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AbstractThis paper demonstrates a thorough examination of the bending behavior of sandwich concrete building structures that are reinforced with graphene nanoplatelets (GPLs). The analysis is confirmed using a machine learning technique. Sandwich structures have notable benefits in terms of strength, longevity, and thermal insulation, making them well-suited for many building applications. Integrating GPLs into the concrete matrix improves the mechanical characteristics and performance of these structures, especially in terms of bending behavior. This study utilizes a machine learning technique to verify the characterization of the temporary bending behavior of a concrete building structure reinforced with graphene nanoplatelets. The approach utilizes a dataset consisting of simulated bending data to create a prediction model that can reliably estimate the temporary bending response of the reinforced structure under different loading situations. The machine learning algorithm’s effectiveness and dependability in optimizing the design and performance of graphene nanoplatelets reinforced sandwich concrete building structures are demonstrated through validation against simulated results. This provides engineers and designers with a powerful tool. This study enhances the comprehension and use of machine learning approaches in analyzing and designing sophisticated structural materials and systems. [ABSTRACT FROM AUTHOR]

AbstractThis article presents a new method called the artificial neural networks-genetic programming (ANNs-GP) algorithm, which effectively predicts the bending behavior of functionally graded graphene origami-enabled auxetic metamaterial (FG-GORAM) structures under transient conditions. Functionally graded materials (FGMs) display spatial heterogeneity in their composition and microstructure, resulting in distinctive mechanical characteristics that make them well-suited for a wide range of engineering applications. The objective of this study is to create a prediction model that can accurately capture the intricate transient bending behavior of FGM structures. To do this, the researchers have used the ANN-GP technique, which combines ANNs with GP. The ANN component acquires knowledge from a dataset including actual or simulated bending data, while the GP component fine-tunes the structure and parameters of the neural network to improve its ability to make accurate predictions. The proposed algorithm combines the strengths of ANNs and GP to accurately predict the bending behavior of FG-GORAM structures. This algorithm is robust and efficient, allowing designers and engineers to optimize the performance and reliability of these structures in various applications. The effectiveness of the ANN-GP method is proved by comparing it to experimental or simulated data. This shows that the algorithm has the potential to be a useful tool for designing and analyzing sophisticated materials and structures. [ABSTRACT FROM AUTHOR]

AbstractThis work introduces a quasi-3D refined theory for assessing the transient dynamic reactions of a sandwich annular sector plate subjected to mechanical shock loading. The sandwich structure has two facesheets composed of graphene origami (GOri)-enabled auxetic metal metamaterials (GOEAMs) and a core composed of carbon fiber reinforced polymer. The auxetic quality of the annular plates is mainly determined by the quantity of graphene and the level of folding in the GOri material. The elements are assessed layer by layer across the thickness of the plates. Micromechanical models supported by genetic programming may be used to predict the position-dependent Poisson’s ratio and other material parameters. The concept of Hamiltonian is used to deduce the governing equations of the structure. The equations of motion, which vary with time, are solved using the numerical solution process and the Laplace transform. A comprehensive parametric study is carried out to examine the influence of various geometric and physical parameters on the time-dependent behavior of annular sector plates. The current mathematical modeling results are being compared to the findings of previous works, as well as a machine learning technique. By using this machine learning methodology, it is feasible to computationally solve differential equations at a reduced expense, while simultaneously surmounting the challenge of formulation. To use machine learning techniques, a dependable dataset acquired from either experimental or numerical analysis is necessary. The dataset was created using the quantitative findings of the investigation. Furthermore, the machine learning technique demonstrates its capacity to provide very precise outcomes when predicting the transient behavior of the existing structure under novel loading and boundary circumstances. [ABSTRACT FROM AUTHOR]

Abstract It’s all about speed and scale in the quickly evolving corporate world of today, and business executives are looking to cutting-edge technology to help them find answers to their most difficult problems. A data-driven strategy provides the means to more thoroughly comprehend, confirm, and quantify decision-making outcomes. It is possible to feel greater confidence in the decisions made by the designers in a data-driven business. So, using this essential technique, and via mathematical modeling, the nonlinear wave dispersion of a sandwich nanoshell made of a Bi-directional functionally graded (BD-FG) core, and piezoelectric patch is investigated. In the mathematical modeling, first-order shear deformation theory with moderately thick hypothesis is used to model the displacement fields of the current work for both layers. After that, compatibility conditions are presented to model interface equations between the layers. For correct and exact modeling of the current nanosystem, nonlocal strain gradient theory with length scale and nonlocal parameters is presented. For solving the nonlinear equations, the harmonic solution technic for the displacement domain, and the multiple scale method for the time domain are presented in the solution procedure section. After that, to decrease the computational cost of the current work, the data-driven solution is presented for solving the nonlinear vibration problem. Comparing the results of current work with the outcomes of data-driven solutions and another published article can verify the results. Finally, in the results section, the influences of various factors such as length scale, nonlocal, applied voltage, area of piezoelectric patch, FG power index, and geometry condition of the structure on the nonlinear phase velocity of the BD-FG nanoshell coupled with piezoelectric patch is presented. [ABSTRACT FROM AUTHOR]

Abstract Due to the use of Perovskite solar cells (PSC) in aerospace applications, so, in this work for the first time, aerodynamic flutter and vibrational analysis of PSC are presented. For modeling the PSCs aerodynamic motion in the mathematical domain, the variational differential quadrature (VDQ) technique is used as a numerical operator for the discretization of various domains. Also, due to improving the stability of the PSC, carbon nanotubes with various distribution patterns as the reinforcement of the metal layer are presented. In this work for modeling the aerodynamics behavior, we use Krumhaar’s modified supersonic piston theory incorporating curvature influence, to simulate the aerodynamic stiffness and damping matrices. Also, for modeling the size effect, a modified couple stress theory with only one length scale factor is suggested. Data-driven solution with the aid of deep neural network with independent variables, input, hidden, and output layers, activation function, neural network’s intrinsic parameters, loss function, and hidden neurons is presented to solve the current applicable structure. The results show that the stability of the system is substantially impacted by the value of the yaw angle, and the link between Mach number and stability is heavily reliant on the value of the yaw angle. Another important outcome is that designing a spherical PSC and increasing its stability should have special attention to the a/b parameter. Also, future engineering research should have special attention that spherical solar cell has the highest natural frequency than other types of solar cells. Other important outcomes of the innovative PSC are presented in the results section for future handbooks on solar cells as sustainable energy. [ABSTRACT FROM AUTHOR]

Abstract Multi-directional functionally graded materials (MD-FGMs) are a brand-new family of sophisticated composites with continually changing structural and mechanical properties but no internal boundaries and interfacial stress concentrations. This study is the first to examine the effects of an instantaneous thermal shock on the thermo-elastodynamic response of a multi-directional functionally graded (MD-FG) doubly curved panel resting on an elastic substrate due to the remarkable properties of these advanced materials. The peripheral discontinuity at the boundaries between the loaded and unloaded surfaces as well as the shear coupling between the various Winkler-spring components are both taken into account in this proposed mode of the Kerr-type elastic substrate. Through the use of 3D-DQA, the system’s equations may be solved for both the simply supported and the completely clamped boundary conditions. For the first time, it is examined how several thermal shock models, such as cosinusoidal, sinusoidal, and Heaviside functions, impact how the system’s stress and deflection parameters fluctuate. The authors of this paper provide a unique strategy for enhancing the computing efficiency of the approach by constructing a deep-learning-based method on the data produced by 3D-DQT and Laplace transform at specified locations due to the dynamic nature of the issue. In order to decrease the mechanical excitation of the existing curved structure brought on by thermal shock, some advice for relevant industries is offered at the conclusion. [ABSTRACT FROM AUTHOR]

Abstract Recent years have seen significant research on oscillations in aircraft structures. A self-excited oscillation known as flutter is known to wear down aircraft equipment. So, in this work, the graphene nanoplatelets reinforced composite (GPLRC) micropanel’s nonlinear aerodynamics are modeled in this paper, and the nonlinear aerodynamics properties are validated using an artificial neural network technique. The axial direction and the material characteristics of the current system are graded. The relationship between the input layers of composite weight fraction, mode number, and slenderness ratio and the output layer of nonlinear vibrations derived from mathematical simulation has been estimated using an artificial neural network model. As a training algorithm, Levenberg–Marquardt back-propagation is employed. This study is new in that it attempts for the first time to estimate the nonlinear vibration properties of micropanels built of axially graded GPLRC material using the aforementioned input layer. Without the need to solve any differential equations or go through time-consuming experimental procedures, the suggested artificial neural network model can forecast linear or nonlinear natural frequencies. The findings demonstrate that axially graded GPLRC micropanel linear/nonlinear vibration problems can be effectively addressed using artificial intelligence techniques. Future vibration analyses of the microstructures for engineering reasons can use the provided mode. [ABSTRACT FROM AUTHOR]