Predicting pipeline burst pressures with machine learning models

Establishing an accurate model to predict burst pressure is desired, which has been developed for decades. Although various models have been developed, errors unavoidably appear in the prediction of burst pressures because of the uncertainty in both input variables and nonlinear relationship of such variables to the burst pressure. Consequently, machine learning models, which is a data-driven approach, are potential alternatives. In this paper,...