Damage Prediction of Underground Pipelines Subjected to Blast Loading

In the present time, the contribution of underground pipelines is of great significance. Considering the importance of underground pipelines and their susceptibility to explosion, the damage prediction and safety of buried pipelines have become very crucial. This study presents the development of artificial intelligence (AI) models to accurately predict damage in underground steel pipelines subjected to blast loading. For the development of AI models, hundreds of blast simulations were performed in ABAQUS/Explicit using Combined Eulerian–Lagrangian (CEL) approach. The overall efficiency of the developed artificial intelligence (AI) models was evaluated by analysing a set of performance indicators. Among the proposed models, artificial neural network exhibited the best performance in predicting the damage in pipeline. As a contribution, this study proposed an effective learning model for damage prediction in buried pipelines subjected to subsurface blast. Results from this study can facilitate designers in computing damage and also in enhancing the impact behaviour, serviceability, and safety of pipelines.