Soft methods in the prediction and identification analysis of axially compressed R/C columns
Abstract
Two problems are presented in the paper concerning axial loading of R/C columns: I) prediction of critical loads, II) identification of concrete strength. The problems were analyzed by two methods: A) Gaussian Processes Method, B) Advanced Back-Propagation Neural Network. The results of the numerical analysis are discussed with respect to numerical efficiency of the applied methods.
Keywords:
Gauss Processes Method (GPM), Advanced Back-Propagation Neural Network (ABPNN), Reinforced Concrete (R/C), axial loading, Success Ratio (SR)References
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[3] PEER (Pacific Earthquake Engineering Research Center). University of California, 2004, database.
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[5] H. Demuth, M. Beale. Neural Network Toolbox for use with MATLAB. User’s Guide version 3. Math Works Inc. Natick MA, 2004.
[6] J.P. Nabney. Netlab – Algorithms for Pattern Recognition. Springer, 2004.
[7] B. Miller. Neurocomputing in selected problems of structural identification and damage detection. Oficyna Wydawnicza Politechniki Rzeszowskiej, 2013.
[8] Z. Waszczyszyn, M. Słoński. Selected problems of artificial neural networks development. In: Z. Waszczyszyn, Ed., Advances of Soft Computing in Engineering, CISM Courses and Lectures, Vol. 512, Springer 2010.
