fibre/woven composite laminates. We study composite laminates made up of 4 plies of fiberglass fabrics (Mat 300 g/m2, Mat 450 g/m2, Taffetas 800 g/m2) and unsaturated polyester resins, which are realised by contact moulding. In order to properly characterize our materials and to understand the phenomena responsible for modifying their behaviour, various physico-chemical and mechanical tests are carried out on composite laminates with and without a gel coat. The main aim of our study is to determine the mechanical properties, e.g. ultimate stress, yield strength and flexural modulus, of the laminates through three-point bending tests, under the effect of hygrothermal aging in aggressive environments, i.e. distilled water and saline water at different time intervals, and high temperature. In order to follow the physico-chemical evolutions and their influences on the mechanical properties of the composite laminates, a comparative study of the properties is carried out for both unaged and aged specimens. The analysis of all obtained results shows that aging time, medium and temperature have a significant influence on the mechanical behaviour of mixed short fibre/woven composites. [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]

Damage detection and localization in civil engineering constructions using dynamic analysis has become an important topic in recent years. This paper presents a methodology based on non-destructive detection, localization and quantification of multiple damages in simple and continuous beams, and a more complex structure, namely two-dimensional frame structure. The proposed methodology makes used of Firefly Algorithm and Genetic Algorithm as optimization tools and the Coordinate Modal Assurance Criterion as an objective function. The results show that the proposed combination of Coordinate Modal Assurance Criterion and Firefly Algorithm or Genetic Algorithm can be easily used to identify multiple local structural damages in complex structures. However, the convergence rate becomes slower for the case of multiple damages compared to the case of single damage. The effect of noise on the algorithm is further investigated. It is found that the proposed technique is able to detect the damage location and its severity with high accuracy in the presence of noise, although the convergence rate became slower than in the case when no noise is present. It is also found that the convergence rate of Firefly Algorithm is much faster than that of Genetic Algorithm. [ABSTRACT FROM AUTHOR]

The durability and simplicity of the programming of meta-heuristic algorithms make them important in the optimization field. This paper presents a novel application for double cracks identification in Carbon Fiber Reinforced Polymer (CFRP) cantilever beams based on experimental and numerical analyses using enhanced optimization techniques. A new hybrid algorithm Particle Swarm Optimization and YUKI (PSO-YUKI) is proposed and combined with Radial Basis Functions (RBF) for solving fast inverse problems. The direct problem is based on the results of the dynamic experimental test of CFRP laminate, measuring the dynamics characteristics of a healthy beam, and the variation in the response corresponding to different scenarios of double cracks with different depths. The Finite Element Method (FEM) is used to simulate this vibrational behavior considering double cracks in different locations. The goal is creating an accurate damage identification method that has a high computational performance, based on the idea of building models that combines the vibrational responses issued from the experiments and simulations. The suggested method is tested based on collected data from numerical and experimental modal analyses in the case of undamaged and damaged CFRP laminates to demonstrate its accuracy and efficiency. The provided results show the robustness of PSO-YUKI compared with PSO for double cracks depth identification. The Matlab Code of PSO-YUKI can be found at https://github.com/Samir-Khatir/Hybrid-PSO-YUKI-.git. [ABSTRACT FROM AUTHOR]

Floating is a dangerous phenomenon for aircrafts and usually ends with plane rupture. It can be used to characterize a dynamic instability of the structure. Floating phenomenon is usually due to an interaction between aerodynamics, elasticity and inertial forces. It appears at a determinate speed of flight called critical floating speed. The aim of the present study is to determine the floating critical speed of an aircraft wing in a subsonic fan by means of experimental tests and numerical simulations using the commercial Finite Element Analysis package ANSYS CFX. The obtained experimental results are in good agreement with those obtained using the numerical approach. [ABSTRACT FROM AUTHOR]

Structural damage identification has been researched for a long time and continues to be an active research topic. This paper proposes the use of the natural frequencies of a novel composite structures made of glass fibre reinforced polymer (GFRP). The proposed methodology consists of an improved Artificial Neural Network (ANN) using optimization algorithms to detect the exact crack length. In the first step, the characterization of fabricated material is provided to determine Young's modulus using an experimental static bending test, tensile test and modal analysis test. Next, numerical validation is performed using commercial software ABAQUS to extract more data for different crack locations in the structure. The comparison between experimental and numerical results shows a good agreement. ANN has been improved using recent optimization techniques such as Jaya, enhanced Jaya (E-Jaya), Whale Optimization Algorithm (WOA) and Arithmetic Optimization Algorithm (AOA) to calibrate the influential parameters during training. After considering several scenarios, the results show that the accuracy of E-Jaya is better than other optimization techniques. This study on crack identification using improved ANN can be used to investigate the safety and soundness of composite structures. [ABSTRACT FROM AUTHOR]

• Damage detection in high-rise buildings. • Finite element model updating using a link between SAP2000 commercial software and MATLAB. • Inverse problem using a bio-inspired meta-heuristic optimization algorithm, named Termite life cycle optimizer (TLCO). In this paper, for the first time, a damage assessment technique for a high-rise concrete structure is presented. This structure has sixteen stories, a total height of 57 m and its total long and short spans are 33 m. Especially, this structure has the appearance of shear wall and core wall elements, which has not yet investigated in the literature. For this purpose, a new Finite Element (FE) model updating technique is proposed based on the development of a successful program, which makes a link between SAP2000 commercial software and MATLAB program. This program allows the user to create a continuous two-way data exchange between SAP2000 and MATLAB. Thus, the process of parameters updating in the FE model in SAP2000 can be implemented by command in the code instead of by point-click procedures. The process of structural damage assessment is then secured by inverse methods. This method will be handled by a bio-inspired meta-heuristic optimization algorithm, namely Termite Life Cycle Optimizer (TLCO), which is proposed by the authors recently. The parameters in TLCO are fixed. Therefore, it is not necessary for the user to set the initial parameters, which is an effective strategy compared to other algorithms. The results obtained in this study show a significant improvement in the damage identification of large-scale structures. [ABSTRACT FROM AUTHOR]

Local stress concentration occurs when faults are present in pipelines under pressure. An example of such defects is the problem of corrosion caused by the environment in the field of pipeline installation. In the first part of this paper, we attempt to model the corrosion in the hemi-ellipsoidal form in order to study the locations of stress concentration in the specimens by several experimental cases and their influence on the stress resistance. The Gurson-Tvergaard-Needleman (GTN) mesoscopic damage model is used to simulate the specimens with good accuracy. In the second part, the investigation is extended to a pipe under static pressure with and without the presence of a glass fibre reinforced polymer (GFRP) composite patch. The maximum stress and percent stress reduction in a defected pipe with a hemi-ellipsoidal defect are determined using a 3D finite element model. This part examines the impact of the geometry of the composite patches on the percentage reduction of the maximum stresses in a section of pipeline subjected to static pressure. In the third part, the stresses and the percentage reduction in the maximum stresses are predicted using an artificial neural network (ANN). An inverse problem using ANN and Jaya algorithm is proposed to predict the group level of different sizes of defects under composite patches based on the maximum stress and percentage reduction of stress that the pipe withstands. The new method relates directly to real-world pipeline construction and repair applications. It could be also used for structural safety monitoring. [ABSTRACT FROM AUTHOR]

Damage detection and localization in civil engineering constructions using dynamic analysis has become an important topic in recent years. This paper presents a methodology based on non-destructive detection, localization and quantification of multiple damages in simple and continuous beams, and a more complex structure, namely two-dimensional frame structure. The proposed methodology makes used of Firefly Algorithm and Genetic Algorithm as optimization tools and the Coordinate Modal Assurance Criterion as an objective function. The results show that the proposed combination of Coordinate Modal Assurance Criterion and Firefly Algorithm or Genetic Algorithm can be easily used to identify multiple local structural damages in complex structures. However, the convergence rate becomes slower for the case of multiple damages compared to the case of single damage. The effect of noise on the algorithm is further investigated. It is found that the proposed technique is able to detect the damage location and its severity with high accuracy in the presence of noise, although the convergence rate became slower than in the case when no noise is present. It is also found that the convergence rate of Firefly Algorithm is much faster than that of Genetic Algorithm. [ABSTRACT FROM AUTHOR]

Discrete wavelet transform is a useful means for crack identification of beam structures. However, its accuracy is severely dependent on the selecting mother wavelet and vanishing moments, which raises a significant challenge in practical structural crack identification. In this paper, a novel approach is introduced for structural health monitoring of beams to fix this challenge. The approach is based on the combination of statistical characteristics of vibrational mode shapes of the beam structures and their discrete wavelet transforms. First, this paper suggests using regression statistics between intact and damaged modes to monitor the health of beam structures. Then, it suggests extracting quasi-Pearson-based mode shape index of the beam structures to use them as an original signal in discrete wavelet transforms. Findings show that the proposed approach has several advantages compared with the conventional mode shape signal processing by the discrete wavelet transforms and significantly improves damage detection's accuracy. [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]

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]

• New Movement Strategy of Cuckoo Search (NMS-CS) for solving optimization problems. • 3 proposed functions are used to establish new strategy movement of Cuckoo birds. • Large scale function (CEC2005) and engineering design problems are verified. • The NMS-CS algorithm has proven highly reliable in solving optimization problems. • NMS-CS is considered to be a more complete version than original CS algorithm. In this paper, a Cuckoo search algorithm, namely the New Movement Strategy of Cuckoo Search (NMS-CS), is proposed. The novelty is in a random walk with step lengths calculated by Lévy distribution. The step lengths in the original Cuckoo search (CS) are significant terms in simulating the Cuckoo bird's movement and are registered as a scalar vector. In NMS-CS, step lengths are modified from the scalar vector to the scalar number called orientation parameter. This parameter is controlled by using a function established from the random selection of one of three proposed novel functions. These functions have diverse characteristics such as; convex, concave, and linear, to establish a new strategy movement of Cuckoo birds in NMS-CS. As a result, the movement of NMS-CS is more flexible than a random walk in the original CS. By using the proposed functions, NMS-CS achieves the distance of movement long enough at the first iterations and short enough at the last iterations. It leads to the proposed algorithm achieving a better convergence rate and accuracy level in comparison with CS. The first 23 classical benchmark functions are selected to illustrate the convergence rate and level of accuracy of NMS-CS in detail compared with the original CS. Then, the other Algorithms such as Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Grey Wolf Optimizer (GWO) are employed to compare with NMS-CS in a ranking of the best accuracy. In the end, three engineering design problems (tension/compression spring design, pressure vessel design and welded beam design) are employed to demonstrate the effect of NMS-CS for solving various real-world problems. The statistical results show the potential performance of NMS-CS in a widespread class of optimization problems and its excellent application for optimization problems having many constraints. Source codes of NMS-CS is publicly available at http://goldensolutionrs.com/codes.html. [ABSTRACT FROM AUTHOR]

Currently, meta-heuristic algorithms have been widely studied and applied, but balancing exploration and exploitation remains a challenge. In this study, a novel meta-heuristic algorithm named Sinh Cosh Optimizer (SCHO) is proposed based on the mathematical inspiration of the characteristics of Sinh and Cosh. SCHO includes four steps: two different phases of exploration and exploitation, the bounded search strategy, and the switching mechanism. SCHO is compared with eight meta-heuristic algorithms for the 23 benchmark functions at different dimensions and CEC 2014, and its strong performance is validated. The efficiency and robustness of SCHO are verified by qualitative analysis, convergence curves, and two statistical tests. Furthermore, five engineering problems are presented. Source codes of SCHO are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/130734-a-sinh-cosh-optimizer. [ABSTRACT FROM AUTHOR]

This paper reviews the application of metaheuristics for optimized sustainable supply chain management (SSCM). This paper explores the potential of metaheuristics to improve the supply chain's sustainability while enhancing its efficiency and competitiveness. The paper provides an overview of the principles of SSCM and the challenges businesses face in achieving sustainable supply chain management. It then introduces the concept of metaheuristics and describes their use in solving complex optimization problems. The paper reviews various metaheuristics algorithms applied to sustainable supply chain management and analyzes their effectiveness in addressing the challenges of SSCM. The paper also identifies the key factors that influence the success of using metaheuristics for SSCM, such as the choice of algorithm, problem complexity, and data quality. Finally, the paper provides recommendations for future research in this area and highlights the potential of metaheuristics to promote sustainable supply chain management. The review suggests that metaheuristics can be a valuable tool for optimizing sustainable supply chain management and improving supply chain operations' sustainability, efficiency, and competitiveness. [ABSTRACT FROM AUTHOR]