An introduction to computing with neural nets
Artificial neural networks: theoretical concepts
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Using neural networks to predict workability of concrete incorporating metakaolin and fly ash
Advances in Engineering Software - Civil-comp 2001
Neural networks modeling of shear strength of SFRC corbels without stirrups
Applied Soft Computing
Aseismic ability estimation of school building using predictive data mining models
Expert Systems with Applications: An International Journal
Genetic programming for predicting aseismic abilities of school buildings
Engineering Applications of Artificial Intelligence
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Neural networks have recently been widely used to model some of the human activities in many areas of civil engineering applications. In the present paper, the models in artificial neural networks (ANN) for predicting compressive strength of concretes containing metakaolin and silica fume have been developed at the age of 1, 3, 7, 28, 56, 90 and 180days. For purpose of building these models, training and testing using the available experimental results for 195 specimens produced with 33 different mixture proportions were gathered from the technical literature. The data used in the multilayer feed forward neural networks models are arranged in a format of eight input parameters that cover the age of specimen, cement, metakaolin (MK), silica fume (SF), water, sand, aggregate and superplasticizer. According to these input parameters, in the multilayer feed forward neural networks models are predicted the compressive strength values of concretes containing metakaolin and silica fume. The training and testing results in the neural network models have shown that neural networks have strong potential for predicting 1, 3, 7, 28, 56, 90 and 180days compressive strength values of concretes containing metakaolin and silica fume.