Artificial neural network is generally information processing system and a computer program that imitates human brain neural network system. By entering the information from outside, artificial neural network can be trained on examples related to a problem, so that modeling of the problem is provided. In this study, compressive strength, Poisson ratio of the lightweight concrete specimens, which have different natural lightweight aggregates, were modeled with artificial neural network. The data which were provided by artificial neural network model were compared with the data obtained from experimental study and a good agreement was determined between the results.
In this study, some physical and mechanical performances of articial aggregated lightweight concretes were compared. Special empirical models were developed to estimate the elasticity modulus of lightweight aggregate concrete (LWAC). Five dierent natural aggregates and one articial lightweight aggregate material were used throughout the research. Mixture proportions were kept as constant values in all concrete mixtures. All mixtures were cast into cubic, prismatic and cylindrical concrete standard moulds and they were cured at the same curing conditions. A series of physical and mechanical properties, such as density, compressive strength and elasticity modulus for LWAC were experimentally determined. According to the research ndings a few empirical models were statistically developed for estimating the elasticity modulus and Poisson's ratio of LWAC and a new diagram practically to be used for estimating the Poisson's ratio of LWAC was also proposed.
In this study, the effects of using andesite powder wastes-produced from natural stone factories as mineral additives in concrete manufacturing-on the compressive strength of concrete were modeled using an Artificial Neural Network (ANN). To achieve this, cement mixtures were produced by using waste andesite powder (WAP) mixture at ratios of 0% (control), 10%, 15% and 20%. The effects of curing time were investigated by preparing specimens at 28 and 90 days. The training set was formed by using cement and the specified WAP mixtures and curing time parameters. It was observed that the results obtained from the training ANNs were consistent with the experimental data.
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