Burst pressure prediction of API 5L X-grade dented pipelines using deep neural network

Dohan Oh, Julia Race, Selda Oterkus, Bonguk Koo

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)
28 Downloads (Pure)


Mechanical damage is recognized as a problem that reduces the performance of oil and gas pipelines and has been the subject of continuous research. The artificial neural network in the spotlight recently is expected to be another solution to solve the problems relating to the pipelines. The deep neural network, which is on the basis of artificial neural network algorithm and is a method amongst various machine learning methods, is applied in this study. The applicability of machine learning techniques such as deep neural network for the prediction of burst pressure has been investigated for dented API 5L X-grade pipelines. To this end, supervised learning is employed, and the deep neural network model has four layers with three hidden layers, and the neural network uses the fully connected layer. The burst pressure computed by deep neural network model has been compared with the results of finite element analysis based parametric study, and the burst pressure calculated by the experimental results. According to the comparison results, it showed good agreement. Therefore, it is concluded that deep neural networks can be another solution for predicting the burst pressure of API 5L X-grade dented pipelines.
Original languageEnglish
Article number766
Number of pages19
JournalJournal of Marine Science and Engineering
Issue number10
Publication statusPublished - 30 Sept 2020


  • artificial neural network (ANN)
  • deep neural network (DNN)
  • burst pressure
  • pipeline
  • dented pipelines
  • ocean and shore technology (OST)


Dive into the research topics of 'Burst pressure prediction of API 5L X-grade dented pipelines using deep neural network'. Together they form a unique fingerprint.

Cite this