Abstract
The tectonic fault, which is one of the most common geohazards in field, poses great threat to buried pipe segments. Pipes will process to buckling or fracture due to large strain induced by continuously increasing ground displacements during earthquakes. Therefore, it is imperative to conduct safety analysis on pipes which are buried in seismic areas for the sake of ensuring normal operation. However, the highly nonlinearity of pipe response restricts the proceeding of reliability assessment. In this study, a hybrid procedure combining finite element method and artificial neural network is proposed for reliability-based assessment. First of all, the finite element model is developed on ABAQUS platform to simulate pipe response to strike-slip fault displacements. Thus, the strain demand value (the peak strain value obtained by finite element model in each design case) can be collected for database establishment, which is the preparation for neural network training. Thoroughness of the strain demand database can be achieved by a fully comprehensive calculation with consideration of influencing factors involving pipe diameter and wall thickness, operating pressure, magnitude of fault displacement, intersection angle between pipeline and fault plane, and characteristic value of backfill mechanics. Sequentially, Back Propagation Neural Network (BPNN) with double hidden layers is trained based on the developed database, and the surrogate strain demand prediction model can be obtained after accuracy verification. Hence, the strain-based limit state function can be respectively determined for tensile and compressive conditions. The strain capacity term is simply assumed based on published papers, the strain demand term is naturally superseded by the surrogate BPNN model, and Monte Carlo Simulation is employed to compute the probability of failure (POF). At last, the workability of the proposed approach is tested by a case study in which basic variables are referred to the Second West-to-East natural gas transmission pipeline project. It indicates that ANN is a good solver for reliability problems with implicit limit state functions especially for highly nonlinear problems. The proposed method is capable of computing POFs, which is an exploratory application for reliability research on pipes withstanding fault displacement loads.
Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis
