Steel columns under fire: a neural network based strength model

Authors:
Zhiye Zhao
Affiliations:
School of Civil and Environmental Engineering, Nanyang Technological University, Singapore, Singapore
Venue:
Advances in Engineering Software
Year:
2006

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Abstract

This paper presents a strength model of steel columns under elevated temperatures using the artificial neural network. The many influencing parameters make it difficult to build an analytical steel strength model. Being a flexible model building method, the artificial neural network is an ideal tool to construct the complex relationship between the input and the output parameters accurately. A hybrid neural network, which combines the sigmoid neurons and the radial basis function neurons at the hidden layer, is proposed to better map the input-Output relationship both locally and globally. The use of the genetic algorithm approach in searching the best-hidden neurons makes the hybrid neural network less likely to be trapped in local minima than the traditional gradient-based search algorithms. The genetic algorithm based hybrid neural network is applied to model the strength of steel columns under fire. The neural network results are compared with the modified Rankine formula.