Using network to estimate the screening effect of surface waves by concrete in-filled trenches

This paper presents a back-propagation neural network model to estimate the screening effects of vertical amplitude of surface waves by 3-dimensional in-filled trenches. A total of six input parameters are selected by using genetic algorithm with important parameter analysis and pre-processing transfer functions. The parameters are trench dimension, distance between vibrating foundation and trenches, infilled material property, etc. The number of hidden layer nodes in the network is determined by Cascade Correlation learning processing. The learning parameters of network are determined by Extended-Delta-Bar-Delta algorithm to regulate learning-rate and momentum constant automatically. The output parameter of network is average vertical amplitude reduction rate. The results show that the neural network model is a very good approach in estimating the screening effect of vertical amplitude of seismic wave.