Advances in nano research

  • 제16권6호
  • /
  • Pages.623-638
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  • 2024
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  • 2287-237X(pISSN)
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  • 2287-2388(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Thermal post-buckling measurement of the advanced nanocomposites reinforced concrete systems via both mathematical modeling and machine learning algorithm

  • Minggui Zhou (School of Intelligent Construction, Luzhou vocational and technical college) ;
  • Gongxing Yan (School of Intelligent Construction, Luzhou vocational and technical college) ;
  • Danping Hu (School of Intelligent Construction, Luzhou vocational and technical college) ;
  • Haitham A. Mahmoud (Industrial Engineering Department, College of Engineering, King Saud University)
  • 투고 : 2020.10.06
  • 심사 : 2024.06.18
  • 발행 : 2024.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

This study investigates the thermal post-buckling behavior of concrete eccentric annular sector plates reinforced with graphene oxide powders (GOPs). Employing the minimum total potential energy principle, the plates' stability and response under thermal loads are analyzed. The Haber-Schaim foundation model is utilized to account for the support conditions, while the transform differential quadrature method (TDQM) is applied to solve the governing differential equations efficiently. The integration of GOPs significantly enhances the mechanical properties and stability of the plates, making them suitable for advanced engineering applications. Numerical results demonstrate the critical thermal loads and post-buckling paths, providing valuable insights into the design and optimization of such reinforced structures. This study presents a machine learning algorithm designed to predict complex engineering phenomena using datasets derived from presented mathematical modeling. By leveraging advanced data analytics and machine learning techniques, the algorithm effectively captures and learns intricate patterns from the mathematical models, providing accurate and efficient predictions. The methodology involves generating comprehensive datasets from mathematical simulations, which are then used to train the machine learning model. The trained model is capable of predicting various engineering outcomes, such as stress, strain, and thermal responses, with high precision. This approach significantly reduces the computational time and resources required for traditional simulations, enabling rapid and reliable analysis. This comprehensive approach offers a robust framework for predicting the thermal post-buckling behavior of reinforced concrete plates, contributing to the development of resilient and efficient structural components in civil engineering.

키워드

과제정보

This work was supported by Sichuan Province Luzhou city of Stars science and technology planning project(2021-JYJ-100) and (2022-JYJ-130). The authors extend their appreciation to King Saud University for funding this work through Researchers Supporting Project number (RSPD2024R1006), King Saud University, Riyadh, Saudi Arabia.

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