科研之友
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摘要
An estimation of scour depth is a prerequisite for the efficient foundation design of the abutment of a bridge. Many equations and models are available in literature for predicting abutment scour depth based on experimental and theoretical approaches. It is still difficult to obtain a general model to provide accurate estimation of scour depth due to the presence of complex flow structure at the base of the abutment. The artificial neural network (ANN) is generally considered as an alternative approach to the experimental and theoretical methods. It acts as a universal function approximator and consequently it is very useful in modelling problems wherein the relationship between dependent and independent variables is poorly understood. In the present study, ANN models with commonly used algorithm of training have, therefore, been developed for the equilibrium scour depth prediction at bridge abutments using a sizable amount of laboratory data from various sources. The ANN models of various training schemes have been found to be better than the conventional regression model based on the performance parameters for both calibration as well as validation set of data. The present study also indicates that the prediction based on the raw data (dimensional) is better than those based on non-dimensional parameters.
- 出版日期2008-3
