Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Intelligent Proportion Optimization of SFRC Using Ensemble Learning: A Multi-Objective Predictive Framework
Steel fiber-reinforced concrete (SFRC) is formulated by incorporating short, slender steel fibers into standard concrete, thereby creating a composite material. This addition significantly mitigates the brittle nature of concrete, enhancing its strength, toughness, and durability. The application of SFRC not only improves structural safety and service life but also promotes the development of green building materials and efficient construction technologies. To optimize the mix proportion design of SFRC, key parameters such as steel fiber content, water, cement, sand, natural aggregates, and water reducer were collected. A neural network model was constructed to leverage its powerful nonlinear mapping capabilities, establishing an implicit relationship between the mix proportions and compressive strength. The trained model enables rapid prediction of SFRC compressive strength, while a genetic algorithm was employed to inversely search for the optimal mix proportions that meet target performance requirements, providing a novel approach and design strategy for the intelligent design of SFRC.
Intelligent Proportion Optimization of SFRC Using Ensemble Learning: A Multi-Objective Predictive Framework
Steel fiber-reinforced concrete (SFRC) is formulated by incorporating short, slender steel fibers into standard concrete, thereby creating a composite material. This addition significantly mitigates the brittle nature of concrete, enhancing its strength, toughness, and durability. The application of SFRC not only improves structural safety and service life but also promotes the development of green building materials and efficient construction technologies. To optimize the mix proportion design of SFRC, key parameters such as steel fiber content, water, cement, sand, natural aggregates, and water reducer were collected. A neural network model was constructed to leverage its powerful nonlinear mapping capabilities, establishing an implicit relationship between the mix proportions and compressive strength. The trained model enables rapid prediction of SFRC compressive strength, while a genetic algorithm was employed to inversely search for the optimal mix proportions that meet target performance requirements, providing a novel approach and design strategy for the intelligent design of SFRC.
Intelligent Proportion Optimization of SFRC Using Ensemble Learning: A Multi-Objective Predictive Framework
04.03.2025
doi:10.22158/asir.v9n1p171
Applied Science and Innovative Research; Vol 9, No 1 (2025); p171 ; 2474-4980 ; 2474-4972
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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