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• An efficient numerical framework for magneto-electro-elastic functionally graded porous nanoplate. • The generalized weak form formulation for nonlinear bending of magneto-electro-elastic functionally graded porous nanoplate. • The magneto-electro-elastic model based on Maxwell's equations is established. • The proposed model can high-efficiently predict mechanical, magnetic and electrical coupling. • Some novel benchmark numerical results are illustrated and introduced. We propose an effective approach for geometrically nonlinear analysis of magneto-electro-elastic functionally graded (MEE-FG) porous nanoplate. The key idea relies on a size-dependent isogeometric approach, which is considered as a reliable and interesting technique in this area. A generalized model for MEE-FG nanoplates with porosities satisfies assumptions of the nonlocal Eringen's theory based on von Kármán strains, isogeometric approach and the higher-order shear deformation theory, inherently addressing third derivatives in the approximations. By employing the nonlocal theory, the proposed model effectively illustrates the reduction in nanoplate stiffness under the influence of the nonlocal parameter. We also explore porous distributions across the plate thickness, employing both even and uneven functions, and note that an increase in the porous parameter leads to a more pronounced nonlinear deflection. The electric and magnetic potentials in accordance with Maxwell's equation are denoted. The generalized weak formulation of MEE-FG porous nanoplates is derived by the principle of extended virtual displacement. The influence of small-scale parameter, power law exponent, porosity coefficient and porous distributions on the nonlinear deflection of MEE-FG porous nanoplates is investigated. These findings have significant implications for future research in this field. [ABSTRACT FROM AUTHOR]

• Developed a novel Blood-Sucking Leech Optimizer (BSLO) inspired by blood-sucking leeches. • Evaluated the performance of BSLO using numerical experiments and six engineering problems. • Compared BSLO with ten meta-heuristic algorithms. • Validated the strong robustness and optimization efficiency of BSLO. • Demonstrated superiority of BSLO for diameter prediction of melt electrospinning writing fiber. In this paper, a new meta-heuristic optimization algorithm motivated by the foraging behaviour of blood-sucking leeches in rice fields is presented, named Blood-Sucking Leech Optimizer (BSLO). BSLO is modelled by five hunting strategies, which are the exploration of directional leeches, exploitation of directional leeches, switching mechanism of directional leeches, search strategy of directionless leeches, and re-tracking strategy. BSLO and ten comparative meta-heuristic optimization algorithms are used for optimizing twenty-three classical benchmark functions, CEC 2017, and CEC 2019. The strong robustness and optimization efficiency of BSLO are confirmed via four qualitative analyses, two statistical tests and convergence curves. Furthermore, the superiority of BSLO for real-world problems under constraints is demonstrated using five classical engineering problems. Finally, a BSLO-based Artificial Neural Network (ANN) predictive model for diameter prediction of melt electrospinning writing fibre is proposed, which further verifies BSLO's applicability for real-world problems. Therefore, BSLO is a potential optimizer for optimizing various problems. Source codes of BSLO are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/163106-blood-sucking-leech-optimizer. [ABSTRACT FROM AUTHOR]

• Size-dependent behaviors of auxetic honeycomb sandwich nanoplates is developed. • The proposed approach utilizes a nonlocal strain-gradient isogeometric analysis which incorporates the effects of both nonlocality and strain gradient. • The sandwich nanoplate consists of a core layer featuring an auxetic honeycomb and two outer skin layers reinforced with graphene nanoplatelets. • The length scale parameters can high-efficiently predict size effects. • Some novel benchmark numerical results are illustrated and introduced. Honeycomb structures, which are known for being lightweight and stiff, are still being researched and developed. They have been used in a wide range of industries, but their full potential has not yet been realized. In this study, a novel computational approach for exploring the size-dependent behaviors of auxetic honeycomb sandwich nanoplates is developed. The proposed approach employs a nonlocal strain-gradient isogeometric analysis integrating the influences of nonlocality and strain gradient into the nanoplate structures. The sandwich nanoplate consists of a core layer featuring an auxetic honeycomb with a negative Poisson's ratio, complemented by two outer skin layers reinforced with graphene nanoplatelets (GNPs). This configuration not only achieves exceptional lightweight characteristics through the utilization of auxetic honeycomb cells but also enhances structural stiffness by incorporating GNPs into the skin layers. The material properties of the core layer are determined using cellular cell formulas, while the reinforcement of the two outer skin layers with GNPs is calculated using the modified Halpin-Tsai model. Numerous numerical examples are conducted to investigate the influence of various parameters on the frequencies of the auxetic honeycomb sandwich nanoplates. Notably, the geometrical dimensions of the auxetic honeycomb cells and the nonlocal and length scale parameters emerge as significant influencers on the results. As the first analysis of honeycomb structures at small dimensions, our findings stand as valuable benchmarks for future analyses. [ABSTRACT FROM AUTHOR]

• 2D behaviors of multi-directional FG plates are analyzed by using GSDT and IGA. • NURBS function describes material distribution in all three directions of FG plates. • Deep neural network is utilized to directly predict behaviors of the FG plates. • Optimal material distributions of tri-directional FG plates are found. • An isogeometric multimesh design is used to save significant computational cost. The paper is aimed at enhancing computational performance for optimizing the material distribution of tri-directional functionally graded (FG) plates. We exploit advantages of using a non-uniform rational B-spline (NURBS) basis function for describing material distribution varying through all three directions of functionally graded (FG) plates. Two-dimensional free vibration and buckling behaviors of multi-directional (1D, 2D and 3D) FG plates analyzed by using a combination of generalized shear deformation theory (GSDT) and isogeometric analysis (IGA) is first proposed. This approach can help to save a significant amount of computational cost while still ensure the accuracy of the solutions. The effectiveness and reliability of the present method are demonstrated by comparing it to other methods in the literature. The obtained results are in excellent agreement with the reference ones. More importantly, data sets consisting of input-output pairs are randomly generated from the analysis process through iterations for the training process in deep neural networks (DNN). DNN is utilized as an analysis tool to supplant finite element analysis to reduce computational cost. By using DNN, behaviors of the multi-directional FG plates are directly predicted from those material distributions. Optimal material distributions of tri-directional FG plates under free vibration or compression in various volume fraction constraints are found by using modified symbiotic organisms search (mSOS) algorithm for the first time. Moreover, an isogeometric multimesh design technique is also used to diminish a large number of design variables in optimization. Optimal results obtained by DNN are compared with those of IGA to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

We investigate a novel computational approach to computational structural optimization based on deep learning. After employing algorithms to solve the stiffness formulation of structures, we used their improvement to optimize the structural computation. A standard illustration of 10 bar-truss was revisited to illustrate the mechanism of neural networks and deep learning. Several benchmark problems of 2D and 3D truss structures were used to verify the reliability of the present approach, and its extension to other engineering structures is straightforward. To enhance computational efficiency, a constant sum technique was proposed to generate data for the input of multi-similar variables. Both displacement and stress enforcements were the constraints of the optimized problem. The optimization data for cross sections with the objective function of total weight were then employed in the context of deep learning. The stochastic gradient descent (SGD) with Nesterov's accelerated gradient (NAG), root mean square propagation (RMSProp) and adaptive moment estimation (Adam) optimizers were compared in terms of convergence. In addition, this paper devised Chebyshev polynomials for a new approach to activation functions in single-layer neural networks. As expected, its convergence was quicker than the popular learning functions, especially in a short training with a small number of epochs for tested problems. Finally, a split data technique for linear regression was proposed to deal with some sensitive data. • A novel computational approach based on deep learning for computational structural optimization. • A constant sum technique for generating data for multi-similar variables input. • A new activation function relied on the Chebyshev polynomials. • Superior performance of the present approach for the new activation function. • A split data technique for linear regression proposed to deal with some sensitive data. [ABSTRACT FROM AUTHOR]

Abstract We in this study present a mixed solid-shell finite element formulation for laminated shell analysis. With the commonly adopted truncations applied to the strain components and the Jacobian determinant, a generalized stiffness matrix is established. Discrete shear gap (DSG) technique together with assumed natural strain (ANS) method is employed in order to alleviate shear locking and trapezoidal locking. Stiffness matrix formulation is analytically calculated based on the edge-based cell backgrounds, which result in simple and efficient computation. The proposed formulation does not require using cross-diagonal meshes, which can be an obstacle for several existing three-node degenerated shell elements. This aspect makes the present method much more appealing than others through examples tested. Numerical validations show that the present method performs well for both structured and unstructured triangular meshes. [ABSTRACT FROM AUTHOR]