scholarly journals Strength Prediction of High-Strength Concrete by Fuzzy Logic and Artificial Neural Networks

2014 ◽  
Vol 26 (11) ◽  
pp. 04014079 ◽  
Author(s):  
Gökmen Tayfur ◽  
Tahir Kemal Erdem ◽  
Önder Kırca
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
High Strength Concrete ◽  
High Strength ◽  
Strength Prediction ◽  
Strength Concrete ◽  
Artificial Neural

Artificial Neural Networks for the Spalling Classification & Failure Prediction Times of High Strength Concrete Columns

2014 ◽  
Vol 5 (3) ◽  
pp. 203-214 ◽  
Author(s):  
John McKinney ◽  
Faris Ali
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Test Data ◽  
Experimental Test ◽  
High Strength Concrete ◽  
Failure Prediction ◽  
High Strength ◽  
Concrete Columns ◽  
Strength Concrete ◽  
High Strength Concrete Columns

This paper presents the results from two supervised Artificial Neural Networks (ANN) developed for the spalling classification and failure prediction of high strength concrete columns (HSCC) subjected to fire. The experimental test data used for the ANN are based on the HSCC tests undertaken at the Fire Research Laboratories at the University of Ulster. 80% of the chosen experimental test data was used to train the network with the remaining 20% used for testing. In the spalling classification example the key ANN input parameters were; furnace temperature, restraint, loading level, force, spalling degree, failure time and spalling type. This was also the case for the failure prediction example except for spalling type. The networks were trained using the resilient propagation algorithm. A 6-10-3 and 5-10-1 ANN architecture gave the best results for the classification and failure prediction times respectively. The results demonstrate that HSCC can be assessed using ANN.

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