Application of ANN for predicting pore water pressure response in a shake table test

In this paper a proper assessment of pore water pressure has been done by using the shake table data. The pore water pressure ratio of a soil mass during an earthquake is dependent on both seismic and soil parameters. The impact of these soil variables on the pore water pressure ratio of soil is investigated through computational and knowledge based tools called neural networks. The artificial neural network is trained using actual shake table data. A forecast study is performed for pore water pressure in the shake table tank. The effect of different parameters such as frequency, no of cycles, void ratio, and depth of soil mass from ground level on pore water pressure ratio has been investigated. It has been found that the void ratio is having more effect on the pore water pressure while analyzing the shake table results.