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Application Of Artificial Neural Network In Estimation And Grade Evaluation Of Foundation Soil Liquefaction

Posted on:2003-04-15Degree:MasterType:Thesis
Country:ChinaCandidate:W J RenFull Text:PDF
GTID:2132360062495428Subject:Structural engineering
Abstract/Summary:PDF Full Text Request
Foundation soil liquefaction is an important problem in earthquake engineering, which comes through a complicate process. Predictions of liquefaction include prediction of probability and damage quantity. Liquefaction involves in a great deal of influencing factors, which have strong randomness and reciprocal nonlinearity. Based on macroscopical earthquake calamities and laboratory tests, traditional methods about estimation and grade evaluation of liquefaction are inducted by means of generalization, analyses and statistics, which have some practicability and some limitation. This thesis analyses and assesses traditional methods, and brings forward the necessity of establishing models of estimation and grade evaluation of liquefaction, which concern minor factitious influencing factors and embrace aquantitative and qualitative indexes.This thesis expounds fundamental principle and realization technique of Artificial Neural Network and Genetic Algorithm, and redacts Artificial Neural Network procedures.-(l)Adopting batch processing high-speed algorithm, the thesis redacts Back-Propagation Network procedure to enchance training velocity, in which learning rate and momentum parameters are modulated self-adaptably during error correction.(2)Combining secondary Genetic Algorithm with Back-Propagation Network, the thesis redacts Genetic Neural Network procedure, which optimizes number of hidden node and weight value and threshold value simultaneously. The procedure overcomes blindness during search, avoids falling into localminimum and increases learning accuracy.(3)Combining secondary Genetic Algorithm with L-M algorithm in toolbox and thinking about effects of partial forecasting sample in adaptive value, the thesis redacts improved Genetic Neural Network procedure in order to pick up faculty of learning and generalization and running velocity.(4)Compared with RBF Network, it is concluded that learning algorithm is master key to improve properties of Artificial Neural Network.On the basis of measured data of earthquake liquefaction and damage quantity, this thesis set forth Artificial Neural Network models of estimation and grade evaluation of liquefaction, which take versatile factors into account. Compared with measured data and traditional means, it is concluded:(l)Artificial Neural Network can forecast more accurately and quickly than traditional means with reasonable data. The results show that the Artificial Neural Network models of estimation and grade evaluation of liquefaction is scientific and effective.(2)The Artificial Neural Network models can reveal internal relations between structural parameters and operation, and formularize the maping of input-output information. By means of computing relative contribution rate of single factor, the thesis not only test and verify rationality of conventional means, but also put forth proposition to norm.
Keywords/Search Tags:Artificial Neural Network, Genetic Algorithm, estimation of earthquake liquefaction, grade evaluation of earthquake liquefaction
PDF Full Text Request
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