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

Abstract

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.