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Volume 55 Issue 8
Aug.  2025
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Article Contents
CHEN Peng, WANG Shirui, XU Haiyan, WEI Wei, GAO Wei, HUANG Bing. Research on Multi-Objective Optimization of Fiber-Reinforced Concrete Based on Response Surface Method[J]. INDUSTRIAL CONSTRUCTION, 2025, 55(8): 226-235. doi: 10.3724/j.gyjzG25051101
Citation: CHEN Peng, WANG Shirui, XU Haiyan, WEI Wei, GAO Wei, HUANG Bing. Research on Multi-Objective Optimization of Fiber-Reinforced Concrete Based on Response Surface Method[J]. INDUSTRIAL CONSTRUCTION, 2025, 55(8): 226-235. doi: 10.3724/j.gyjzG25051101

Research on Multi-Objective Optimization of Fiber-Reinforced Concrete Based on Response Surface Method

doi: 10.3724/j.gyjzG25051101
  • Received Date: 2025-05-11
    Available Online: 2025-10-24
  • In order to realize the multi-objective optimal design of the mix proportion for fiber-reinforced concrete, this paper adopted the response surface method to investigate the effects of the contents of polypropylene fiber, silica fume, and microbeads, as well as their interactions, on the workability and mechanical properties of fiber-reinforced concrete. Furthermore, it carried out multi-objective optimization analysis to determine the optimal mix proportion. The results showed that the regression model established in this paper exhibited remarkable fitting accuracy and credibility. Among the interactions of various factors, the most significant effects on slump flow, 28-day compressive strength, and flexural strength of fiber-reinforced concrete were attributed to the interactions between silica fume and microbeads, polypropylene fiber and silica fume, and polypropylene fiber and microbeads, respectively. The multi-objective optimization analysis revealed that the optimal mix proportion parameters for fiber-reinforced concrete were as follows: polypropylene fiber content of 1.62%, silica fume content of 8.56%, and microbead content of 10%. Under these conditions, the slump flow, 28-day compressive strength, and flexural strength reached 655 mm, 64.1 MPa, and 13.4 MPa, respectively, meeting the target response requirements. This study verifies the feasibility of using the response surface methodology for multi-objective optimization of mining concrete materials.
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