Prediction of concrete strength with artificial neurol networks by using physical properties of aggregates

The effect of physical and mechanical properties of aggregate on concrete strength can only be determined with experiments. Experimental studies may take a long time to complete, create economic burden, require materials and technical personnel. Artificial Neural Network has shown us that these losses and requirements can be reduced. In this study, aggregate with different roots and properties were collected indifferent locations of Marmara region and their physical properties were specified. Acquired aggregate was used to create concrete and then their compressive strength was determined in laboratory experiments. The results of this experiment were organised and changed to a format that can be used as model input. Aggregate is one of the main raw materials used in the mixed concrete production. Therefore, aggregate quality carries a great importance for the mixed concrete producers. Grain size, grain shape, organic and alkali matter contents and mineralogical compositions are important material properties on the industrial evaluation of the aggregate deposits. Determination of the effect of the physical and mechanical properties of aggregates that have considerable effect on concrete strength is only possible by conducting a series of experimental studies.These studies take long time and mostly are not economic.Therefore, different methods formed by utilizing the experimental studies done before are used to determine the strength characteristics.In this study, the impact of physical properties of aggregates and using 7-day and 28-day cured concrete has been researched. Then a model has been developed by using artificial neural network which the results obtained from the tests. The values determined experimentally have been estimated by developing models in Artificial Neural Network method.It has been observed at the comparisons that the training and test results in the models can be estimated. The developed model indicates that without making an experiment, providing input on properties of aggregate allows us to estimate concrete compressive strength. Artificial Neural Network model estimations and linear regression results are also compared. According to this comparison between Artificial Neural Network results and experiment data; it was seen that Artificial Neural Network result has a really small error margin of 2.8% percentage in difference. Artificial Neural Network results are observed to be more successful then linear regression results and estimations are close to 97%.