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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]