Corrosion is one of the most challenging problems that affects the safety and durability of onshore pipelines. Corrosion-resistant steels play a pivotal role in ensuring long lasting function, cost reduction in upkeep, and prevention of major breakdowns. The traditional materials selection methods often depend on hands-on testing, expert experience and the use of a trial-and-error process. The rapid advancement of machine learning and artificial intelligence (AI) driven techniques is revolutionizing materials discovery, property prediction, and material design by minimizing human intervention and accelerating scientific progress. In the advent of the digital revolution, Artificial Intelligence (AI) has emerged as a pivotal tool in various domains, including materials design and discovery. This paper aims to develop and facilitate how AI is used to interpret Ashby’s chart and merge databases of materials together to select materials for corrosion resistant steels in onshore oil and gas pipelines. This paper proposes an AI Assisted material selection framework specifically designed to identify optimal corrosion resistant steels for onshore pipelines oil and gas pipelines by improving forecast accuracy, reducing uncertainty, enabling informed material selection, and strengthening longterm pipeline integrity in corrosive operating environments. By leveraging machine learning (ML) techniques such as Gradient Boosting Machines (GBM), Principal Component Analysis (PCA) for dimensionality reduction, and Neural Networks. The framework intelligently analyzes vast datasets encompassing operational parameters, environmental conditions, and historical corrosion rates.
References
[1]
Papadimitriou, I., Gialampoukidis, I., Vrochidis, S. and Kompatsiaris, I. (2024) AI Methods in Materials Design, Discovery and Manufacturing: A Review. ComputationalMaterialsScience, 235, Article ID: 112793. https://doi.org/10.1016/j.commatsci.2024.112793
[2]
Merayo, D., Rodríguez-Prieto, A. and Camacho, A.M. (2019) Comparative Analysis of Artificial Intelligence Techniques for Material Selection Applied to Manufacturing in Industry 4.0. ProcediaManufacturing, 41, 42-49. https://doi.org/10.1016/j.promfg.2019.07.027
[3]
Ebili, F., Oterkus, S. and Oterkus, E. (2025) A Detailed Structural Review of Onshore and Offshore Pipelines Containing Defects. SustainableMarineStructures, 7, 177-208. https://doi.org/10.36956/sms.v7i3.2320
[4]
Ossai, C.I. (2019) A Data-Driven Machine Learning Approach for Corrosion Risk Assessment—A Comparative Study. BigDataandCognitiveComputing, 3, Article 28. https://doi.org/10.3390/bdcc3020028
[5]
Nešić, S. (2007) Key Issues Related to Modelling of Internal Corrosion of Oil and Gas Pipelines—A Review. CorrosionScience, 49, 4308-4338. https://doi.org/10.1016/j.corsci.2007.06.006
[6]
Kermani, M.B. and Morshed, A. (2003) Carbon Dioxide Corrosion in Oil and Gas Production—A Compendium. Corrosion, 59, 659-683. https://doi.org/10.5006/1.3277596
[7]
Ossai, C.I., Boswell, B. and Davies, I.J. (2015) Pipeline Failures in Corrosive Environments—A Conceptual Analysis of Trends and Effects. EngineeringFailureAnalysis, 53, 36-58. https://doi.org/10.1016/j.engfailanal.2015.03.004
[8]
Chen, J., Althaus, S.M., Liu, H. and Sun, Q. (2019) Shale Gas Transport in Rock Matrix: Diffusion in the Presence of Surface Adsorption and Capillary Condensation. JournalofNaturalGasScienceandEngineering, 66, 18-25. https://doi.org/10.1016/j.jngse.2019.03.015
Kaddor, C. and Steinbüchel, A. (2011) Implications of Various Phosphoenolpyruvate-Carbohydrate Phosphotransferase System Mutations on Glycerol Utilization and Poly(3-Hydroxybutyrate) Accumulation in Ralstonia eutropha H16. AMBExpress, 1, Article No. 16. https://doi.org/10.1186/2191-0855-1-16
[11]
Ashby, M.F., Bréchet, Y.J.M., Cebon, D. and Salvo, L. (2004) Selection Strategies for Materials and Processes. Materials&Design, 25, 51-67. https://doi.org/10.1016/s0261-3069(03)00159-6
[12]
Reda, A., Shahin, M.A. and Montague, P. (2025) Review of Material Selection for Corrosion-Resistant Alloy Pipelines. Engineered Science, 33, Article 1373. https://doi.org/10.30919/es1373
[13]
Wei, R.P. and Gao, M. (1991) Technical Note: Distribution of Initial Current between Bare and Filmed Surfaces: What Is Being Measured in a Scratched Electrode Test? Corrosion, 47, 948-951. https://doi.org/10.5006/1.3585207
[14]
Caleyo, F., Velázquez, J.C., Valor, A. and Hallen, J.M. (2009) Markov Chain Modelling of Pitting Corrosion in Underground Pipelines. CorrosionScience, 51, 2197-2207. https://doi.org/10.1016/j.corsci.2009.06.014
[15]
Jahan, A., Ismail, M.Y., Sapuan, S.M. and Mustapha, F. (2010) Material Screening and Choosing Methods—A Review. Materials&Design, 31, 696-705. https://doi.org/10.1016/j.matdes.2009.08.013
[16]
Do, H., Rothermel, G. and Kinneer, A. (2006) Prioritizing Junit Test Cases: An Empirical Assessment and Cost-Benefits Analysis. EmpiricalSoftwareEngineering, 11, 33-70. https://doi.org/10.1007/s10664-006-5965-8
[17]
Badings, T.S. and van Putten, D.S. (2020) Data Validation and Reconciliation for Error Correction and Gross Error Detection in Multiphase Allocation Systems. JournalofPetroleumScienceandEngineering, 195, Article ID: 107567. https://doi.org/10.1016/j.petrol.2020.107567
[18]
Lee, S.Y., Byeon, S., Kim, H.S., Jin, H. and Lee, S. (2021) Deep Learning-Based Phase Prediction of High-Entropy Alloys: Optimization, Generation, and Explanation. Materials&Design, 197, Article ID: 109260. https://doi.org/10.1016/j.matdes.2020.109260
[19]
Hernández, S., Nešić, S., Weckman, G. and Ghai, V. (2006) Use of Artificial Neural Networks for Predicting Crude Oil Effect on Carbon Dioxide Corrosion of Carbon Steels. Corrosion, 62, 467-482. https://doi.org/10.5006/1.3279905
[20]
Ben Seghier, M.E.A., Höche, D. and Zheludkevich, M. (2022) Prediction of the Internal Corrosion Rate for Oil and Gas Pipeline: Implementation of Ensemble Learning Techniques. JournalofNaturalGasScienceandEngineering, 99, Article ID: 104425. https://doi.org/10.1016/j.jngse.2022.104425
[21]
Soori, M., Jough, F.K.G., Dastres, R. and Arezoo, B. (2026) AI-Based Decision Support Systems in Industry 4.0, a Review. JournalofEconomyandTechnology, 4, 206-225. https://doi.org/10.1016/j.ject.2024.08.005
[22]
Liu, L. (2024) Machine Learning-Driven Corrosion Detection and Classification in Pipelines. Ph.D. Thesis, University of Wales Trinity Saint David.
[23]
Lu, H., Peng, H., Xu, Z., Qin, G., Azimi, M., Matthews, J.C., et al. (2023) Theory and Machine Learning Modeling for Burst Pressure Estimation of Pipeline with Multipoint Corrosion. Journal of Pipeline Systems Engineering and Practice, 14, 04023022. https://doi.org/10.1061/jpsea2.pseng-1481
[24]
Bansal, P., Zheng, Z., Shao, C., Li, J., Banu, M., Carlson, B.E., et al. (2022) Physics-informed Machine Learning Assisted Uncertainty Quantification for the Corrosion of Dissimilar Material Joints. ReliabilityEngineering&SystemSafety, 227, Article ID: 108711. https://doi.org/10.1016/j.ress.2022.108711
[25]
Wen, C., Zhang, Y., Wang, C., Xue, D., Bai, Y., Antonov, S., et al. (2019) Machine Learning Assisted Design of High Entropy Alloys with Desired Property. ActaMaterialia, 170, 109-117. https://doi.org/10.1016/j.actamat.2019.03.010
[26]
Lajevardi, S.A., Shahrabi, T., Baigi, V. and Shafiei M, A. (2009) Prediction of Time to Failure in Stress Corrosion Cracking of 304 Stainless Steel in Aqueous Chloride Solution by Artificial Neural Network. ProtectionofMetalsandPhysicalChemistryofSurfaces, 45, 610-615. https://doi.org/10.1134/s2070205109050207
[27]
Adumene, S., Khan, F., Adedigba, S., Zendehboudi, S. and Shiri, H. (2021) Dynamic Risk Analysis of Marine and Offshore Systems Suffering Microbial Induced Stochastic Degradation. ReliabilityEngineering&SystemSafety, 207, Article ID: 107388. https://doi.org/10.1016/j.ress.2020.107388
[28]
Ortiz, A., Bonnin-Pascual, F., Garcia-Fidalgo, E. and Company-Corcoles, J. (2016) Vision-Based Corrosion Detection Assisted by a Micro-Aerial Vehicle in a Vessel Inspection Application. Sensors, 16, Article 2118. https://doi.org/10.3390/s16122118
[29]
Bertolini, L. and Redaelli, E. (2009) Throwing Power of Cathodic Prevention Applied by Means of Sacrificial Anodes to Partially Submerged Marine Reinforced Concrete Piles: Results of Numerical Simulations. CorrosionScience, 51, 2218-2230. https://doi.org/10.1016/j.corsci.2009.06.012
[30]
Nasser, A.M.M., Montasir, O.A., Wan Abdullah Zawawi, N.A. and Alsubal, S. (2020) A Review on Oil and Gas Pipelines Corrosion Growth Rate Modelling Incorporating Artificial Intelligence Approach. IOPConferenceSeries: EarthandEnvironmentalScience, 476, Article ID: 012024. https://doi.org/10.1088/1755-1315/476/1/012024
[31]
Goswami, L., Deka, M.K. and Roy, M. (2023) Artificial Intelligence in Material Engineering: A Review on Applications of Artificial Intelligence in Material Engineering. AdvancedEngineeringMaterials, 25, 2300104. https://doi.org/10.1002/adem.202300104
[32]
Blake, W.K. and Gershfeld, J.L. (1989) The Aeroacoustics of Trailing Edges. In: Gad-el-Hak, M., Ed., Frontiers in Experimental Fluid Mechanics, Springer, 457-532. https://doi.org/10.1007/978-3-642-83831-6_10
[33]
Singh, B. and Krishnathasan, K. (2009) Pragmatic Effects of Flow on Corrosion Prediction. CORROSION 2009, Atlanta, 22-26 March 2009, 1-29. https://doi.org/10.5006/c2009-09275
[34]
Naveen Prasad, B.S., Meka, U., Rajasekaran, R. and Banerjee, S. (2025) Machine Learning Techniques in Water Treatment. In: Namdeti, R. and Abuda Joaquin, A., Eds., MachineLearninginWaterTreatment, Wiley, 345-411.
[35]
Zhou, Q., Chen, X. and Wang, J. (2025) Machine Learning Assisted Material Discovery: A Small Data Approach. AccountsofMaterialsResearch, 6, 685-694. https://doi.org/10.1021/accountsmr.1c00236
[36]
Morgan, D. and Jacobs, R. (2020) Opportunities and Challenges for Machine Learning in Materials Science. AnnualReviewofMaterialsResearch, 50, 71-103. https://doi.org/10.1146/annurev-matsci-070218-010015
[37]
Al-Sabaeei, A.M., Alhussian, H., Abdulkadir, S.J. and Jagadeesh, A. (2023) Prediction of Oil and Gas Pipeline Failures through Machine Learning Approaches: A Systematic Review. EnergyReports, 10, 1313-1338. https://doi.org/10.1016/j.egyr.2023.08.009
[38]
Askari, M., Aliofkhazraei, M. and Afroukhteh, S. (2019) A Comprehensive Review on Internal Corrosion and Cracking of Oil and Gas Pipelines. JournalofNaturalGasScienceandEngineering, 71, Article ID: 102971. https://doi.org/10.1016/j.jngse.2019.102971
[39]
Bhandari, J., Khan, F., Abbassi, R., Garaniya, V. and Ojeda, R. (2015) Modelling of Pitting Corrosion in Marine and Offshore Steel Structures—A Technical Review. JournalofLossPreventionintheProcessIndustries, 37, 39-62. https://doi.org/10.1016/j.jlp.2015.06.008
[40]
Shaik, N.B., Jongkittinarukorn, K., Benjapolakul, W. and Bingi, K. (2024) A Novel Neural Network-Based Framework to Estimate Oil and Gas Pipelines Life with Missing Input Parameters. ScientificReports, 14, Article No. 4511. https://doi.org/10.1038/s41598-024-54964-3
[41]
Lu, H., Xi, D., Xiang, Y., Su, Z. and Cheng, Y.F. (2025) Vehicle-Canine Collaboration for Urban Pipeline Methane Leak Detection. NatureCities, 2, 336-343. https://doi.org/10.1038/s44284-024-00183-w
[42]
Mokaberi, A., Derakhshandeh-Haghighi, R. and Abbaszadeh, Y. (2015) Fatigue Fracture Analysis of Gas Turbine Compressor Blades. EngineeringFailureAnalysis, 58, 1-7. https://doi.org/10.1016/j.engfailanal.2015.08.026
[43]
Sur, D., Gupta, A., Dubey, S. and Kumar, A. (2025) Properties of Materials and Selection Criteria. Chemical Engineering Essentials 2: Advanced Processes, Materials, and Sustainability, 79-107.
[44]
Yazdi, M. (2022) Management of Offshore Structures under Microbiologically Influenced Corrosion (MIC). Master’s Thesis, Memorial University of Newfoundland.
[45]
Canonaco, G., Roveri, M., Alippi, C., Podenzani, F., Bennardo, A., Conti, M., et al. (2021) A Transfer-Learning Approach for Corrosion Prediction in Pipeline Infrastructures. AppliedIntelligence, 52, 7622-7637. https://doi.org/10.1007/s10489-021-02771-y
[46]
May, Z., Alam, M.K., Nayan, N.A., Rahman, N.A.A. and Mahmud, M.S. (2021) Acoustic Emission Corrosion Feature Extraction and Severity Prediction Using Hybrid Wavelet Packet Transform and Linear Support Vector Classifier. PLOSONE, 16, e0261040. https://doi.org/10.1371/journal.pone.0261040
[47]
Qu, Z., Zou, X., Xiong, G., Yue, X. and Zhang, L. (2025) Hybrid Intelligent Model for Predicting Corrosion Rate of Carbon Steel in Co2 Environments. MaterialsandCorrosion, 76, 1319-1326. https://doi.org/10.1002/maco.202514840
[48]
Hussain, A.M., Sanoussi, A.A. and Hussain, H.A.M. (2010) Pollution of Drinking Water Transported by Corroded Metallic Pipelines. WITTransactionsonEcologyandtheEnvironment, 135, 61-69. https://doi.org/10.2495/wp100061
[49]
Zhang, L. and Gao, X. (2024) Transfer Adaptation Learning: A Decade Survey. IEEETransactionsonNeuralNetworksandLearningSystems, 35, 23-44. https://doi.org/10.1109/tnnls.2022.3183326
[50]
Esna-Ashari, M. (2025) Beyond the Black Box: A Review of Quantitative Metrics for Neural Network Interpretability and Their Practical Implications. International Journal of Sustainable Applied Science and Engineering, 2, 1-24. https://bgsiran.ir/journal/ojs-3.1.1-4/index.php/IJSASE/article/view/133
[51]
Ma, H., Geng, M., Wang, F., Zheng, W., Ai, Y. and Zhang, W. (2024) Data Augmentation of a Corrosion Dataset for Defect Growth Prediction of Pipelines Using Conditional Tabular Generative Adversarial Networks. Materials, 17, Article 1142. https://doi.org/10.3390/ma17051142
[52]
Schütt, K.T., Sauceda, H.E., Kindermans, P., Tkatchenko, A. and Müller, K. (2018) SchNet—A Deep Learning Architecture for Molecules and Materials. TheJournalofChemicalPhysics, 148, Article ID: 241722. https://doi.org/10.1063/1.5019779
[53]
Karniadakis, G.E., Kevrekidis, I.G., Lu, L., Perdikaris, P., Wang, S. and Yang, L. (2021) Physics-Informed Machine Learning. NatureReviewsPhysics, 3, 422-440. https://doi.org/10.1038/s42254-021-00314-5
[54]
Zhong, X., Gallagher, B., Liu, S., Kailkhura, B., Hiszpanski, A. and Han, T.Y. (2022) Explainable Machine Learning in Materials Science. npjComputationalMaterials, 8, Article No. 204. https://doi.org/10.1038/s41524-022-00884-7
[55]
Meng, C., Griesemer, S., Cao, D., Seo, S. and Liu, Y. (2025) When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning. Machine Learning for Computational Science and Engineering, 1, Article No. 20. https://doi.org/10.1007/s44379-025-00016-0
