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• W4313596476 endingPage "e01831" @default.
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• W4313596476 abstract "The industrial revolution brought environmental degradation to light. Concrete and plastic degrade the ecosystem and cause unsustainable development. Academic and industrial sectors are interested in lowering carbon emissions associated with concrete. Meanwhile, global sand scarcity worries environmentalists. To reach sustainable development goals, cement and fine aggregate must be substituted with other abundant waste/natural materials. This study aimed to develop a green concrete by utilizing plastic waste and creating modelling tool for predicting the mechanical properties of plastic concrete. Different composition of silica fume and superplasticizers substituted fine aggregate and cement in both irradiated (treated) and regular (untreated) plastic concrete. Compressive strength (fc’) and split tensile strength (fst) of the resulting concrete were studied. Moreover, from literature data, 320 data points each for fc' and fst were used to train gene expression programming (GEP) models. Models’ accuracy was evaluated employing various statistical measures. Regular plastic waste concrete has demonstrated a lower fc’ and exhibited anomalous behavior for fst. While irradiated plastic waste concrete has demonstrated improved mechanical characteristics, comparatively. Correlation coefficients using GEP models for fc’ and fst were found to be 0.92 and 0.88, respectively. Furthermore, sensitivity analysis revealed that plastic was the most significant in the GEP model’s development. K fold validation was employed to prevent over-fitting of the models. GEP provides an empirical expression for each outcome to predict future database features. This research improves green concrete's long-term sustainability by reducing carbon emissions and alleviating fine aggregate scarcity." @default.
• W4313596476 created "2023-01-06" @default.
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• W4313596476 date "2023-07-01" @default.
• W4313596476 modified "2023-09-26" @default.
• W4313596476 title "Plastic concrete mechanical properties prediction based on experimental data" @default.
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• W4313596476 doi "https://doi.org/10.1016/j.cscm.2023.e01831" @default.
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