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• W3131636996 endingPage "105443" @default.
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• W3131636996 abstract "Abstract Several million tons of demolition and construction (D&C) wastes are being produced worldwide. Brick waste is one of the eminent D&C wastes and several models have been performed on recycling brick waste to produce environmentally friendly concrete. This study develops three fuzzy-metaheuristic ensembles, based on adaptive neurofuzzy inference system (ANFIS) with a fuzzy c-mean clustering approach to forecasting the compressive strength of brick aggregate concrete (BAC). Such models incorporate ANFIS with particle swarm optimization (PSO), genetic algorithm (GA), and firefly algorithm (FFA). For the model development, 132 datasets were used for standalone and hybrid models. All models were trained and tested with 80% and 20% of the datasets, respectively. A k-fold cross-validation method has been applied to validate the generalization accuracy of the established models. A sensitivity analysis is also used for evaluating the influence of each input variable on the proposed techniques. Among the different designed and trained models, the results revealed that the hybrid ensemble models are more successful than ANFIS based fuzzy c-mean clustering approach in forecasting the 28-days compressive strength of BAC. The developed ANFIS-PSO model yields better prediction compared with the other two hybrid GA and FFA models so the correlation coefficient (R2), a root means square error (RMSE), and the mean absolute error (MAE) were acquired as 0.913, 0.057, and 0.032, respectively. A sensitivity analysis indicated that the content of cement and the specific gravity of brick aggregates played a significant role in the model output." @default.
• W3131636996 created "2021-03-01" @default.
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• W3131636996 date "2021-06-01" @default.
• W3131636996 modified "2023-10-16" @default.
• W3131636996 title "Fuzzy-metaheuristic ensembles for predicting the compressive strength of brick aggregate concrete" @default.
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• W3131636996 doi "https://doi.org/10.1016/j.resconrec.2021.105443" @default.
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