Machine learning methods for predicting residual strength in corroded oil and gas steel pipes
Ilman, M. N. & Kusmono, Analysis of internal corrosion in subsea oil pipeline. Case Stud. Eng. Fail. Anal. 2, 1–8 (2014).
Lu, H. & Cheng, Y. F. Detecting urban gas pipeline leaks using a vehicle–canine collaboration strategy. Nat. Cities (2025).
Lu, H., Xi, D., Xiang, Y., Su, Z. & Cheng, Y. F. Vehicle-canine collaboration for urban pipeline methane leak detection. Nat. Cities. (2025).
Adumene, S., Khan, F., Adedigba, S., Zendehboudi, S. & Shiri, H. Dynamic risk analysis of marine and offshore systems suffering microbial induced stochastic degradation. Reliab. Eng. Syst. Saf. 207, 107388 (2021).
Bhandari, J., Khan, F., Abbassi, R., Garaniya, V. & Ojeda, R. Modelling of pitting corrosion in marine and offshore steel structures – A technical review. J. Loss Prev. Process Ind. 37, 39–62 (2015).
Google Scholar
Lu, H. et al. Theory and machine learning modeling for burst pressure estimation of pipeline with multipoint corrosion. J. Pipeline Syst. Eng. Pract. 14, 04023022 (2023).
Xu, L. et al. The research progress and prospect of data mining methods on corrosion prediction of oil and gas pipelines. Eng. Fail. Anal. 144, 106951 (2023).
Shaik, N. B., Pedapati, S. R., Othman, A. R., Bingi, K. & Dzubir, F. A. A. An intelligent model to predict the life condition of crude oil pipelines using artificial neural networks. Neural Comput. Appl. 33, 14771–14792 (2021).
Askari, M., Aliofkhazraei, M. & Afroukhteh, S. A comprehensive review on internal corrosion and cracking of oil and gas pipelines. J. Nat. Gas. Sci. Eng. 71, 102971 (2019).
Shaik, N. B., Pedapati, S. R. & B. A. Dzubir, F. A. Remaining useful life prediction of a piping system using artificial neural networks: a case study. Ain Shams Eng. J. 13, 101535 (2022).
Shaik, N. B., Jongkittinarukorn, K., Benjapolakul, W. & Bingi, K. A novel neural network-based framework to estimate oil and gas pipelines life with missing input parameters. Sci. Rep. 14, 4511 (2024).
Google Scholar
Ji, M., Yang, M. & Soghrati, S. A deep learning model to predict the failure response of steel pipes under pitting corrosion. Comput. Mech. 71, 295–310 (2023).
Lu, H., Iseley, T., Matthews, J., Liao, W. & Azimi, M. An ensemble model based on relevance vector machine and multi-objective salp swarm algorithm for predicting burst pressure of corroded pipelines. J. Petrol. Sci. Eng. 203, 108585 (2021).
Google Scholar
Soomro, A. A. et al. Integrity assessment of corroded oil and gas pipelines using machine learning: a systematic review. Eng. Fail. Anal. 131, 105810 (2022).
Google Scholar
Shaik, N. B. et al. Recurrent neural network-based model for estimating the life condition of a dry gas pipeline. Process Saf. Environ. Prot. 164, 639–650 (2022).
Google Scholar
Data and Statistics Overview | PHMSA. (Washington, DC, United States).
Wang, Q. & Lu, H. A novel stacking ensemble learner for predicting residual strength of corroded pipelines. npj Mater. Degrad. 8, 87 (2024).
Lu, H., Xu, Z.-D., Iseley, T. & Matthews, J. C. Novel data-driven framework for predicting residual strength of corroded pipelines. J. Pipeline Syst. Eng. Pract. 12, 04021045 (2021).
Zhou, R., Gu, X. & Luo, X. Residual strength prediction of X80 steel pipelines containing group corrosion defects. Ocean Eng. 274, 114077 (2023).
Cross, C. S. B31G – Manual for Determining the Remaining Strength of Corroded Pipelines Supplement to ASME B31 Code for Pressure Piping (ASME, 2012).
Guedes Soares, C., Garbatov, Y. & Zayed, A. Effect of environmental factors on steel plate corrosion under marine immersion conditions. Corros. Eng., Sci. Technol. 46, 524–541 (2011).
Google Scholar
Huang, Y., Zhang, P. & Qin, G. Investigation by numerical modeling of the mechano-electrochemical interaction at a dent-corrosion defect on pipelines. Ocean Eng. 256, 111402 (2022).
Kiefner, J. F. & Vieth, P. H. A modified criterion for evaluating the remaining strength of corroded pipe. (1989).
Liu, H., Khan, F. & Thodi, P. Revised burst model for pipeline integrity assessment. Eng. Fail. Anal. 80, 24–38 (2017).
Amaya-Gómez, R., Sánchez-Silva, M., Bastidas-Arteaga, E., Schoefs, F. & Muñoz, F. Reliability assessments of corroded pipelines based on internal pressure – a review. Eng. Fail. Anal. 98, 190–214 (2019).
Su, Y., Li, J., Yu, B., Zhao, Y. & Yao, J. Fast and accurate prediction of failure pressure of oil and gas defective pipelines using the deep learning model. Reliab. Eng. Syst. Saf. 216, 108016 (2021).
Hibbitt, H. D. ABAQUS/EPGEN—a general purpose finite element code with emphasis on nonlinear applications. Nucl. Eng. Des. 77, 271–297 (1984).
Mok, D. H. B., Pick, R. J., Glover, A. G. & Hoff, R. Bursting of line pipe with long external corrosion. Int. J. Press. Vessels Pip. 46, 195–216 (1991).
Zhang, T. et al. Efficient prediction method of triple failure pressure for corroded pipelines under complex loads based on a backpropagation neural network. Reliab. Eng. Syst. Saf. 231, 108990 (2023).
Phan, H. C., Dhar, A. S. & Mondal, B. C. Revisiting burst pressure models for corroded pipelines. Can. J. Civ. Eng. 44, 485–494 (2017).
Ma, B., Shuai, J., Liu, D. & Xu, K. Assessment on failure pressure of high strength pipeline with corrosion defects. Eng. Fail. Anal. 32, 209–219 (2013).
Huang, Y., Qin, G. & Hu, G. Failure pressure prediction by defect assessment and finite element modelling on pipelines containing a dent-corrosion defect. Ocean Eng. 266, 112875 (2022).
Qin, G., Cheng, Y. F. & Zhang, P. Finite element modeling of corrosion defect growth and failure pressure prediction of pipelines. Int. J. Press. Vessels Pip. 194, 104509 (2021).
Google Scholar
Ma, H. et al. A new hybrid approach model for predicting burst pressure of corroded pipelines of gas and oil. Eng. Fail. Anal. 149, 107248 (2023).
Ma, Y. et al. Deeppipe: theory-guided neural network method for predicting burst pressure of corroded pipelines. Process Saf. Environ. Prot. 162, 595–609 (2022).
Google Scholar
Hussain, M. et al. Review of prediction of stress corrosion cracking in gas pipelines using machine learning. Machines 12, 42 (2024).
Rachman, A., Zhang, T. & Ratnayake, R. M. C. Applications of machine learning in pipeline integrity management: a state-of-the-art review. Int. J. Press. Vessels Pip. 193, 104471 (2021).
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G. & Group, T. P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 6, e1000097 (2009).
Google Scholar
Zhang, L. et al. A review of machine learning in building load prediction. Appl. Energy 285, 116452 (2021).
Benjamin, A. C., Vieira, R. D., Freire, J. L. F. & de Castro, J. T. P. Burst tests on pipeline with long external corrosion. Am. Soc. Mech. Eng. Dig. Collect. (2016).
Benjamin, A. C., Freire, J. L. F., Vieira, R. D., Diniz, J. L. C. & De Andrade, E. Q. Burst tests on pipeline containing interacting corrosion defects. In 24th International Conference on Offshore Mechanics and Arctic Engineering, Vol. 3 403–417 (ASMEDC, Halkidiki, Greece, 2005).
Vijaya Kumar, S. D., Karuppanan, S. & Ovinis, M. Failure pressure prediction of high toughness pipeline with a single corrosion defect subjected to combined loadings using artificial neural network (ANN). Metals 11, 373 (2021).
Oliva, J. B. et al. Bayesian nonparametric kernel-learning. In Proc. 19th International Conference on Artificial Intelligence and Statistics 1078–1086 (PMLR, 2016).
Patki, N., Wedge, R. & Veeramachaneni, K. The synthetic data vault. In 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA) 399–410 (2016).
Aldosari, H., Rajasekaran, S. & Ammar, R. Generative adversarial neural network and genetic algorithms to predict oil and gas pipeline defect lengths. In Proc. of the ISCA 34th International Conference, Online 21–29 (2021).
Almustafa, M. K. & Nehdi, M. L. Machine learning prediction of structural response for FRP retrofitted RC slabs subjected to blast loading. Eng. Struct. 244, 112752 (2021).
Goodfellow, I. et al. Generative adversarial networks. Commun. ACM 63, 139–144 (2020).
Marani, A., Jamali, A. & Nehdi, M. L. Predicting ultra-high-performance concrete compressive strength using tabular generative adversarial networks. Materials 13, 4757 (2020).
Google Scholar
Park, N. et al. Data synthesis based on generative adversarial networks. Proc. VLDB Endow. 11, 1071–1083 (2018).
Xu, L. & Veeramachaneni, K. Synthesizing tabular data using generative adversarial networks. Preprint arXiv:1811.11264 (2018).
Jain, S., Seth, G., Paruthi, A., Soni, U. & Kumar, G. Synthetic data augmentation for surface defect detection and classification using deep learning. J. Intell. Manuf. 33, 1007–1020 (2022).
Xu, P., Du, R. & Zhang, Z. Predicting pipeline leakage in petrochemical system through GAN and LSTM. Knowl. Based Syst. 175, 50–61 (2019).
He, Z. & Zhou, W. Generation of synthetic full-scale burst test data for corroded pipelines using the tabular generative adversarial network. Eng. Appl. Artif. Intell. 115, 105308 (2022).
Zhou, Y., Zhang, Q., Singh, V. P. & Xiao, M. General correlation analysis: a new algorithm and application. Stoch. Environ. Res Risk Assess. 29, 665–677 (2015).
Baghban, A., Kahani, M., Nazari, M. A., Ahmadi, M. H. & Yan, W.-M. Sensitivity analysis and application of machine learning methods to predict the heat transfer performance of CNT/water nanofluid flows through coils. Int. J. Heat. Mass Transf. 128, 825–835 (2019).
Google Scholar
Ding, Y., Zhang, Q., Yuan, T. & Yang, K. Model input selection for building heating load prediction: a case study for an office building in Tianjin. Energy Build 159, 254–270 (2018).
Li, X., Zhang, L., Khan, F. & Han, Z. A data-driven corrosion prediction model to support digitization of subsea operations. Process Saf. Environ. Prot. 153, 413–421 (2021).
Google Scholar
Qiang, G., Zhe, T., Yan, D. & Neng, Z. An improved office building cooling load prediction model based on multivariable linear regression. Energy Build 107, 445–455 (2015).
Phan, H. C. & Duong, H. T. Predicting burst pressure of defected pipeline with principal component analysis and adaptive neuro fuzzy inference system. Int. J. Press. Vessels Pip. 189, 104274 (2021).
Tenenbaum, J. B., de Silva, V. & Langford, J. C. A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323 (2000).
Google Scholar
Li, X., Jia, R. & Zhang, R. A data-driven methodology for predicting residual strength of subsea pipeline with double corrosion defects. Ocean Eng. 279, 114530 (2023).
Lu, H., Matthews, J. C., Azimi, M. & Iseley, T. Near real-time HDD pullback force prediction model based on improved radial basis function neural networks. J. Pipeline Syst. Eng. Pract. 11, 04020042 (2020).
Hoerl, A. E. & Kennard, R. W. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12, 55–67 (1970).
Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B Stat. Methodol. 58, 267–288 (1996).
Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B Stat. Methodol. 67, 301–320 (2005).
Cai, J., Jiang, X., Yang, Y., Lodewijks, G. & Wang, M. Data-driven methods to predict the burst strength of corroded line pipelines subjected to internal pressure. J. Mar. Sci. Appl. 21, 115–132 (2022).
Abyani, M., Bahaari, M. R., Zarrin, M. & Nasseri, M. Predicting failure pressure of the corroded offshore pipelines using an efficient finite element based algorithm and machine learning techniques. Ocean Eng. 254, 111382 (2022).
Mahmutoglu, Y. & Turk, K. Positioning of leakages in underwater natural gas pipelines for time-varying multipath environment. Ocean Eng. 207, 107454 (2020).
Wang, L. et al. Status diagnosis and feature tracing of the natural gas pipeline weld based on improved random forest model. Int. J. Press. Vessels Pip. 200, 104821 (2022).
Bennett, K. P. & Mangasarian, O. L. Robust linear programming discrimination of two linearly inseparable sets. Optim. Methods Softw. 1, 23–34 (1992).
Cortes, C. & Vapnik, V. Support-vector networks. Mach. Learn. 20, 273–297 (1995).
Tipping, M. E. Sparse bayesian learning and the relevance vector machine. J. Mach. Learn. Res. 1, 211–244 (2001).
Silva, R. C. C., Guerreiro, J. N. C. & Loula, A. F. D. A study of pipe interacting corrosion defects using the FEM and neural networks. Adv. Eng. Softw. 38, 868–875 (2007).
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
Google Scholar
Chen, Z. et al. Residual strength prediction of corroded pipelines using multilayer perceptron and modified feedforward neural network. Reliab. Eng. Syst. Saf. 231, 108980 (2023).
Gholami, H., Shahrooi, S. & Shishesaz, M. Predicting the burst pressure of high-strength carbon steel pipe with gouge flaws using artificial neural network. J. Pipeline Syst. Eng. Pract. 11, 04020034 (2020).
Miao, X. & Zhao, H. Novel method for residual strength prediction of defective pipelines based on HTLBO-DELM model. Reliab. Eng. Syst. Saf. 237, 109369 (2023).
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
Louppe, G. Understanding random forests: from theory to practice (Doctoral dissertation, Universite de Liege, Belgium, 2014).
Breiman, L. Bagging predictors. Mach. Learn. 24, 123–140 (1996).
Geurts, P., Ernst, D. & Wehenkel, L. Extremely randomized trees. Mach. Learn. 63, 3–42 (2006).
Yingjie, Z., Yibo, A. & Weidong, Z. Physics informed ensemble learning used for interval prediction of fracture toughness of pipeline steels in hydrogen environments. Theor. Appl. Fract. Mech. 130, 104302 (2024).
Google Scholar
Yu, A. et al. Rapid accomplishment of cost-effective and macro-defect-free LPBF-processed Ti parts based on deep data augmentation. J. Manuf. Process. 120, 1023–1034 (2024).
Li, Q. & Song, Z. Prediction of compressive strength of rice husk ash concrete based on stacking ensemble learning model. J. Clean. Prod. 382, 135279 (2023).
Xiao, R., Zayed, T., Meguid, M. A. & Sushama, L. Predicting failure pressure of corroded gas pipelines: a data-driven approach using machine learning. Process Saf. Environ. Prot. 184, 1424–1441 (2024).
Google Scholar
Song, Y. et al. Interpretable machine learning for maximum corrosion depth and influence factor analysis. npj Mater. Degrad. 7, 9 (2023).
Liu, T. et al. Modeling the load carrying capacity of corroded reinforced concrete compression bending members using explainable machine learning. Mater. Today Commun. 36, 106781 (2023).
Google Scholar
Chen, M. et al. XGBoost-based algorithm interpretation and application on post-fault transient stability status prediction of power system. IEEE Access 7, 13149–13158 (2019).
Gertz, M. et al. Using the XGBoost algorithm to classify neck and leg activity sensor data using on-farm health recordings for locomotor-associated diseases. Comput. Electron. Agric. 173, 105404 (2020).
Mo, H., Sun, H., Liu, J. & Wei, S. Developing window behavior models for residential buildings using XGBoost algorithm. Energy Build 205, 109564 (2019).
Geng, Z. et al. Development and validation of a machine learning-based predictive model for assessing the 90-day prognostic outcome of patients with spontaneous intracerebral hemorrhage. J. Transl. Med. 22, 236 (2024).
Google Scholar
Mesghali, H. et al. Predicting maximum pitting corrosion depth in buried transmission pipelines: insights from tree-based machine learning and identification of influential factors. Process Saf. Environ. Prot. 187, 1269–1285 (2024).
Google Scholar
Khan, A. A., Chaudhari, O. & Chandra, R. A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation. Expert Syst. Appl. 244, 122778 (2024).
Goliatt, L., Saporetti, C. M. & Pereira, E. Super learner approach to predict total organic carbon using stacking machine learning models based on well logs. Fuel 353, 128682 (2023).
Google Scholar
Hajihosseinlou, M., Maghsoudi, A. & Ghezelbash, R. Stacking: a novel data-driven ensemble machine learning strategy for prediction and mapping of Pb-Zn prospectivity in Varcheh district, west Iran. Expert Syst. Appl. 237, 121668 (2024).
De Masi, G., Gentile, M., Vichi, R., Bruschi, R. & Gabetta, G. Machine learning approach to corrosion assessment in subsea pipelines. In OCEANS 2015 – Genova 1–6 (2015).
Phan, H. C. & Dhar, A. S. Predicting pipeline burst pressures with machine learning models. Int. J. Press. Vessels Pip. 191, 104384 (2021).
Chen, R. J. C., Bloomfield, P. & Fu, J. S. An evaluation of alternative forecasting methods to recreation visitation. J. Leis. Res. 35, 441–454 (2003).
Shen, Y., Wu, S., Wang, Y., Wang, J. & Yang, Z. Interpretable model for rockburst intensity prediction based on Shapley values-based Optuna-random forest. Underground Space (2024).
Zhang, Y. l., Qiu, Y-g., Armaghani, D. J., Monjezi, M. & Zhou, J. Enhancing rock fragmentation prediction in mining operations: A Hybrid GWO-RF model with SHAP interpretability. J. Cent. South Univ. 31, 2916–2929 (2024).
Ding, H. et al. A hybrid approach for modeling bicycle crash frequencies: integrating random forest based SHAP model with random parameter negative binomial regression model. Accid. Anal. Prev. 208, 107778 (2024).
Google Scholar
Ukwuoma, C. C. et al. Enhancing hydrogen production prediction from biomass gasification via data augmentation and explainable AI: a comparative analysis. Int. J. Hydrog. Energy 68, 755–776 (2024).
Google Scholar
Lo, M., Karuppanan, S. & Ovinis, M. Failure pressure prediction of a corroded pipeline with longitudinally interacting corrosion defects subjected to combined loadings using FEM and ANN. J. Mar. Sci. Eng. 9, 281 (2021).
Li, X., Jing, H., Liu, X., Chen, G. & Han, L. The prediction analysis of failure pressure of pipelines with axial double corrosion defects in cold regions based on the BP neural network. Int. J. Press. Vessels Pip. 202, 104907 (2023).
Oh, D., Race, J., Oterkus, S. & Koo, B. Burst pressure prediction of API 5L X-grade dented pipelines using deep neural network. J. Mar. Sci. Eng. 8, 766 (2020).
Liu, P., Han, Y. & Tian, Y. Residual strength prediction of pipeline with single defect based on SVM algorithm. J. Phys.: Conf. Ser. 1944, 012019 (2021).
Liu, X. et al. An ANN-based failure pressure prediction method for buried high-strength pipes with stray current corrosion defect. Energy Sci. Eng. 8, 248–259 (2020).
Lo, M., Karuppanan, S. & Ovinis, M. ANN- and FEA-based assessment equation for a corroded pipeline with a single corrosion defect. J. Mar. Sci. Eng. 10, 476 (2022).
Lo, M., Vijaya Kumar, S. D., Karuppanan, S. & Ovinis, M. An artificial neural network-based equation for predicting the remaining strength of mid-to-high strength pipelines with a single corrosion defect. Appl. Sci. 12, 1722 (2022).
Google Scholar
Chin et al. Failure pressure prediction of pipeline with single corrosion defect using artificial neural network. Pipeline Sci. Technol. 4, 10–17 (2020).
Keshtegar, B. & el Amine Ben Seghier, M. Modified response surface method basis harmony search to predict the burst pressure of corroded pipelines. Eng. Fail. Anal. 89, 177–199 (2018).
Sun, C., Wang, Q., Li, Y., Li, Y. & Liu, Y. Numerical simulation and analytical prediction of residual strength for elbow pipes with erosion defects. Materials 15, 7479 (2022).
Google Scholar
Tingke, L., Yuanchun, P., Jiadi, L., Dulin & Xingqin, L. Research on residual strength prediction model of corroded pipeline based on improved PSO-BP. IOP Conf. Ser. Earth Environ. Sci. 687, 012007 (2021).
Vijaya Kumar, S. D., Lo, M., Karuppanan, S. & Ovinis, M. Failure pressure prediction of medium to high toughness pipe with circumferential interacting corrosion defects subjected to combined loadings using artificial neural network. Appl. Sci. 12, 4120 (2022).
Google Scholar
Vijaya Kumar, S. D., Lo, M., Karuppanan, S. & Ovinis, M. Empirical failure pressure prediction equations for pipelines with longitudinal interacting corrosion defects based on artificial neural network. J. Mar. Sci. Eng. 10, 764 (2022).
Xu, W.-Z., Li, C. B., Choung, J. & Lee, J.-M. Corroded pipeline failure analysis using artificial neural network scheme. Adv. Eng. Softw. 112, 255–266 (2017).
Zhang, H. & Tian, Z. Failure analysis of corroded high-strength pipeline subject to hydrogen damage based on FEM and GA-BP neural network. Int. J. Hydrog. Energy 47, 4741–4758 (2022).
Vijaya Kumar, S. D., Karuppanan, S. & Ovinis, M. Artificial neural network-based failure pressure prediction of API 5L X80 pipeline with circumferentially aligned interacting corrosion defects subjected to combined loadings. Materials 15, 2259 (2022).
Google Scholar
Meng, H., Xu, N., Zhu, Y. & Mei, G. Generating stochastic structural planes using statistical models and generative deep learning models: a comparative investigation. Mathematics 12, 2545 (2024).
Nieto, P. J. G., Gonzalo, E. G., García, L. A. M., Prado, L. Á. & Sánchez, A. B. Predicting the critical superconducting temperature using the random forest, MLP neural network, M5 model tree and multivariate linear regression. Alex. Eng. J. 86, 144–156 (2024).
Ebrahimi, M. & Basiri, A. RACEkNN: a hybrid approach for improving the effectiveness of the k-nearest neighbor algorithm. Knowl. Based Syst. 301, 112357 (2024).
Wang, R., Zhang, M., Gong, F., Wang, S. & Yan, R. Improving port state control through a transfer learning-enhanced XGBoost model. Reliab. Eng. Syst. Saf. 253, 110558 (2025).
link
