Cover Image

Prediction of oil and gas pipeline failures through machine learning approaches: A systematic review

Saved in:

Bibliographic Details
Main Authors:	Al-Sabaeei, Abdulnaser M, Alhussian, Hitham, Abdulkadir, Said Jadid, Jagadeesh, Ajayshankar
Format:	Article
Published:	Elsevier 2023
Online Access:	http://scholars.utp.edu.my/id/eprint/37165/
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Prediction of oil and gas pipeline failures through machine learning approaches: A systematic review
by: Al-Sabaeei, A.M., et al.
Published: (2023)

Utilization of response surface methodology and machine learning for predicting and optimizing mixing and compaction temperatures of bio-modified asphalt
by: Al-Sabaeei, Abdulnaser M, et al.
Published: (2023)

Utilization of response surface methodology and machine learning for predicting and optimizing mixing and compaction temperatures of bio-modified asphalt
by: Al-Sabaeei, Abdulnaser M, et al.
Published: (2023)

Predicting the rutting parameters of nanosilica/waste denim fiber composite asphalt binders using the response surface methodology and machine learning methods
by: Al-Sabaeei, Abdulnaser M, et al.
Published: (2023)

Predicting the rutting parameters of nanosilica/waste denim fiber composite asphalt binders using the response surface methodology and machine learning methods
by: Al-Sabaeei, Abdulnaser M, et al.
Published: (2023)

Predicting the rutting parameters of nanosilica/waste denim fiber composite asphalt binders using the response surface methodology and machine learning methods
by: Al-Sabaeei, Abdulnaser M., et al.
Published: (2023)

Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: A systematic survey
by: Talpur, Noureen, et al.
Published: (2023)

Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: A systematic survey
by: Talpur, Noureen, et al.
Published: (2023)

Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: A systematic survey
by: Talpur, Noureen, et al.
Published: (2023)

Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: A systematic survey
by: Talpur, Noureen, et al.
Published: (2023)

Failure investigation of natural gas pipeline
by: Abdul Majid, Zulkifli, et al.
Published: (2012)

Failure analysis of natural gas pipeline
by: Abdul Majid, Zulkifli, et al.
Published: (2008)

Systematic Literature Review of Information Extraction From Textual Data: Recent Methods, Applications, Trends, and Challenges
by: Abdullah, Mohd Hafizul Afifi, et al.
Published: (2023)

Systematic Literature Review of Information Extraction From Textual Data: Recent Methods, Applications, Trends, and Challenges
by: Abdullah, Mohd Hafizul Afifi, et al.
Published: (2023)

Systematic Literature Review of Information Extraction From Textual Data: Recent Methods, Applications, Trends, and Challenges
by: Abdullah, Mohd Hafizul Afifi, et al.
Published: (2023)

Prediction of critical total drawdown in sand production from gas wells: Machine learning approach
by: Alakbari, Fahd Saeed, et al.
Published: (2023)

The consequence assessment of gas pipeline failure due to corrosion
by: Zardasti, Libriati, et al.
Published: (2015)

Utilization of response surface methodology and machine learning for predicting and optimizing mixing and compaction temperatures of bio-modified asphalt
by: Al-Sabaeei, A.M., et al.
Published: (2023)

Three-term back propagation neural network for moisture prediction
by: Abdulkadir, Said Jadid
Published: (2012)

Development of machine learning prediction model for sand production in oil & gas pipeline
by: Azlin, Ahmad Arief Daniel
Published: (2024)

Integrity assessment of corroded oil and gas pipelines using machine learning: A systematic review
by: Soomro, A.A., et al.
Published: (2022)

Integrity assessment of corroded oil and gas pipelines using machine learning: A systematic review
by: Soomro, A.A., et al.
Published: (2022)

A Comprehensive Systematic Review of YOLO for Medical Object Detection (2018 to 2023)
by: Qureshi, Rizwan, et al.
Published: (2023)

Failure Prediction of Corrosion in Pipeline Infrastructures using Decision Tree Regression
by: Mohamad Sapawi, Mohamad Dzul Airie
Published: (2024)

Manufacture and prediction of the failure samples of weldment layers on mild steel pipeline
by: Clament Sicmainan
Published: (2024)

A review on Bayesian modeling approach to quantify failure risk assessment of oil and gas pipelines due to corrosion
by: Soomro, A.A., et al.
Published: (2022)

Risk consequences assessment of gas pipeline failure incorporating local loss factors
by: Mohd. Hanafiah, Norhamimi
Published: (2020)

Evaluation of machine learning algorithms in predicting CO2 internal corrosion in oil and gas pipelines
by: Mohammad Zubir, W.M.A., et al.
Published: (2019)

Evaluation of machine learning algorithms in predicting CO 2 internal corrosion in oil and gas pipelines
by: Mohammad Zubir, W.M.A., et al.
Published: (2019)

Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-Bayesian Optimization.
by: Alqushaibi, Alawi, et al.
Published: (2023)

Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-Bayesian Optimization.
by: Alqushaibi, Alawi, et al.
Published: (2023)

Prediction of Machine Failure by Using Machine Learning Algorithm
by: Fakhrurazi, Nur Amalina
Published: (2019)

Failure pressure prediction of pipelines steel in the presence of corrosion defects and pre-strain
by: Prakash Raj, Subramaniam
Published: (2013)

Failure pressure prediction of pipelines steel in the presence of corrosion defects and pre-strain
by: Prakash Raj, Subramaniam
Published: (2013)

Top-of-line spatial corrosion prediction in gas pipelines
by: Mustaffa, Z., et al.
Published: (2014)

TOP-OF-LINE SPATIAL CORROSION PREDICTION IN GAS PIPELINES
by: Mustaffa, Zahiraniza
Published: (2014)

TOP-OF-LINE SPATIAL CORROSION PREDICTION IN GAS PIPELINES
by: Mustaffa, Zahiraniza, et al.
Published: (2014)

Predicting student's performance using machine learning methods: A systematic literature review
by: Baashar Y., et al.
Published: (2023)

Failure pressure prediction of pipeline with single corrosion defect using artificial neural network
by: Chin, K.T., et al.
Published: (2021)

ANALYSIS OF SMOTE-BASED DATA AUGMENTATION AND MACHINE LEARNING MODELS FOR BURST PRESSURE PREDICTION OF OIL AND GAS PIPELINES
by: SOOMRO, AFZAL AHMED
Published: (2024)