Transient bending analysis of the graphene nanoplatelets reinforced sandwich concrete building structure validated by machine learning algorithm

Abstract

This 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.

Keywords:

• Transient bending
• GPLs
• concrete building structure
• machine learning algorithm
• quasi-3D higher-order plate theory

Acknowledgement

The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/122/45.

Disclosure statement

No potential competing interest was reported by the authors.

Additional information

Funding

This work was supported by Sichuan Province Luzhou City of Stars science and technology planning project (2022-GYF-16), and the scientific research and innovation team construction project of Luzhou vocational and Technical College (2021YJTD07).