Abstract
The contribution to global CO2 emissions from concrete production is increasing. In this paper, the effect of concrete mix constituents on the properties of concrete and CO2 emissions was investigated. The tested materials used 47 mixtures, consisting of ordinary Portland cement (OPC) type I, coarse aggregate, river sand and chemical admixtures. Response surface methodology (RSM) and particle swarm optimisation (PSO) algorithms were employed to evaluate the mix constituents at different levels simultaneously. Quadratic and line models were produced to fit the experimental results. Based on these models, the concrete mixture necessary to achieve optimum engineering properties was found using RSM and PSO. The resulting mixture required to obtain the desired mechanical properties for concrete was 1.10-2.00 fine aggregate/cement, 1.90-2.90 coarse aggregate/cement, 0.30-0.4 water/cement, and 0.01-0.013 chemical admixtures/cement. Both methods had over 94% accuracy, compared to the experimental results. Finally, by employing RSM and PSO methods, the number of experimental mixtures tested could be reduced, saving time and money, as well as decreasing CO2 emissions.
Highlights
One of the most utilised construction materials in the world is concrete
Very little effect can be achieved in final model equations, using another factors instead of materials quantities can led to the similar results in the final models equations of design of experiments (DOE) and particle swarm optimisation (PSO) methods
The five main control factors in the DOE and the PSO methods included the material quantities of cement, coarse aggregate, fine aggregate, the water-cement ratio, and the chemical admixtures/superplasticiser (SP)
Summary
The DOE method, using a response surface methodology (RSM) technique defines a suitable model necessary to create a relationship between the factors and the various responses (9, 10, 12). The PSO method is based on a randomly initialised population It can solve engineering problems using very few parameters, avoiding trial and error, to find the appropriate coefficients of the proposed model. Factorial design of experiments (DOE) and PSO were applied to evaluate several factors in different concrete mixtures. A 47 mixture design was used in the DOE program and the PSO method solved the appropriate equations required to assess concrete strength, splitting strength, and flexural strength. The equations can be used in future to determine the required compressive strength, splitting strength and flexural strength of concrete, thereby saving time and reducing concrete material waste resulting from a number of trial and error mixtures. The prediction/optimisation was conducted to estimate the cement content recipe in order to produce the required performance. Predicting the Mechanical Properties of Concrete Using Intelligent Techniques to Reduce CO2 Emissions 3
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