Seismic Behavior of Concrete Filled Circular Steel Tubular Columns Based on Artificial Neural Network

Abstract:

Artificial neural network (ANN) is self-adaptability, fault toleration and fuzziness. It is suitable to solve the seismic properties of high strength reinforced concrete columns with concrete filled steel tube core (HRCCFT). A three-layer back-propagation network model is build up to study the seismic properties of HRCCFT. The model is trained according to 30 sets of experimental data. The network convergence is fast. The model is verified by 8 groups of experimental data, the results show the predicted values of displacement ductility are in good agreement with test values. The precision of model is better than that of formula from other reference. This method is good enough to be used as an auxiliary method for structure design.

• Periodical: Advanced Materials Research (Volume 502)
• Pages: 189-192
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.502.189
• Online since: April 2012
• Authors: Hua Wei, Yu Du, Hai Jun Wang
• Keywords: Artificial Neural Network (ANN), Concrete Filled Circular Steel Tubular Column, Optimization

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DOI: 10.12989/sem.2005.19.6.653

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