Open AccessNeural model of projecting flexural strength of cement concrete intended for airfield pavements
Author(s) -
Małgorzata Linek
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/664/1/012013
Subject(s) - artificial neural network , extrapolation , aggregate (composite) , constant (computer programming) , flexural strength , backpropagation , allowance (engineering) , cement , structural engineering , computer science , mathematics , engineering , materials science , mathematical analysis , composite material , mechanical engineering , artificial intelligence , programming language
This work present to the mathematical model in the form of Artificial Neural Network (ANN), intended for projecting concrete flexural strength. Input data was classified according to the type of component material and its content in concrete mix (cement contents, coarse aggregate, fine aggregate, water and admixtures). In order to determine mathematical model, a multilayer, one-way perceptron network was used, recursion network with sigmoidal neurons. The model assumes that neurons are gathered in some layers (one input layer, hidden layers and one output layer). The conducted cross-section of the impact of variable parameters values (learning constant α and momentum values η ) on the accuracy of representation of flexural strength was analyses. Assessment criterion was assumed as consideration the lowest mistake level and 100% compliance. According to the obtained results ANN was assumed to be the best representing network for constant value of momentum 0.3, learning constant of 0.04 and 9 neurons in a hidden layer. Very good coincidence of component models with experiment results was achieved. At testing stage, the coincidence was achieved at the level of 99.25%, in case of the assumed network structure. During model verification by means of experimental results, the average coincidence was 99.68%.