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Multi-objective optimization of properties on polymer fiber-reinforced desulfurization gypsum-based composite cementitious materials
Graphical abstract Display Omitted
Highlights The polynomial regression models from RSM were utilized to predict performances of PFRDGCMs. Effects of epoxy resin, calcium carbonate whisker and polyester fiber were clarified. The enhancing mechanism of PFRDGCM performances was discussed by microscopic analysis. The multi-objective optimization for properties of PFRDGCMs was realized by RSM.
Abstract Epoxy resin, calcium carbonate whisker and polyester fiber were mixed into desulfurized gypsum-based composite cementitious material (DGCM) to prepare polymer fiber-reinforced desulfurization gypsum-based composite cementitious material (PFRDGCM). Based on the response surface methodology (RSM) experimental design, the reliability of polynomial regression models was discussed, and the effects of factor interaction on properties and microscopic morphology of specimens were investigated. Additionally, the optimum mixture ratio of PFRDGCM was determined by the numerical function and verified by experimental results. The results showed that quadratic polynomial models accurately predicted properties of PFRDGCM and reflected the effects of single factor and multi-factor interaction on the compressive strength, water resistance, and durability of PFRDGCM. Moreover, the optimum PFRDGCM ratio for epoxy resin, calcium carbonate whisker, and polyester fiber was 5.49%, 1.02%, and 1.34 %, respectively. The measured test value of compressive strength, water absorption, softening coefficient, dry-wet strength coefficient, and freeze–thaw strength coefficient were 31.79 MPa, 3.58, 0.70, 1.16, and 1.14, respectively, with a relative error value less than 10 %, indicating that the RSM models can provide a reference for the multi-objective optimization of PFRDGCM.
Multi-objective optimization of properties on polymer fiber-reinforced desulfurization gypsum-based composite cementitious materials
Graphical abstract Display Omitted
Highlights The polynomial regression models from RSM were utilized to predict performances of PFRDGCMs. Effects of epoxy resin, calcium carbonate whisker and polyester fiber were clarified. The enhancing mechanism of PFRDGCM performances was discussed by microscopic analysis. The multi-objective optimization for properties of PFRDGCMs was realized by RSM.
Abstract Epoxy resin, calcium carbonate whisker and polyester fiber were mixed into desulfurized gypsum-based composite cementitious material (DGCM) to prepare polymer fiber-reinforced desulfurization gypsum-based composite cementitious material (PFRDGCM). Based on the response surface methodology (RSM) experimental design, the reliability of polynomial regression models was discussed, and the effects of factor interaction on properties and microscopic morphology of specimens were investigated. Additionally, the optimum mixture ratio of PFRDGCM was determined by the numerical function and verified by experimental results. The results showed that quadratic polynomial models accurately predicted properties of PFRDGCM and reflected the effects of single factor and multi-factor interaction on the compressive strength, water resistance, and durability of PFRDGCM. Moreover, the optimum PFRDGCM ratio for epoxy resin, calcium carbonate whisker, and polyester fiber was 5.49%, 1.02%, and 1.34 %, respectively. The measured test value of compressive strength, water absorption, softening coefficient, dry-wet strength coefficient, and freeze–thaw strength coefficient were 31.79 MPa, 3.58, 0.70, 1.16, and 1.14, respectively, with a relative error value less than 10 %, indicating that the RSM models can provide a reference for the multi-objective optimization of PFRDGCM.
Multi-objective optimization of properties on polymer fiber-reinforced desulfurization gypsum-based composite cementitious materials
An, Hongfang (author) / Wang, Lingling (author) / Lv, Fangtao (author) / Fu, Rusong (author) / Lu, Yuexian (author) / Kong, Dewen (author)
2023-01-29
Article (Journal)
Electronic Resource
English
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