Prediction of voltages on mitigated pipelines paralleling electric transmission lines using ANN

A. H. Al-Badi, K. Ellithy, S. Al-Alawi

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


This paper describes an artificial neural network (ANN) model developed to predict the total voltage on mitigated pipelines due to the effect of the inductive and conductive AC interference under fault conditions. The pipeline shared right-of-way with high voltage power lines and it is mitigated by gradient control wires. In particular, the developed ANN predicts the mitigated pipeline voltage under different soil resistivities, fault currents and separation distances. The results showed that the R2 value for the training and testing sets were 0.9978 and 0.996, respectively. This indicates that results from the ANN model compared well with the calculated values demonstrating the capability of the ANN simulation techniques. The results also demonstrate that the ANN-based model developed in this work can predict the voltage after applying mitigation system with high accuracy. The accuracy of the predicted voltage is very important to protect the overall pipeline integrity and make the pipeline and appurtenances safe for operating personnel.

Original languageEnglish
Pages (from-to)15-22
Number of pages8
JournalInternational Journal of Computers and Applications
Issue number1
Publication statusPublished - Mar 4 2010


  • Artificial neural network
  • Conductive interference
  • Corrosion
  • Inductive interference
  • Mitigation
  • Pipeline voltages

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design


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