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- W2340667169 abstract "Article history: Received: 17 August 2012 Accepted: 19 May 2013 This paper presents a new model for predicting the compressive strength of steel-confined concrete on circular concrete filled steel tube (CCFST) stub columns under axial loading condition based on Artificial Neural Networks (ANNs) using a wide range of experimental investigations. Based on the previous theoretical and experimental studies the input variables considered in developing the ANNs model are outer diameter of column, compressive strength of unconfined concrete, length of column, wall thickness and tensile yield stress of steel tube. After the learning step, the neural network can be extracted the relationships between the input variables and output parameter. The criteria for stopping the training of the networks are Regression values and Mean Square Error. After constructing networks with constant input neurons but with different number of hiddenlayer neurons, the best network was selected. The neural network results are compared with the existing models which showed the results are in good agreement with experiments." @default.
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- W2340667169 date "2014-02-01" @default.
- W2340667169 modified "2023-09-26" @default.
- W2340667169 title "Compressive Strength of Confined Concrete in CCFST Columns" @default.
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- W2340667169 doi "https://doi.org/10.22075/jrce.2014.12" @default.
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