The prediction of damage condition in regards to damage factor influence of light structures on expansive soils in Victoria, Australia
Abstract
This paper proposes a neural network model using genetic algorithm for a model for the prediction of the damage condition of existing light structures founded in expansive soils in Victoria, Australia. It also accounts for both individual effects and interactive effects of the damage factors influencing the deterioration of light structures. A Neural Network Model was chosen because it can deal with 'noisy' data while a Genetic Algorithm was chosen because it does not get 'trapped' in local optimum like other gradient descent methods. The results obtained were promising and indicate that a Neural Network Model trained using a Genetic Algorithm has the ability to develop an interactive relationship and a Predicted Damage Conditions Model.
References
[2] AS2870-1996 Residential slabs and footings-construction. Standards Australia International, Australia, 1996.
[3] J.P. Bigus. Data Mining with Neural Networks - Solving Business Problems from Application Development to Decision Support. McGraw-Hill, New York, 1996.
[4] J.H. Chou, J. Ghaboussi. Structural damage detection and identification using Genetic Algorithm. Computers and Structures, 79: 1335-1353, 2001.
[5] H. Demuth, M. Beale. Neural network toolbox for use with Matlab: User's Guide - Version 4. The MathWorks, 2001.
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This work is licensed under the Creative Commons Attribution 4.0 International CC BY 4.0.
