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Fiber-reinforced concrete significantly enhances the structural and nonstructural properties of traditional concrete by incorporating fibers like steel, glass, and polymers. These fibers, varying from natural ones such as sisal and cellulose to manufactured ones like polypropylene and Kevlar, are mixed into hydraulic cement with aggregates. Steel fibers, often preferred for their robustness, contribute to improved ductility, toughness, and post-cracking performance. The concrete is classified...
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The design of prismatic beams, structural elements with a uniform cross-section, focuses on ensuring safety and structural integrity under load. The design process begins by determining the allowable stress, either from material properties tables, or by dividing the material's ultimate strength by a safety factor. This safety factor is essential for accommodating uncertainties, and varies depending on the material—timber, steel, or concrete—with each having unique strength and...
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Concrete is a fundamental building material, and understanding its strengths is crucial for construction projects. The relationship between its tensile and compressive strengths is intricate, showing that while these strengths are related, they do not increase at the same rate. Tensile strength's growth is slower and is affected by various factors such as the methods used for testing, the size and shape of the specimen, the texture of the aggregate used, and the moisture content of the...
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Concrete exhibits specific behaviors under different compressive loads. Understanding this is crucial for understanding its structural integrity. When concrete undergoes uniaxial compression, it tends to develop cracks that run parallel to the direction of the force. These parallel cracks stem from localized tensile stresses that occur perpendicular to the compression direction. Additionally, angled cracks may appear due to the formation of shear planes.
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AI-Powered Inverse Design of High-Performance Unidirectional CFRP Composites: Breaking the Compressive-Tensile

Yinyi Xu1, Jiani Zhang1, Tianhao Xu1

  • 1Shanghai Key Laboratory of Advanced Polymeric Materials, Key Laboratory for Ultrafine Materials of Ministry of Education, Frontiers Science Center for Materiobiology and Dynamic Chemistry, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai, China.

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This study introduces an AI framework using machine learning and genetic algorithms to design advanced unidirectional carbon fiber reinforced polymer (UD-CFRP) composites with improved compressive strength. The AI approach enhances composite design efficiency and performance.

Keywords:
artificial intelligencecompositescompressive‐to‐tensile strength ratiogenetic algorithminverse design

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Area of Science:

  • Materials Science
  • Composite Materials Engineering
  • Artificial Intelligence in Engineering

Background:

  • Unidirectional carbon fiber reinforced polymer (UD-CFRP) composites exhibit excellent tensile properties but suffer from low compressive strength.
  • This limitation restricts their use as primary structural components in demanding engineering applications.
  • Developing UD-CFRPs with high compression-to-strength ratios is crucial for expanding their structural applications.

Purpose of the Study:

  • To develop a systematic AI-enabled framework for the inverse design of UD-CFRPs.
  • To achieve UD-CFRPs with enhanced compression-to-strength ratios.
  • To demonstrate a novel approach for optimizing composite material properties.

Main Methods:

  • An AI-enabled inverse design framework integrating machine learning and genetic algorithms was employed.
  • An ensemble machine learning model was used for robust prediction of UD-CFRP mechanical properties.
  • Genetic algorithms were utilized to optimize resin matrix parameters for desired performance.

Main Results:

  • The AI framework successfully designed UD-CFRPs with improved compression-to-strength ratios.
  • The ensemble model provided robust mechanical property predictions.
  • The developed framework demonstrated superior computational efficiency compared to traditional forward design methods.
  • Experimental validation confirmed the framework's effectiveness.

Conclusions:

  • The AI-driven inverse design framework offers an efficient and effective strategy for developing high-performance UD-CFRPs.
  • This approach overcomes the limitations of traditional trial-and-error experimental methods.
  • The methodology presents a pioneering application of AI in composite inverse design with potential for broader composite material development.