AI Article Synopsis

  • CFRP (Carbon Fiber Reinforced Polymer) is effective in reinforcing steel structures due to its high strength and lightweight properties.
  • The study combines Finite Element Analysis and experimental testing to assess how different reinforcement patterns of CFRP influence the load-carrying capacity of steel beams, finding that increasing layers of CFRP cloth improves capacity by up to 11.1%.
  • A random forest algorithm model was developed to evaluate load-carrying capacity, achieving good prediction accuracy with a Mean Absolute Error of 0.12 and a Root Mean Square Error of 0.25, aiding further research in CFRP reinforcement technology.

Article Abstract

Carbon Fiber Reinforced Polymer (CFRP) can be used to reinforce steel structures depending on its high strength and lightweight resistance. To analyze and evaluate the load-carrying capacity of CFRP-reinforced steel structures. This study uses the Finite Element Analysis (FEA) and the experimental tests combined to investigate the influence that the reinforcement patterns and the relevant parameters have on the load-carrying capacity. We made specimens with different reinforcement patterns. Take the steel beam specimen with full reinforcement as an example. Compared with the load-carrying capacity of the steel beam reinforced by two-layer CFRP cloth, that respectively increases by 5.16% and 11.1% when the number of the CFRP cloth increases to four and six, respectively. Based on a specimen set consisting of CFRP-reinforced steel structures under different reinforcement patterns, the random forest algorithm is used to develop an evaluation model for the load carrying. The performance test results show that the MAE (Mean Absolute Error) of the evaluation model can reach 0.12 and the RMSE (Root Mean Square Error) is 0.25, presenting a good prediction accuracy, which lays a solid foundation for the research on the CFRP-based reinforcement technology and process.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11435699PMC
http://dx.doi.org/10.3390/polym16182678DOI Listing

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