Artificial Neural Networks in Prediction of Concrete Strength Reduction Due to High Temperature

2005 ◽  
Vol 102 (2) ◽  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
High Temperature ◽  
Concrete Strength ◽  
Strength Reduction ◽  
Artificial Neural

Evaluation Compressive Strength of Cement-Limestone-Slag Ternary Blended Concrete Using Artificial Neural Networks (ANN) and Gene Expression Programming (GEP)

2020 ◽  
Vol 837 ◽  
pp. 119-124
Author(s):  
Xiao Yong Wang
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gene Expression Programming ◽  
Concrete Strength ◽  
Correlation Coefficients ◽  
Strength Development ◽  
Ann Model ◽  
Artificial Neural ◽  
Ternary Blended Concrete

Limestone and slag blended concrete is an innovative concrete which belongs to the family of limestone calcined clay cement (LC3) concrete. Strength is an important property of structural concrete. This study shows artificial neural networks (ANN) and gene expression programming (GEP) models for predicting strength development of limestone and slag blended concrete. ANN model consists of an input layer, a hidden layer, and output layer. GEP model consists of the sum of three expression trees. The input parameters of ANN and GEP models are mixtures and ages. The output parameter is a strength. The correlation coefficients of ANN and GEP model are 0.99 and 0.98, respectively. Both ANN and GEP model can produce prediction results of the strength of ternary blended concrete reliably.

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