Abstract
One of the problems of optimization of concrete is to formulate a mathematical equation that shows the relationship between the various constituents of concrete and its properties. In this work, modelling of the compressive strength of concrete admixed with metakaolin was carried out using the Gene Expression Programming (GEP) algorithm. The dataset from laboratory experimentation was used for the analysis. The mixture proportions were made of three different water/binder ratios (0.4, 0.5, and 0.6), and the grades of concrete produced were grade M15 and M20. The compressive strength of the concrete was determined after 28 days of curing. The parameters used in the GEP algorithm are the input variables which include cement content, water, metakaolin content, and fine and coarse aggregate, while the response was designated as the compressive strength. The model was trained and tested using the parameters. The R-square value from the GEP algorithm was compared with the use of conventional stepwise regression analysis. With a coefficient of determination (R-square value) of 0.95, the GEP algorithm has shown to be a good alternative for modelling concrete compressive strength.
Highlights
Ere are other properties expected of concrete other than high performance, such as workability, strength, and durability at all times
Akin and Abejide [7] adopted the use of Gene Expression Programming (GEP) in modelling the compressive strength of concrete produced by partially replacing Portland cement with ground-granulated blast furnace slag
With the increase in the use of the metakaolin above 10% replacement for all the w/b ratios of 0.4, 0.5, and 0.6, it is seen that the strength of the concrete was reducing, which validates the findings of Murali and Sruthee [23] that the percentage of metakaolin such as 5 and 10% showed considerable increase in the strength characteristics of the concrete relative to the conventional concrete type
Summary
Gene expression programming (GEP) is an evolutionary algorithm that generates computer programmes or models. E GEP gene in equation (1) can be expressed in a mathematical form as follows: x1 − x2 + sin 3 + x1. Unlike the parse-tree representation in canonical genetic programming, GEP uses a fixed length of character strings to represent solutions to the problems, which are afterwards expressed as parse-trees (called “expression tree” in GEP) of different sizes and shapes when evaluating their fitness [17]. One of the advantages of the GEP technique is that the creation of genetic diversity is extremely simplified as genetic operators work at the chromosome level. Another strength of GEP is that the unique multigenic nature allows the evolution of more complex programs composed of several subprograms [18,19,20]. E schematic diagram in Figure 2 shows the whole process of a GEP algorithm from the start to end
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