Neural Network Application Overview in Prediction of Properties of Cement-based Mortar and Concrete
نویسندگان
چکیده
Neural networks have recently been broadly used in civil engineering applications due to their versatile capability as a simulator for the complex behavior of many problems (Adeli 2001). Compressive strength of cement mortar is mainly affected by water/cement ratio and aggregate/cement ratio. Recently, high performance concrete has been actively studied, however it is more difficult to predict the properties of high performance concrete. The neural networks (NN) have been considered as a method to solve very complex problems by using interconnected computing elements. This paper summarizes feasibility studies of neural network application for investigating complex non-linear interactions between various variables in complex concrete performance. It can be concluded from the investigation that the application of NNs in the field of concrete material can be more user-friendly and more precise model, and helps prevent some problems like corrosion, workability loss, strength loss, creep, and shrinkage, which are related to durability and safety of concrete.
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