Estimating the optimal mix design of silica fume concrete using biogeography-based programming
By Emadaldin Mohammadi Golafshani, Ali Behnood. Cement & Concrete Composites, 2019
148 citations recorded by the source corpus.
Abstract The use of silica fume in concrete mixtures has been dramatically increased in concrete industry, especially for achieving high strength concrete. An accurate model of estimating the compressive strength and optimal mix design of silica fume concrete can save in time and cost. In this study, the biogeography-based programming (BBP) was used as a symbolic regression method to predict the compressive strength of silica fume concrete, while the constrained biogeography-based optimization (CBBO) was used to estimate its optimal mix design. For this purpose, a comprehensive database was gathered from various published documents. From the collected data, about 75% of all data was employed to train the model, while the rest was used to verify the developed model. The amounts of cement, water, silica fume, coarse aggregate, fine aggregate, superplasticizer, as well as the maximum size of aggregate and concrete age were selected as the effective input variables of the model. The compressive strength of silica fume concrete was considered as the only output variable. The results show that the BBP model can be successfully used for the prediction of the compressive strength of silica fume concrete with acceptable accuracy. In addition, a graphical user interface was designed which allows the users to estimate the optimal mix design of silica fume concrete.
Subfields: Building and Construction, Civil and Structural Engineering
View Paper: https://www.semanticscholar.org/paper/e14e0e49fcfb673674bcdd165ca7c8ce89e52776
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Citations in the corpus
- Estimation of the dynamic modulus of asphalt concretes using random forests algorithm (2020, unknown): “However, it is not considered as an optimal solution since defining the structure of the regression model is required in advance (Alavi and Gandomi 2011, Golafshani and Behnood 2018a, Golafshani and Behnood 2019).”
- Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model (2021, methodology): “…although the effective-ness of the RF model is clearly shown in this study, the could be conducted based on the investigations of Golafshani and Behnood [39], as well as Behnood et al. [32]. updated database is expected to be the most significant number of samples with the broadest range of values.”