Abstract
Pipeline condition assessment is a cost-effective method to determine the status of pipeline structure and predict failure probability. Although 100% inspection may not be feasible for decision makers, recent advancements in machine learning techniques have enabled more effective pipeline condition assessment. This paper provides a comprehensive review of machine learning applications in pipeline condition assessment, covering aspects such as fault diagnosis, risk prediction, parameter prediction, and visual defect recognition. The present study endeavors to make the following contributions: (1) extraction of the model, data size, and other relevant information from 91 papers, (2) in-depth analysis of the state of the art and frameworks of the models discussed in the 91 papers, (3) summary of the data characteristics, input variables, and accuracy of machine learning models, and (4) exploration of the potential avenues for future research in the use of machine learning for pipeline condition assessment. This review aims to serve as a practical reference for scholars engaged in related research. The review highlights the fact that the majority of the models employed in pipeline condition assessment are original, and the utilization of hybrid models remains limited. Transfer learning and reinforcement learning are identified as potential avenues for future research because they hold promise in facilitating the adaptive selection of model inputs and the transfer of models to similar projects. Furthermore, breaking down data barriers is deemed essential for advancing the use of machine learning in pipeline condition assessment.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This study was financially supported by the Major Project of Fundamental Research on Frontier Leading Technology of Jiangsu Province (Grant No. BK20222006); and Natural Science Foundation of Jiangsu Province (Grant No. BK20220848).
References
Abbas, A. K., R. Flori, H. Almubarak, J. Dawood, H. Abbas, and A. Alsaedi. 2019. “Intelligent prediction of stuck pipe remediation using machine learning algorithms.” In Proc., SPE Annual Technical Conf. and Exhibition. Calgary, Canada: Society of Petroleum Engineers.
Ahn, B., J. Kim, and B. Choi. 2019. “Artificial intelligence-based machine learning considering flow and temperature of the pipeline for leak early detection using acoustic emission.” Eng. Fract. Mech. 210 (Apr): 381–392. https://doi.org/10.1016/j.engfracmech.2018.03.010.
Alshaikh, A., A. Magana-Mora, S. A. Gharbi, and A. Al-Yami. 2019. “Machine learning for detecting stuck pipe incidents: Data analytics and models evaluation.” In Proc., Int. Petroleum Technology Conf. Calgary, Canada: Society of Petroleum Engineers.
Alves Coelho, J., A. Glória, and P. Sebastião. 2020. “Precise water leak detection using machine learning and real-time sensor data.” IoT 1 (2): 474–493. https://doi.org/10.3390/iot1020026.
Anghel, C. I. 2009. “Risk assessment for pipelines with active defects based on artificial intelligence methods.” Int. J. Press. Vessels Pip. 86 (7): 403–411. https://doi.org/10.1016/j.ijpvp.2009.01.009.
ASCE. 2022. 2021 report card for America’s infrastructure. Reston, VA: ASCE.
Avci, O., O. Abdeljaber, S. Kiranyaz, M. Hussein, M. Gabbouj, and D. J. Inman. 2021. “A review of vibration-based damage detection in civil structures: From traditional methods to machine learning and deep learning applications.” Mech. Syst. Signal Process. 147 (Jan): 107077. https://doi.org/10.1016/j.ymssp.2020.107077.
Bagriacik, A., R. A. Davidson, M. W. Hughes, B. A. Bradley, and M. Cubrinovski. 2018. “Comparison of statistical and machine learning approaches to modeling earthquake damage to water pipelines.” Soil Dyn. Earthquake Eng. 112 (Sep): 76–88. https://doi.org/10.1016/j.soildyn.2018.05.010.
Banjara, N. K., S. Sasmal, and S. Voggu. 2020. “Machine learning supported acoustic emission technique for leakage detection in pipelines.” Int. J. Press. Vessels Pip. 188 (Dec): 104243. https://doi.org/10.1016/j.ijpvp.2020.104243.
Camacho-Navarro, J., M. Ruiz, R. Villamizar, L. Mujica, and G. Moreno-Beltrán. 2017. “Ensemble learning as approach for pipeline condition assessment.” J. Phys.: Conf. Ser. 842 (1): 012019. https://doi.org/10.1088/1742-6596/842/1/012019.
Caradot, N., M. Riechel, M. Fesneau, N. Hernandez, A. Torres, H. Sonnenberg, E. Eckert, N. Lengemann, J. Waschnewski, and P. Rouault. 2018. “Practical benchmarking of statistical and machine learning models for predicting the condition of sewer pipes in Berlin, Germany.” J. Hydroinf. 20 (5): 1131–1147. https://doi.org/10.2166/hydro.2018.217.
Chen, H., H. Ye, L. V. Chen, and H. Su. 2004. “Application of support vector machine learning to leak detection and location in pipelines.” In Vol. 3 of Proc., 21st IEEE Instrumentation and Measurement Technology Conf. (IEEE Cat. No. 04CH37510), 2273–2277. New York: IEEE.
Chen, Z., X. Li, W. Wang, Y. Li, L. Shi, and Y. Li. 2023. “Residual strength prediction of corroded pipelines using multilayer perceptron and modified feedforward neural network.” Reliab. Eng. Syst. Saf. 231 (Mar): 108980. https://doi.org/10.1016/j.ress.2022.108980.
Cheng, J. C., and M. Wang. 2018. “Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques.” Autom. Constr. 95 (Nov): 155–171. https://doi.org/10.1016/j.autcon.2018.08.006.
Chhotaray, G., and A. Kulshreshtha. 2019. “Defect detection in oil and gas pipeline: A machine learning application.” In Proc., Data Management, Analytics and Innovation, 177–184. Singapore: Springer.
Coelho, L. B., D. Zhang, Y. Van Ingelgem, D. Steckelmacher, A. Nowé, and H. Terryn. 2022. “Reviewing machine learning of corrosion prediction in a data-oriented perspective.” NPJ Mater. Degrad. 6 (1): 8. https://doi.org/10.1038/s41529-022-00218-4.
da Cruz, R. P., F. V. da Silva, and A. M. F. Fileti. 2020. “Machine learning and acoustic method applied to leak detection and location in low-pressure gas pipelines.” Clean Technol. Environ. Policy 22 (3): 627–638. https://doi.org/10.1007/s10098-019-01805-x.
Dawood, T., E. Elwakil, H. M. Novoa, and J. F. Gárate Delgado. 2020. “Water pipe failure prediction and risk models: State-of-the-art review.” Can. J. Civ. Eng. 47 (10): 1117–1127. https://doi.org/10.1139/cjce-2019-0481.
Dong, J., N. Wang, H. Fang, R. Wu, C. Zheng, D. Ma, and H. Hu. 2022. “Automatic damage segmentation in pavement videos by fusing similar feature extraction siamese network (SFE-SNet) and pavement damage segmentation capsule network (PDS-CapsNet).” Autom. Constr. 143 (Nov): 104537. https://doi.org/10.1016/j.autcon.2022.104537.
Duong, B. P., and J. M. Kim. 2018. “Pipeline fault diagnosis using wavelet entropy and ensemble deep neural technique.” In Proc., Int. Conf. on Image and Signal Processing, 292–300. Cham, Switzerland: Springer.
El-Abbasy, M. S., A. Senouci, T. Zayed, F. Mirahadi, and L. Parvizsedghy. 2014. “Artificial neural network models for predicting condition of offshore oil and gas pipelines.” Autom. Constr. 45 (Sep): 50–65. https://doi.org/10.1016/j.autcon.2014.05.003.
El-Abbasy, M. S., A. Senouci, T. Zayed, L. Parvizsedghy, and F. Mirahadi. 2016. “Unpiggable oil and gas pipeline condition forecasting models.” J. Perform. Constr. Facil. 30 (1): 04014202. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000716.
Fang, X., W. Guo, Q. Li, J. Zhu, Z. Chen, J. Yu, B. Zhou, and H. Yang. 2020. “Sewer pipeline fault identification using anomaly detection algorithms on video sequences.” IEEE Access 8 (Feb): 39574–39586. https://doi.org/10.1109/ACCESS.2020.2975887.
Fitchett, J. C., K. Karadimitriou, Z. West, and D. M. Hughes. 2020. “Machine learning for pipe condition assessments.” J. Am. Water Work Assoc. 112 (May): 50–55. https://doi.org/10.1002/awwa.1501.
Flah, M., I. Nunez, W. B. Chaabene, and M. L. Nehdi. 2021. “Machine learning algorithms in civil structural health monitoring: A systematic review.” Arch. Comput. Methods Eng. 28 (Jun): 2621–2643. https://doi.org/10.1007/s11831-020-09471-9.
Giraldo-González, M. M., and J. P. Rodríguez. 2020. “Comparison of statistical and machine learning models for pipe failure modeling in water distribution networks.” Water 12 (4): 1153. https://doi.org/10.3390/w12041153.
Guo, X., L. Zhang, W. Liang, and S. Haugen. 2018. “Risk identification of third-party damage on oil and gas pipelines through the Bayesian network.” J. Loss Prev. Process Ind. 54 (Jul): 163–178. https://doi.org/10.1016/j.jlp.2018.03.012.
Harvey, R. R., and E. A. McBean. 2014. “Predicting the structural condition of individual sanitary sewer pipes with random forests.” Can. J. Civ. Eng. 41 (4): 294–303. https://doi.org/10.1139/cjce-2013-0431.
Hassan, S. I., L. M. Dang, I. Mehmood, S. Im, C. Choi, J. Kang, Y. Park, and H. Moon. 2019. “Underground sewer pipe condition assessment based on convolutional neural networks.” Autom. Constr. 106 (Oct): 102849. https://doi.org/10.1016/j.autcon.2019.102849.
Hoang, N. D., and V. D. Tran. 2019. “Image processing-based detection of pipe corrosion using texture analysis and metaheuristic-optimized machine learning approach.” Comput. Intell. Neurosci. 2019: 8097213.
Hou, Y., Q. Li, C. Zhang, G. Lu, Z. Ye, Y. Chen, L. Wang, and D. Cao. 2021. “The state-of-the-art review on applications of intrusive sensing, image processing techniques, and machine learning methods in pavement monitoring and analysis.” Engineering 7 (6): 845–856. https://doi.org/10.1016/j.eng.2020.07.030.
Huang, Y., G. Qin, and G. Hu. 2022. “Failure pressure prediction by defect assessment and finite element modelling on pipelines containing a dent-corrosion defect.” Ocean Eng. 266 (Dec): 112875. https://doi.org/10.1016/j.oceaneng.2022.112875.
Isa, D., and R. Rajkumar. 2009. “Pipeline defect prediction using support vector machines.” Appl. Artif. Intell. 23 (8): 758–771. https://doi.org/10.1080/08839510903210589.
Jia, Z., L. Ren, H. Li, and W. Sun. 2018. “Pipeline leak localization based on FBG hoop strain sensors combined with BP neural network.” Appl. Sci. 8 (2): 146. https://doi.org/10.3390/app8020146.
Jiang, F., and S. Dong. 2020. “Collision failure risk analysis of falling object on subsea pipelines based on machine learning scheme.” Eng. Fail. Anal. 114 (Aug): 104601. https://doi.org/10.1016/j.engfailanal.2020.104601.
Khan, J. A., M. Irfan, S. Irawan, F. K. Yao, M. S. Abdul Rahaman, A. R. Shahari, A. R. Shahari, A. Glowacz, and N. Zeb. 2020. “Comparison of machine learning classifiers for accurate prediction of real-time stuck pipe incidents.” Energies 13 (14): 3683. https://doi.org/10.3390/en13143683.
Khodayari-Rostamabad, A., J. P. Reilly, N. K. Nikolova, J. R. Hare, and S. Pasha. 2009. “Machine learning techniques for the analysis of magnetic flux leakage images in pipeline inspection.” IEEE Trans. Magn. 45 (8): 3073–3084. https://doi.org/10.1109/TMAG.2009.2020160.
Kitchenham, B. 2004. Vol. 33 of Procedures for performing systematic reviews, 1–26. Keele, UK: Keele Univ.
Konstantinou, C., and I. Stoianov. 2020. “A comparative study of statistical and machine learning methods to infer causes of pipe breaks in water supply networks.” Urban Water J. 17 (6): 534–548. https://doi.org/10.1080/1573062X.2020.1800758.
Kumar, S. S., D. M. Abraham, M. R. Jahanshahi, T. Iseley, and J. Starr. 2018. “Automated defect classification in sewer closed circuit television inspections using deep convolutional neural networks.” Autom. Constr. 91 (Jul): 273–283. https://doi.org/10.1016/j.autcon.2018.03.028.
Kumar, S. S., M. Wang, D. M. Abraham, M. R. Jahanshahi, T. Iseley, and J. C. Cheng. 2020. “Deep learning–based automated detection of sewer defects in CCTV videos.” J. Comput. Civ. Eng. 34 (1): 04019047. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000866.
Lang, X., P. Li, Z. Hu, H. Ren, and Y. Li. 2017. “Leak detection and location of pipelines based on LMD and least squares twin support vector machine.” IEEE Access 5 (May): 8659–8668. https://doi.org/10.1109/ACCESS.2017.2703122.
LeCun, Y., Y. Bengio, and G. Hinton. 2015. “Deep learning.” Nature 521 (7553): 436–444. https://doi.org/10.1038/nature14539.
Li, D., A. Cong, and S. Guo. 2019. “Sewer damage detection from imbalanced CCTV inspection data using deep convolutional neural networks with hierarchical classification.” Autom. Constr. 101 (May): 199–208. https://doi.org/10.1016/j.autcon.2019.01.017.
Li, X., J. Wang, and G. Chen. 2022. “A machine learning methodology for probabilistic risk assessment of process operations: A case of subsea gas pipeline leak accidents.” Process Saf. Environ. Prot. 165 (Sep): 959–968. https://doi.org/10.1016/j.psep.2022.04.029.
Liang, W., and L. Zhang. 2012. “A wave change analysis (WCA) method for pipeline leak detection using Gaussian mixture model.” J. Loss Prev. Process Ind. 25 (1): 60–69. https://doi.org/10.1016/j.jlp.2011.06.017.
Liu, Y., X. Ma, Y. Li, Y. Tie, Y. Zhang, and J. Gao. 2019. “Water pipeline leakage detection based on machine learning and wireless sensor networks.” Sensors 19 (23): 5086. https://doi.org/10.3390/s19235086.
Liu, Z., and S. Li. 2020. “A sound monitoring system for prevention of underground pipeline damage caused by construction.” Autom. Constr. 113 (May): 103125. https://doi.org/10.1016/j.autcon.2020.103125.
Lu, H., S. Behbahani, M. Azimi, J. C. Matthews, S. Han, and T. Iseley. 2020a. “Trenchless construction technologies for oil and gas pipelines: State-of-the-art review.” J. Constr. Eng. Manage. 146 (6): 03120001. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001819.
Lu, H., S. Behbahani, X. Ma, and T. Iseley. 2021a. “A multi-objective optimizer-based model for predicting composite material properties.” Constr. Build. Mater. 284 (May): 122746. https://doi.org/10.1016/j.conbuildmat.2021.122746.
Lu, H., F. Cheng, X. Ma, and G. Hu. 2020b. “Short-term prediction of building energy consumption employing an improved extreme gradient boosting model: A case study of an intake tower.” Energy 203 (Jul): 117756. https://doi.org/10.1016/j.energy.2020.117756.
Lu, H., T. Iseley, S. Behbahani, and L. Fu. 2020c. “Leakage detection techniques for oil and gas pipelines: State-of-the-art.” Tunnelling Underground Space Technol. 98 (Apr): 103249. https://doi.org/10.1016/j.tust.2019.103249.
Lu, H., T. Iseley, J. Matthews, and W. Liao. 2021b. “Hybrid machine learning for pullback force forecasting during horizontal directional drilling.” Autom. Constr. 129 (Sep): 103810. https://doi.org/10.1016/j.autcon.2021.103810.
Lu, H., T. Iseley, J. Matthews, W. Liao, and A. Mohammadamin. 2021c. “An ensemble model based on relevance vector machine and multi-objective salp swarm algorithm for predicting burst pressure of corroded pipelines.” J. Pet. Sci. Eng. 203 (Aug): 108585. https://doi.org/10.1016/j.petrol.2021.108585.
Lu, H., J. C. Matthews, M. Azimi, and T. Iseley. 2020d. “Near real-time HDD pullback force prediction model based on improved radial basis function neural networks.” J. Pipeline Syst. Eng. Pract. 11 (4): 04020042. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000490.
Lu, H., H. Peng, Z. D. Xu, J. C. Matthews, N. Wang, and T. Iseley. 2022. “A feature selection–based intelligent framework for predicting maximum depth of corroded pipeline defects.” J. Perform. Constr. Facil. 36 (5): 04022044. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001753.
Lu, H., X. Wu, H. Ni, M. Azimi, X. Yan, and Y. Niu. 2020e. “Stress analysis of urban gas pipeline repaired by inserted hose lining method.” Composites, Part B 183 (Feb): 107657. https://doi.org/10.1016/j.compositesb.2019.107657.
Lu, H., Z. D. Xu, T. Iseley, and J. C. Matthews. 2021d. “Novel data-driven framework for predicting residual strength of corroded pipelines.” J. Pipeline Syst. Eng. Pract. 12 (4): 04021045. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000587.
Lu, H., Z. D. Xu, T. Iseley, H. Peng, and L. Fu. 2023. Pipeline inspection and health monitoring technology: The key to integrity management. Singapore city, Singapore: Springer Nature.
Ma, D., H. Fang, N. Wang, H. Lu, J. Matthews, and C. Zhang. 2023. “Transformer-optimized generation, detection, and tracking network for images with drainage pipeline defects.” Comput.-Aided Civ. Infrastruct. Eng. https://doi.org/10.1111/mice.12970.
Ma, D., H. Fang, N. Wang, C. Zhang, J. Dong, and H. Hu. 2022a. “Automatic detection and counting system for pavement cracks based on PCGAN and YOLO-MF.” IEEE Trans. Intell. Transp. Syst. 23 (11): 22166–22178. https://doi.org/10.1109/TITS.2022.3161960.
Ma, D., H. Fang, N. Wang, H. Zheng, J. Dong, and H. Hu. 2022b. “Automatic defogging, deblurring, and real-time segmentation system for sewer pipeline defects.” Autom. Constr. 144 (Dec): 104595. https://doi.org/10.1016/j.autcon.2022.104595.
Ma, Y., J. Zheng, Y. Liang, J. J. Klemeš, J. Du, Q. Liao, H. Lu, and B. Wang. 2022c. “Deeppipe: Theory-guided neural network method for predicting burst pressure of corroded pipelines.” Process Saf. Environ. Prot. 162 (Jun): 595–609. https://doi.org/10.1016/j.psep.2022.04.036.
Malek Mohammadi, M. 2019. “Development of condition prediction models for sanitary sewer pipes.” Doctoral dissertation, College of Engineering, Univ. of Texas at Arlington.
Malek Mohammadi, M., M. Najafi, N. Salehabadi, R. Serajiantehrani, and V. Kaushal. 2020. “Predicting condition of sanitary sewer pipes with gradient boosting tree.” In Proc., Pipelines 2020, 80–89. Reston, VA: ASCE.
Mandal, S. K., F. T. Chan, and M. K. Tiwari. 2012. “Leak detection of pipeline: An integrated approach of rough set theory and artificial bee colony trained SVM.” Expert Syst. Appl. 39 (3): 3071–3080. https://doi.org/10.1016/j.eswa.2011.08.170.
Martinez-Luengo, M., A. Kolios, and L. Wang. 2016. “Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm.” Renewable Sustainable Energy Rev. 64 (Oct): 91–105. https://doi.org/10.1016/j.rser.2016.05.085.
Mashford, J., D. Marlow, D. Tran, and R. May. 2011. “Prediction of sewer condition grade using support vector machines.” J. Comput. Civ. Eng. 25 (4): 283–290. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000089.
Maulud, D., and A. M. Abdulazeez. 2020. “A review on linear regression comprehensive in machine learning.” J. Appl. Sci. Technol. Trends 1 (4): 140–147. https://doi.org/10.38094/jastt1457.
Mazumder, R. K., A. M. Salman, and Y. Li. 2021. “Failure risk analysis of pipelines using data-driven machine learning algorithms.” Struct. Saf. 89 (Mar): 102047. https://doi.org/10.1016/j.strusafe.2020.102047.
Ni, L., J. Jiang, and Y. Pan. 2013. “Leak location of pipelines based on transient model and PSO-SVM.” J. Loss Prev. Process Ind. 26 (6): 1085–1093. https://doi.org/10.1016/j.jlp.2013.04.004.
Noble, W. S. 2006. “What is a support vector machine?” Nat. Biotechnol. 24 (12): 1565–1567. https://doi.org/10.1038/nbt1206-1565.
Oh, S. W., D. B. Yoon, G. J. Kim, J. H. Bae, and H. S. Kim. 2018. “Acoustic data condensation to enhance pipeline leak detection.” Nucl. Eng. Des. 327 (Feb): 198–211. https://doi.org/10.1016/j.nucengdes.2017.12.006.
Ossai, C. I. 2019. “A data-driven machine learning approach for corrosion risk assessment—A comparative study.” Big Data Cognit. Comput. 3 (2): 28. https://doi.org/10.3390/bdcc3020028.
Ossai, C. I. 2020. “Corrosion defect modelling of aged pipelines with a feed-forward multi-layer neural network for leak and burst failure estimation.” Eng. Fail. Anal. 110 (Mar): 104397. https://doi.org/10.1016/j.engfailanal.2020.104397.
Ouadah, A. 2018. “Pipeline defects risk assessment using machine learning and analytical hierarchy process.” In Proc., 2018 Int. Conf. on Applied Smart Systems (ICASS), 1–6. New York: IEEE.
Pal, M. 2005. “Random forest classifier for remote sensing classification.” Int. J. Remote Sens. 26 (1): 217–222. https://doi.org/10.1080/01431160412331269698.
Parvizsedghy, L., and T. Zayed. 2016. “Consequence of failure: Neurofuzzy-based prediction model for gas pipelines.” J. Perform. Constr. Facil. 30 (4): 04015073. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000817.
Peng, H., H. Lu, Z. D. Xu, Y. Wang, and Z. Zhang. 2023. “Predicting solid-particle erosion rate of pipelines using support vector machine with improved sparrow search algorithm.” J. Pipeline Syst. Eng. Pract. 14 (2): 04022077. https://doi.org/10.1061/JPSEA2.PSENG-1367.
Peng, S., Z. Zhang, E. Liu, W. Liu, and W. Qiao. 2021. “A new hybrid algorithm model for prediction of internal corrosion rate of multiphase pipeline.” J. Nat. Gas Sci. Eng. 85 (Jan): 103716. https://doi.org/10.1016/j.jngse.2020.103716.
Pérez-Pérez, E. J., F. R. López-Estrada, G. Valencia-Palomo, L. Torres, V. Puig, and J. D. Mina-Antonio. 2021. “Leak diagnosis in pipelines using a combined artificial neural network approach.” Control Eng. Pract. 107 (Feb): 104677. https://doi.org/10.1016/j.conengprac.2020.104677.
POWER. 2015. “Underground piping: Out of sight, out of mind, until it leaks.” Accessed June 15, 2021. https://www.powermag.com/underground-piping-out-of-sight-out-of-mind-until-it-leaks/.
Qin, G., and Y. F. Cheng. 2021. “Modeling of mechano-electrochemical interaction at a corrosion defect on a suspended gas pipeline and the failure pressure prediction.” Thin-Walled Struct. 160 (Mar): 107404. https://doi.org/10.1016/j.tws.2020.107404.
Qin, G., Y. F. Cheng, and P. Zhang. 2021. “Finite element modeling of corrosion defect growth and failure pressure prediction of pipelines.” Int. J. Press. Vessels Pip. 194 (Dec): 104509. https://doi.org/10.1016/j.ijpvp.2021.104509.
Qin, G., A. Xia, H. Lu, Y. Wang, R. Li, and C. Wang. 2023. “A hybrid machine learning model for predicting crater width formed by explosions of natural gas pipelines.” J. Loss Prev. Process Ind. 82 (Apr): 104994. https://doi.org/10.1016/j.jlp.2023.104994.
Rai, A., and J. M. Kim. 2021. “A novel pipeline leak detection approach independent of prior failure information.” Measurement 167 (Jan): 108284. https://doi.org/10.1016/j.measurement.2020.108284.
Robles-Velasco, A., P. Cortés, J. Muñuzuri, and L. Onieva. 2020. “Prediction of pipe failures in water supply networks using logistic regression and support vector classification.” Reliab. Eng. Syst. Saf. 196 (Apr): 106754. https://doi.org/10.1016/j.ress.2019.106754.
Saade, M., and S. Mustapha. 2020. “Assessment of the structural conditions in steel pipeline under various operational conditions—A machine learning approach.” Measurement 166 (Dec): 108262. https://doi.org/10.1016/j.measurement.2020.108262.
Sattar, A. M., Ö. F. Ertuğrul, B. Gharabaghi, E. A. McBean, and J. Cao. 2019. “Extreme learning machine model for water network management.” Neural Comput. Appl. 31 (1): 157–169. https://doi.org/10.1007/s00521-017-2987-7.
Seghier, M. E. A. B., B. Keshtegar, K. F. Tee, T. Zayed, R. Abbassi, and N. T. Trung. 2020. “Prediction of maximum pitting corrosion depth in oil and gas pipelines.” Eng. Fail. Anal. 112 (May): 104505. https://doi.org/10.1016/j.engfailanal.2020.104505.
Sfar Zaoui, W., T. Lauber, C. Pohl, M. Kerk, T. Glaeser, and W. Jelinek. 2020. “Machine-learning distributed-temperature-sensing-based pipeline leak detection.” In Proc., Abu Dhabi International Petroleum Exhibition & Conf. Calgary, Canada: Society of Petroleum Engineers.
Shi, F., Y. Liu, Z. Liu, and E. Li. 2018. “Prediction of pipe performance with stacking ensemble learning based approaches.” J. Intell. Fuzzy Syst. 34 (6): 3845–3855. https://doi.org/10.3233/JIFS-169556.
Shi, F., X. Peng, Z. Liu, E. Li, and Y. Hu. 2020. “A data-driven approach for pipe deformation prediction based on soil properties and weather conditions.” Sustainable Cities Soc. 55 (Apr): 102012. https://doi.org/10.1016/j.scs.2019.102012.
Shravani, D., Y. R. Prajwal, S. B. Prapulla, N. G. R. Salanke, G. Shobha, and S. F. Ahmad. 2019. “A machine learning approach to water leak localization.” In Vol. 4 of Proc., 2019 4th Int. Conf. on Computational Systems and Information Technology for Sustainable Solution (CSITSS), 1–6. New York: IEEE.
Snider, B., and E. A. McBean. 2020. “Improving urban water security through pipe-break prediction models: Machine learning or survival analysis.” J. Environ. Eng. 146 (3): 04019129. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001657.
Sousa, V., J. P. Matos, and N. Matias. 2014. “Evaluation of artificial intelligence tool performance and uncertainty for predicting sewer structural condition.” Autom. Constr. 44 (Aug): 84–91. https://doi.org/10.1016/j.autcon.2014.04.004.
Sun, J., Q. Xiao, J. Wen, and Y. Zhang. 2016. “Natural gas pipeline leak aperture identification and location based on local mean decomposition analysis.” Measurement 79 (Feb): 147–157. https://doi.org/10.1016/j.measurement.2015.10.015.
Vladeanu, G., and J. Matthews. 2019. “Wastewater pipe condition rating model using multicriteria decision analysis.” J. Water Resour. Plann. Manage. 145 (12): 04019058. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001134.
Wang, C., Y. Zhang, J. Song, Q. Liu, and H. Dong. 2019. “A novel optimized SVM algorithm based on PSO with saturation and mixed time-delays for classification of oil pipeline leak detection.” Syst. Sci. Control Eng. 7 (1): 75–88. https://doi.org/10.1080/21642583.2019.1573386.
Wang, C., J. Zheng, Y. Liang, M. Li, W. Chen, Q. Liao, and H. Zhang. 2022a. “Deeppipe: A hybrid model for multi-product pipeline condition recognition based on process and data coupling.” Comput. Chem. Eng. 160 (Apr): 107733. https://doi.org/10.1016/j.compchemeng.2022.107733.
Wang, C., J. Zheng, Y. Liang, Q. Liao, B. Wang, and H. Zhang. 2022b. “Deeppipe: Operating condition recognition of multiproduct pipeline based on KPCA-CNN.” J. Pipeline Syst. Eng. Pract. 13 (2): 04022006. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000641.
Wang, C., J. Zheng, Y. Liang, B. Wang, J. J. Klemeš, Z. Zhu, and Q. Liao. 2022c. “Deeppipe: An intelligent monitoring framework for operating condition of multi-product pipelines.” Energy 261 (Dec): 125325. https://doi.org/10.1016/j.energy.2022.125325.
Wang, M., S. S. Kumar, and J. C. Cheng. 2021. “Automated sewer pipe defect tracking in CCTV videos based on defect detection and metric learning.” Autom. Constr. 121 (Jan): 103438. https://doi.org/10.1016/j.autcon.2020.103438.
Wang, N., Q. Zhao, S. Li, X. Zhao, and P. Zhao. 2018. “Damage classification for masonry historic structures using convolutional neural networks based on still images.” Comput.-Aided Civ. Infrastruct. Eng. 33 (12): 1073–1089. https://doi.org/10.1111/mice.12411.
Wang, X., M. S. Ghidaoui, and P. J. Lee. 2020. “Linear model and regularization for transient wave–based pipeline-condition assessment.” J. Water Resour. Plann. Manage. 146 (5): 04020028. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001205.
Wang, Z., and S. Li. 2020. “Data-driven risk assessment on urban pipeline network based on a cluster model.” Reliab. Eng. Syst. Saf. 196 (Apr): 106781. https://doi.org/10.1016/j.ress.2019.106781.
Winkler, D., M. Haltmeier, M. Kleidorfer, W. Rauch, and F. Tscheikner-Gratl. 2018. “Pipe failure modelling for water distribution networks using boosted decision trees.” Struct. Infrastruct. Eng. 14 (10): 1402–1411. https://doi.org/10.1080/15732479.2018.1443145.
Wu, W. 2015. “Oil and gas pipeline risk assessment model by fuzzy inference systems and artificial neural network.” Doctoral dissertation, Faculty of Graduate Studies and Research, Univ. of Regina.
Xiao, R., Q. Hu, and J. Li. 2021. “A model-based health indicator for leak detection in gas pipeline systems.” Measurement 171 (Feb): 108843. https://doi.org/10.1016/j.measurement.2020.108843.
Xie, J., X. Xu, and S. Dubljevic. 2019. “Long range pipeline leak detection and localization using discrete observer and support vector machine.” AIChE J. 65 (7): e16532. https://doi.org/10.1002/aic.16532.
Xu, D., L. Chen, C. Yu, S. Zhang, X. Zhao, and X. Lai. 2023. “Failure analysis and control of natural gas pipelines under excavation impact based on machine learning scheme.” Int. J. Press. Vessels Pip. 201 (Feb): 104870. https://doi.org/10.1016/j.ijpvp.2022.104870.
Xu, Z., Y. Yang, and A. Miao. 2021a. “Dynamic analysis and parameter optimization of pipelines with multidimensional vibration isolation and mitigation device.” J. Pipeline Syst. Eng. Pract. 12 (1): 04020058. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000504.
Xu, Z., C. Zhu, and L. Shao. 2021b. “Damage identification of pipeline based on ultrasonic guided wave and wavelet denoising.” J. Pipeline Syst. Eng. Pract. 12 (4): 04021051. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000600.
Xue, P., Y. Jiang, Z. Zhou, X. Chen, X. Fang, and J. Liu. 2020. “Machine learning-based leakage fault detection for district heating networks.” Energy Build. 223 (Sep): 110161. https://doi.org/10.1016/j.enbuild.2020.110161.
Yang, M., and T. Su. 2008. “Automated diagnosis of sewer pipe defects based on machine learning approaches.” Expert Syst. Appl. 35 (3): 1327–1337. https://doi.org/10.1016/j.eswa.2007.08.013.
Yang, S., L. Zhang, J. Fan, and B. Sun. 2021. “Experimental study on erosion behavior of fracturing pipeline involving tensile stress and erosion prediction using random forest regression.” J. Nat. Gas Sci. Eng. 87 (Mar): 103760. https://doi.org/10.1016/j.jngse.2020.103760.
Yang, Y., S. Li, and P. Zhang. 2022. “Data-driven accident consequence assessment on urban gas pipeline network based on machine learning.” Reliab. Eng. Syst. Saf. 219 (Mar): 108216. https://doi.org/10.1016/j.ress.2021.108216.
Ye, X. W., T. Jin, and C. B. Yun. 2019. “A review on deep learning-based structural health monitoring of civil infrastructures.” Smart Struct. Syst. 24 (5): 567–585.
Yin, X., Y. Chen, A. Bouferguene, H. Zaman, M. Al-Hussein, and L. Kurach. 2020. “A deep learning-based framework for an automated defect detection system for sewer pipes.” Autom. Constr. 109 (Jan): 102967. https://doi.org/10.1016/j.autcon.2019.102967.
Yu, X., Y. Lu, and Q. Gao. 2021. “Pipeline image diagnosis algorithm based on neural immune ensemble learning.” Int. J. Press. Vessels Pip. 189 (Feb): 104249. https://doi.org/10.1016/j.ijpvp.2020.104249.
Zadkarami, M., M. Shahbazian, and K. Salahshoor. 2017. “Pipeline leak diagnosis based on wavelet and statistical features using Dempster–Shafer classifier fusion technique.” Process Saf. Environ. Prot. 105 (Jan): 156–163. https://doi.org/10.1016/j.psep.2016.11.002.
Zajam, S., T. Joshi, and B. Bhattacharya. 2019. “Application of wavelet analysis and machine learning on vibration data from gas pipelines for structural health monitoring.” Procedia Struct. Integrity 14 (Jan): 712–719. https://doi.org/10.1016/j.prostr.2019.05.089.
Zeng, W., J. Gong, A. C. Zecchin, M. F. Lambert, A. R. Simpson, and B. S. Cazzolato. 2018. “Condition assessment of water pipelines using a modified layer-peeling method.” J. Hydraul. Eng. 144 (12): 04018076. https://doi.org/10.1061/(ASCE)HY.1943-7900.0001547.
Zheng, J., Y. Dai, Y. Liang, Q. Liao, and H. Zhang. 2020. “An online real-time estimation tool of leakage parameters for hazardous liquid pipelines.” Int. J. Crit. Infrastruct. Prot. 31 (Dec): 100389. https://doi.org/10.1016/j.ijcip.2020.100389.
Zheng, J., J. Du, Y. Liang, Q. Liao, Z. Li, H. Zhang, and Y. Wu. 2021a. “Deeppipe: A semi-supervised learning for operating condition recognition of multi-product pipelines.” Process Saf. Environ. Prot. 150 (Jun): 510–521. https://doi.org/10.1016/j.psep.2021.04.031.
Zheng, J., J. Du, Y. Liang, B. Wang, M. Li, Q. Liao, and N. Xu. 2023. “Deeppipe: A hybrid intelligent framework for real-time batch tracking of multi-product pipelines.” Chem. Eng. Res. Des. 191 (Mar): 236–248. https://doi.org/10.1016/j.cherd.2022.12.036.
Zheng, J., J. Du, Y. Liang, C. Wang, Q. Liao, and H. Zhang. 2021b. “Deeppipe: Theory-guided LSTM method for monitoring pressure after multi-product pipeline shutdown.” Process Saf. Environ. Prot. 155 (Nov): 518–531. https://doi.org/10.1016/j.psep.2021.09.046.
Zheng, J., Y. Liang, N. Xu, B. Wang, T. Zheng, Z. Li, Q. Liao, and H. Zhang. 2021c. “Deeppipe: A customized generative model for estimations of liquid pipeline leakage parameters.” Comput. Chem. Eng. 149 (Jun): 107290. https://doi.org/10.1016/j.compchemeng.2021.107290.
Zheng, J., C. Wang, Y. Liang, Q. Liao, Z. Li, and B. Wang. 2022. “Deeppipe: A deep-learning method for anomaly detection of multi-product pipelines.” Energy 259 (Nov): 125025. https://doi.org/10.1016/j.energy.2022.125025.
Zhou, B., V. Lau, and X. Wang. 2019a. “Machine-learning-based leakage-event identification for smart water supply systems.” IEEE Internet Things J. 7 (3): 2277–2292. https://doi.org/10.1109/JIOT.2019.2958920.
Zhou, J., J. Sun, Y. Wang, and F. Chen. 2017. “Wrapping practical problems into a machine learning framework: Using water pipe failure prediction as a case study.” Int. J. Intell. Syst. Technol. Appl. 16 (3): 191–207. https://doi.org/10.1504/IJISTA.2017.085355.
Zhou, M., Z. Pan, Y. Liu, Q. Zhang, Y. Cai, and H. Pan. 2019b. “Leak detection and location based on ISLMD and CNN in a pipeline.” IEEE Access 7 (Mar): 30457–30464. https://doi.org/10.1109/ACCESS.2019.2902711.
Zhu, L. X., and L. Zou. 2005. “Application of genetic algorithm in decision-making optimization of underground gas pipeline risk model.” In Vol. 5 of Proc., 2005 Int. Conf. on Machine Learning and Cybernetics, 2988–2992. New York: IEEE.
Information & Authors
Information
Published In
Journal of Pipeline Systems Engineering and Practice
Volume 14 • Issue 3 • August 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Dec 10, 2022
Accepted: Mar 10, 2023
Published online: May 13, 2023
Published in print: Aug 1, 2023
Discussion open until: Oct 13, 2023
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Lei Xu, Pengfei Yu, Shaomu Wen, Yongfan Tang, Yunfu Wang, Yuan Tian, Ting Mao, Changjun Li, Visualization and Analysis of Oil and Gas Pipeline Corrosion Research: A Bibliometric Data-Mining Approach, Journal of Pipeline Systems Engineering and Practice, 10.1061/JPSEA2.PSENG-1605, 15, 3, (2024).
- Zhiheng Xia, Zhao-Dong Xu, Hongfang Lu, Haoyan Peng, Xinyu Liu, Yankun Jia, Leakage Simulation and Prediction for High-Pressure Natural Gas Pipeline in a Confined Space, Journal of Pipeline Systems Engineering and Practice, 10.1061/JPSEA2.PSENG-1555, 15, 2, (2024).
- Xulei Zang, Zhao-Dong Xu, Chen Zhu, Hongfang Lu, Haoyan Peng, Zhenwu Zhang, A New Pipeline Condition Assessment Method Using a Multicomponent Interferometric Dictionary for Quantification of Pipeline Notches, Journal of Pipeline Systems Engineering and Practice, 10.1061/JPSEA2.PSENG-1518, 15, 2, (2024).