Application Of Artificial Neural Network In Estimation Of Foundation Soil Liquefaction

Foundation soil liquefaction is an important problem in earthquake engineering, which comes through a complicate process. Liquefaction involves a great deal of influence 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 limits. 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 a quantitative and qualitative indexes.This thesis expounds fundamental principle and realization technique of Artificial Neural Network, and redacts Artificial Neural Network procedures:l)Adopting additional momentum algorithm, the thesis redacts Back-Propagation Network procedure to enhance training velocity, in which learning rate and momentum parameters are modulated self-adaptive during error corrections.2)Compared with RBF Network, it is concluded that learning algorithm is master key to improve properties of Artificial Neural Network.On the basis of indoor dynamic tri-axial test, this thesis set forth Artificial Neural Network models of estimation of liquefaction, which take versatile factors into account. Compared with traditional means, it is concluded:1) Artificial Neural Network can forecast more accurately and quickly than traditional means with reasonable data. The results show that the Artificial Neural Network model of estimation of liquefaction is scientific and effective.2) The Artificial Neural Network models can reveal internal relation between structural parameters and operation, and formularize the mapping 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 propositions to norm.