Ant Colony Optimization Algorithms Optimizes Neural Networks on Prediction of Soil Liquefaction
Date Issued
2009
Date
2009
Author(s)
Li, Yuan-Hung
Abstract
Geotechnical problems have many unconfirmed and irregular nonlinear characteristics. The causalities are complex and can not be displayed by simple mathematic formula. The prediction of soil liquefaction is usually estimated by formula of experience to decide whether it correspond to safety coefficient. The soil parameters may not be calculated only by simple experience rules because there are complicated relationships between the parameters which are mutual effect. We have to find a tool with nonlinear system to estimate the soil liquefaction problems. The multilayer structure of artificial neural networks is use to deal with the complex nonlinear problems. We establish the response mechanism by observing the input-output pairs.he study collects 208 observing data of earthquakes over the world. Classifying 70% to training set and 30% to testing set by random. Training the data using back- propagation neural networks which is optimized by ant colony optimization algorithms to find the best network parameters. Comparing the results of net output with the results of experience formula, the results of networks have better display.
Subjects
Neural Networks
Optimization
Soil Liquefaction
Type
thesis
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