Stress intensity factor prediction on offshore pipelines using surrogate modeling techniques

This study aims to predict the accurate stress intensity factor (SIF) of a crack propagating in offshore piping, which is one of the crucial factors used to assess the remaining fatigue life (RFL) of offshore pipelines. Four soft computing techniques are examined and evaluated in the modeling of SIF of a crack propagating in topside piping, as an inexpensive alternative to the finite element methods (FEM). In the training and testing stages, developed models of Functional Network (FN), Emotional Neural Network (ENN), Relevance Vector Machine (RVM), and minimax probability machine regression (MPMR) for SIF prediction were used and compared. Also, a comparative study was conducted for the developed models with the Adaptive Gaussian Process Regression Model (AGPRM). The load, crack depth, and half crack length have been adopted as input variables of the models, and the output variable is the SIF. All variables were simulated and determined based on the flat-plate FEM model. The analysis confirms that the RVM and MPMR models are superior to FN and ENN models for SIF prediction in the training and testing stages. In addition, the RVM and MPMR models show better than AGPRM for the prediction of pipe SIF in the testing stage. The MPMR accurately outperforms all models within 1.31% prediction error, and the majority of its error values at 99% confidence level fall within ±29.64 MPamm.