Prediction of vertical displacement for a buried pipeline subjected to normal fault using a hybrid FEM-ANN approach

Fault movement during earthquakes is a geotechnical phenomenon threatening buried pipelines and with the potential to cause severe damage to critical infrastructures. Therefore, effective prediction of pipe displacement is crucial for preventive management strategies. This study aims to develop a fast, hybrid model for predicting vertical displacement of pipe networks when they experience faulting. In this study, the complex behavior of soil and a buried pipeline system subjected to a normal fault is analyzed by using an artificial neural network (ANN) to generate predictions the behavior of the soil when different parameters of it are changed. For this purpose, a finite element model is developed for a pipeline subjected to normal fault displacements. The data bank used for training the ANN includes all the critical soil parameters (cohesion, internal friction angle, Young’s modulus, and faulting). Furthermore, a mathematical formula is presented, based on biases and weights of the ANN model. Experimental results show that the maximum error of the presented formula is 2.03%, which makes the proposed technique efficiently predict the vertical displacement of buried pipelines and hence, helps to optimize the upcoming pipeline projects.