Kaya, MustafaKömür, M. AydınGürsel, Erçin2022-09-072022-09-0720222287-53012287-531Xhttps:/dx.doi.org/10.12989/acc.2022.13.6.411https://hdl.handle.net/20.500.12451/9674Aggregates mineralogical, and petrographic properties directly affect the mechanical properties of the produced high strength. This study is focused on the effects of magmatic, sedimentary, and metamorphic aggregates on the performance of high strength concrete. In this study, the effect of the mineralogical properties of aggregates on the compressive strength and modulus of elasticity of high-strength concrete was estimated by Artifical Neural Network (ANN). To estimate the compressive strength and elasticity modules, 96 test specimens were produced. After 28 days under suitable conditions, tests were carried out to determine the compressive strength and modulus of elasticity of the test specimens. This study also focused on the application of artificial neural networks (ANN) to predict the 28-day compressive strength and the modulus of elasticity of high-strength concrete. An ANN model is developed, trained, and tested by using the available test data obtained from the experimental studies. The ANN model is found to predict the modulus of elasticity, and 28 days compressive strength of high strength concrete well, within the ranges of the input parameters. These comparisons show that ANNs have a strong potential to predict the compressive strength and modulus of elasticity of high-strength concrete over the range of input parameters considered.eninfo:eu-repo/semantics/closedAccessAggregate Mineralogical PropertiesArtificial Neural NetworkCompressive StrengthHigh Strength ConcreteModulus of ElasticityArticle13641142210.12989/acc.2022.13.6.411Q2WOS:000823419600001Q2