Beton basınç dayanımı tahmini için yeni bir model

Concrete is the utmost chief material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients. In this thesis, effect of water, gravel, sand, blast furnace slag, plasticizer, and the effects of flay ash on concrete compressive strength was presented by using Long short-term memory (LSTM). Artificial recurrent neural network (RNN) architecture used in the field of deep learning. Unlike standard feedback neural networks, LSTM has feedback links. The concrete compressive strength is regression problem which several classical artificial intelligence and machine learning techniques applied to solve it. In this study, the model consist from eight input and one output which represented the concrete compressive strength.