Flexural buckling load prediction of aluminium alloy columns using soft computing techniques

Authors:
Abdulkadir Cevik;Nihat Atmaca;Talha Ekmekyapar;Ibrahim H. Guzelbey
Affiliations:
Department of Civil Engineering, University of Gaziantep, 27310 Gaziantep, Turkey;Gaziantep Vocational High School, University of Gaziantep, 27310 Gaziantep, Turkey;Department of Civil Engineering, University of Gaziantep, 27310 Gaziantep, Turkey;Department of Mechanical Engineering, University of Gaziantep, 27310 Gaziantep, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 4
Cited 1

Learning internal representations by error propagation

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Genetic programming: on the programming of computers by means of natural selection

Genetic programming: on the programming of computers by means of natural selection
Soft Computing and Fuzzy Logic

IEEE Software
Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence (Studies in Computational Intelligence)

Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence (Studies in Computational Intelligence)

Determination of scrap/supply probability curves for the mechanical properties of aluminium alloys in hot extrusion using a neural network-like approach

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.05

Visualization

Abstract

This paper presents the application of soft computing techniques for strength prediction of heat-treated extruded aluminium alloy columns failing by flexural buckling. Neural networks (NN) and genetic programming (GP) are presented as soft computing techniques used in the study. Gene-expression programming (GEP) which is an extension to GP is used. The training and test sets for soft computing models are obtained from experimental results available in literature. An algorithm is also developed for the optimal NN model selection process. The proposed NN and GEP models are presented in explicit form to be used in practical applications. The accuracy of the proposed soft computing models are compared with existing codes and are found to be more accurate.