Modeling and optimization of a trench layer location around a pipeline using artificial neural networks and particle swarm optimization algorithm

• The mesh free LRBF-DQ method was used to solve the governing equations.
• The artificial neural networks was used to predict liquefaction around a pipeline.
• The PSOA was used to find the optimum positions of a trench layer around a pipeline.

The main objective of the present work is to utilize particle swarm optimization algorithm (PSOA) integrated with feed-forward multi-layer perceptron (MLP) type of artificial neural networks (ANN) to find the optimum positions of a trench layer around a pipeline in order to obtain the minimum liquefaction potential. The mesh free local radial basis function differential quadrature method (LRBF-DQ) was used to solve the governing equations of seismic accumulative excess pore pressure containing pore pressure source term. This data was used to train the ANN using back propagation weight update rule. Then the trained ANN predicts the liquefaction potential and PSOA was used to find the best location of the trench layer. The results obtained by the MATLAB codes of LRBF-DQ, ANN and PSOA are showed that there was a linear relation between the location of the pipeline and the optimum location of the trench layer. Moreover the minimum liquefaction potential has been occurred when the trench layer placed beneath of the pipeline.