{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T14:51:39Z","timestamp":1782485499937,"version":"3.54.5"},"reference-count":229,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100020624","name":"GLWA","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100020624","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007270","name":"University of Michigan","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007270","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Advanced Engineering Informatics"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.aei.2026.104978","type":"journal-article","created":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T00:32:19Z","timestamp":1781829139000},"page":"104978","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["When sensing meets computational Intelligence: A comprehensive survey on pipeline damage detection and localization"],"prefix":"10.1016","volume":"76","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2037-0928","authenticated-orcid":false,"given":"Salman","family":"Khalid","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Graham","family":"Bell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Curt","family":"Wolf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John W.","family":"Norton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sherif","family":"El-Tawil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.aei.2026.104978_b0005","doi-asserted-by":"crossref","unstructured":"S. Kenny, Chapter Seven - Offshore Pipelines\u2014Elements of Managing Risk, in: F. Khan, R. Abbassi (Eds.), Methods Chem. Process Saf., Elsevier, 2018: pp. 289\u2013325. https:\/\/doi.org\/10.1016\/bs.mcps.2018.04.005.","DOI":"10.1016\/bs.mcps.2018.04.005"},{"key":"10.1016\/j.aei.2026.104978_b0010","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.engfailanal.2014.04.025","article-title":"Challenges to the integrity of old pipelines buried in stable ground","volume":"42","author":"Otegui","year":"2014","journal-title":"Eng. Fail. Anal."},{"key":"10.1016\/j.aei.2026.104978_b0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijpvp.2023.104973","article-title":"Strain-based design and assessment for pipeline integrity management: a review of applications and gaps","volume":"204","author":"Schell","year":"2023","journal-title":"Int. J. Press. Vessels Pip."},{"key":"10.1016\/j.aei.2026.104978_b0020","doi-asserted-by":"crossref","first-page":"60","DOI":"10.2166\/wpt.2021.094","article-title":"A review of water quality factors in water main failure prediction models","volume":"17","author":"Monfared","year":"2021","journal-title":"Water Pract. Technol."},{"key":"10.1016\/j.aei.2026.104978_b0025","first-page":"1","article-title":"Water Main break rates in the USA and Canada: a Comprehensive Study","author":"Barfuss","year":"2023","journal-title":"Y Rep."},{"key":"10.1016\/j.aei.2026.104978_b0030","doi-asserted-by":"crossref","first-page":"2783","DOI":"10.3390\/su15032783","article-title":"A comprehensive analysis of in-line inspection tools and technologies for steel oil and gas pipelines","volume":"15","author":"Parlak","year":"2023","journal-title":"Sustainability"},{"key":"10.1016\/j.aei.2026.104978_b0035","unstructured":"I.L. Akrasi, M.S. Hamaqi, T.I. Hathloul, A.F. Almakinzi, A.A. Naim, Field Experience on Why Pipeline In-Line Inspections (ILI) Fail, in: NACE Corros., NACE, 2021: p. D091S037R008. https:\/\/onepetro.org\/NACECORR\/proceedings-abstract\/CORR21\/9-CORR21\/464053 (accessed March 31, 2025)."},{"key":"10.1016\/j.aei.2026.104978_b0040","article-title":"Pipeline Inspection and Health monitoring Technology: the Key to Integrity Management, Springer Nature Singapore","author":"Lu","year":"2023","journal-title":"Singapore"},{"key":"10.1016\/j.aei.2026.104978_b0045","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1515\/adms-2017-0014","article-title":"Inspection of Gas Pipelines using magnetic Flux Leakage Technology","volume":"17","author":"Usarek","year":"2017","journal-title":"Adv. Mater. Sci."},{"key":"10.1016\/j.aei.2026.104978_b0050","first-page":"274","article-title":"Applications of ultrasonic techniques in oil and gas pipeline industries: a review","volume":"5","author":"Alobaidi","year":"2015","journal-title":"Am. J. Oper. Res."},{"key":"10.1016\/j.aei.2026.104978_b0055","doi-asserted-by":"crossref","first-page":"3862","DOI":"10.3390\/s21113862","article-title":"Pipeline in-line inspection method, instrumentation and data management","volume":"21","author":"Ma","year":"2021","journal-title":"Sensors"},{"key":"10.1016\/j.aei.2026.104978_b0060","doi-asserted-by":"crossref","first-page":"394","DOI":"10.4236\/jep.2012.35049","article-title":"Oil\/gas pipeline leak inspection and repair in underwater poor visibility conditions: challenges and perspectives","volume":"3","author":"Jasper","year":"2012","journal-title":"J. Environ. Prot."},{"key":"10.1016\/j.aei.2026.104978_b0065","doi-asserted-by":"crossref","DOI":"10.1115\/1.4048324","article-title":"Mechanical damage and response of a buried steel pipeline under subsurface explosion load","volume":"143","author":"Zhang","year":"2021","journal-title":"J. Press. Vessel. Technol."},{"key":"10.1016\/j.aei.2026.104978_b0070","doi-asserted-by":"crossref","DOI":"10.1109\/JSEN.2025.3551731","article-title":"A High-Sensitivity Strain Sensor with Femtosecond Fiber Bragg Grating for Pipeline Deformation monitoring","author":"Gong","year":"2025","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.aei.2026.104978_b0075","unstructured":"F. Saleem, Z. Ahmad, J.-M. Kim, Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals., Appl. Sci. 2076-3417 15 (2025). https:\/\/search.ebscohost.com\/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=20763417&AN=182432220&h=SrdNhYDjEmypRkMPsMm1z55v9BpOXW534pat8gj%2FfEIT%2FWqWOjsgxMlqfzAlNz7eUu9degZvqfJ1zQAvitaddA%3D%3D&crl=c (accessed March 31, 2025)."},{"key":"10.1016\/j.aei.2026.104978_b0080","doi-asserted-by":"crossref","first-page":"101246","DOI":"10.1109\/ACCESS.2021.3096930","article-title":"State-of-the-art review on the acoustic emission source localization techniques","volume":"9","author":"Hassan","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.aei.2026.104978_b0085","doi-asserted-by":"crossref","first-page":"811","DOI":"10.3390\/s21030811","article-title":"Review of current guided wave ultrasonic testing (GWUT) limitations and future directions","volume":"21","author":"Olisa","year":"2021","journal-title":"Sensors"},{"key":"10.1016\/j.aei.2026.104978_b0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.jss.2023.111615","article-title":"The pipeline for the continuous development of artificial intelligence models\u2014Current state of research and practice","volume":"199","author":"Steidl","year":"2023","journal-title":"J. Syst. Softw."},{"key":"10.1016\/j.aei.2026.104978_b0095","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.engfracmech.2018.03.010","article-title":"Artificial intelligence-based machine learning considering flow and temperature of the pipeline for leak early detection using acoustic emission","volume":"210","author":"Ahn","year":"2019","journal-title":"Eng. Fract. Mech."},{"key":"10.1016\/j.aei.2026.104978_b0100","unstructured":"N.D. Nwiabu, K.E. Igbudu, Oil and Gas Pipeline Monitoring using Artificial Neural Network, Int. J. Comput. Appl. 975 (n.d.) 8887."},{"key":"10.1016\/j.aei.2026.104978_b0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.ultras.2022.106685","article-title":"CNN-LSTM network-based damage detection approach for copper pipeline using laser ultrasonic scanning","volume":"121","author":"Huang","year":"2022","journal-title":"Ultrasonics"},{"key":"10.1016\/j.aei.2026.104978_b0110","first-page":"1","article-title":"A novel deep offline-to-online transfer learning framework for pipeline leakage detection with small samples","volume":"72","author":"Wang","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.aei.2026.104978_b0115","doi-asserted-by":"crossref","unstructured":"J. Zhang, H. Wang, G. Ji, L. Zhuo, Z. Zhao, X. Liu, W. Chen, Digital Twin System Utilizing Hybrid Approach of Physics Model and Machine Learning for Pipe Stuck Early Identification and Warning Based on Real-Time Data, in: n.d. https:\/\/dx.doi.org\/10.4043\/34642-MS (accessed April 17, 2025).","DOI":"10.4043\/34642-MS"},{"key":"10.1016\/j.aei.2026.104978_b0120","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1007\/s11831-019-09388-y","article-title":"Applications of Generative Adversarial Networks (GANs): an Updated Review","volume":"28","author":"Alqahtani","year":"2021","journal-title":"Arch. Comput. Methods Eng."},{"key":"10.1016\/j.aei.2026.104978_b0125","article-title":"Deep learning-based reliability model for oil and gas pipeline subjected to stress corrosion cracking: a review and concept","volume":"48","author":"Soomro","year":"2021","journal-title":"J. Hunan Univ. Nat. Sci."},{"key":"10.1016\/j.aei.2026.104978_b0130","article-title":"Xue, Fiber optic sensing technology in underground pipeline health monitoring: a comprehensive review","author":"Wen","year":"2024","journal-title":"Struct. Health Monit."},{"key":"10.1016\/j.aei.2026.104978_b0135","doi-asserted-by":"crossref","DOI":"10.2196\/13659","article-title":"Artificial intelligence and the implementation challenge","volume":"21","author":"Shaw","year":"2019","journal-title":"J. Med. Internet Res."},{"key":"10.1016\/j.aei.2026.104978_b0140","article-title":"Integrating Leading-Edge Artificial Intelligence (AI), internet of things (IoT), and big Data Technologies for Smart and Sustainable Architecture","author":"Rane","year":"2023","journal-title":"Engineering and Construction (AEC) Industry: Challenges and Future Directions"},{"key":"10.1016\/j.aei.2026.104978_b0145","series-title":"Using Interpretable Machine Learning for Data-Driven Decision Support for Infrastructure Operation & Maintenance","author":"Chatzi","year":"2022"},{"key":"10.1016\/j.aei.2026.104978_b0150","doi-asserted-by":"crossref","first-page":"1715","DOI":"10.1007\/s12666-020-02002-x","article-title":"Leak Detection System for Long-Distance Onshore and Offshore Gas Pipeline using Acoustic Emission Technology. a Review","volume":"73","author":"Lukonge","year":"2020","journal-title":"Trans. Indian Inst. Met."},{"key":"10.1016\/j.aei.2026.104978_b0155","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2022.104226","article-title":"Acoustic leak detection approaches for water pipelines","volume":"138","author":"Fan","year":"2022","journal-title":"Autom. Constr."},{"key":"10.1016\/j.aei.2026.104978_b0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2021.107994","article-title":"A comprehensive review of acoustic based leak localization method in pressurized pipelines","volume":"161","author":"Hu","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.aei.2026.104978_b0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.engfailanal.2021.105810","article-title":"Integrity assessment of corroded oil and gas pipelines using machine learning: a systematic review","volume":"131","author":"Soomro","year":"2022","journal-title":"Eng. Fail. Anal."},{"key":"10.1016\/j.aei.2026.104978_b0170","doi-asserted-by":"crossref","DOI":"10.1016\/j.engfailanal.2023.107060","article-title":"Leak detection and localization techniques in oil and gas pipeline: a bibliometric and systematic review","volume":"146","author":"Yuan","year":"2023","journal-title":"Eng. Fail. Anal."},{"key":"10.1016\/j.aei.2026.104978_b0175","doi-asserted-by":"crossref","DOI":"10.3390\/s19112548","article-title":"Recent advances in Pipeline monitoring and Oil Leakage Detection Technologies: Principles and Approaches","volume":"19","author":"Adegboye","year":"2019","journal-title":"Sensors"},{"key":"10.1016\/j.aei.2026.104978_b0180","article-title":"The physics of fatigue crack propagation","volume":"108928","author":"Sangid","year":"2025","journal-title":"Int. J. Fatigue"},{"key":"10.1016\/j.aei.2026.104978_b0185","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.msea.2015.06.030","article-title":"Investigation of X70 line pipe steel fracture during single edge-notched tensile testing using acoustic emission monitoring","volume":"640","author":"Chuluunbat","year":"2015","journal-title":"Mater. Sci. Eng. A"},{"key":"10.1016\/j.aei.2026.104978_b0190","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijfatigue.2022.106860","article-title":"Identification and prediction of fatigue crack growth under different stress ratios using acoustic emission data","volume":"160","author":"Chai","year":"2022","journal-title":"Int. J. Fatigue"},{"key":"10.1016\/j.aei.2026.104978_b0195","doi-asserted-by":"crossref","unstructured":"A. Smith, Dixon ,Neil, G. and Fowmes, Monitoring buried pipe deformation using acoustic emission: quantification of attenuation, Int. J. Geotech. Eng. 11 (2017) 418\u2013430. https:\/\/doi.org\/10.1080\/19386362.2016.1227581.","DOI":"10.1080\/19386362.2016.1227581"},{"key":"10.1016\/j.aei.2026.104978_b0200","first-page":"703","article-title":"Characterization of Cracks and Delaminations using Pwas Ad Lamb Wave based Time-Frequency Methods","volume":"3","author":"Gangadharan","year":"2010","journal-title":"Int. J. Smart Sens. Intell. Syst."},{"key":"10.1016\/j.aei.2026.104978_b0205","doi-asserted-by":"crossref","unstructured":"V. Giurgiutiu, Chapter 7 - Piezoelectric Wafer Active Sensors \u2013 PWAS Transducers, in: V. Giurgiutiu (Ed.), Struct. Health Monit. Piezoelectric Wafer Act. Sens. Second Ed., Academic Press, Oxford, 2014: pp. 357\u2013394. https:\/\/doi.org\/10.1016\/B978-0-12-418691-0.00007-1.","DOI":"10.1016\/B978-0-12-418691-0.00007-1"},{"key":"10.1016\/j.aei.2026.104978_b0210","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijpvp.2023.105033","article-title":"Ultrasonic guided wave techniques and applications in pipeline defect detection: a review","volume":"206","author":"Zang","year":"2023","journal-title":"Int. J. Press. Vessels Pip."},{"key":"10.1016\/j.aei.2026.104978_b0215","doi-asserted-by":"crossref","first-page":"1358","DOI":"10.1007\/s11665-014-0880-4","article-title":"Effect of Strength and Microstructure on stress Corrosion Cracking Behavior and Mechanism of X80 Pipeline Steel in High pH Carbonate\/Bicarbonate solution","volume":"23","author":"Zhu","year":"2014","journal-title":"J. Mater. Eng. Perform."},{"key":"10.1016\/j.aei.2026.104978_b0220","article-title":"Analysis and experiment of structural geometry for improved strain sensitivity of FBG sensors","author":"Yadav","year":"2024","journal-title":"J. Opt."},{"key":"10.1016\/j.aei.2026.104978_b0225","doi-asserted-by":"crossref","unstructured":"J.D. Betancur R\u00edos, C. Eli\u00e9cer Torres, J.H. Aristizabal, A. Galvis, R.A. D\u00edaz, D. Trespalacios, H.O. Cuevas, Monitoring Stress\/Strain in Buried Pipelines Through the Use of Fiber Bragg Grating Sensors, in: n.d. https:\/\/doi.org\/10.1115\/IPG2015-8540.","DOI":"10.1115\/IPG2015-8540"},{"key":"10.1016\/j.aei.2026.104978_b0230","doi-asserted-by":"crossref","first-page":"347","DOI":"10.32604\/sdhm.2019.05139","article-title":"Fiber Grating-based Strain Sensor Array for Health monitoring of Pipelines","volume":"13","author":"Wang","year":"2019","journal-title":"Struct. Durab. Health Monit."},{"key":"10.1016\/j.aei.2026.104978_b0235","series-title":"Gas Technology Institute","author":"Robertson","year":"2019"},{"key":"10.1016\/j.aei.2026.104978_b0240","series-title":"Electroanalytical Chemistry: a Series of advances","author":"Bard","year":"2011"},{"key":"10.1016\/j.aei.2026.104978_b0245","doi-asserted-by":"crossref","first-page":"4959","DOI":"10.3390\/s21154959","article-title":"Failure Detection Methods for Pipeline Networks: from Acoustic Sensing to Cyber-Physical Systems","volume":"21","author":"Wong","year":"2021","journal-title":"Sensors"},{"key":"10.1016\/j.aei.2026.104978_b0250","first-page":"1","article-title":"A MEMS IMU-Based Air-Propelled Positioning Ball for Small-Diameter Underground Pipelines Localization","author":"Niu","year":"2025","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.aei.2026.104978_b0255","doi-asserted-by":"crossref","DOI":"10.1016\/j.yofte.2024.103911","article-title":"State-of-The-Art application and challenges of optical fibre distributed acoustic sensing in civil engineering","volume":"87","author":"Ghazali","year":"2024","journal-title":"Opt. Fiber Technol."},{"key":"10.1016\/j.aei.2026.104978_b0260","first-page":"1","article-title":"Analysis of Magnetic-Flux Leakage (MFL) Data for Pipeline Corrosion Assessment","volume":"56","author":"Peng","year":"2020","journal-title":"IEEE Trans. Magn."},{"key":"10.1016\/j.aei.2026.104978_b0265","unstructured":"N. Sathappan, Magnetic Flux Leakage techniques for detecting corrosion of pipes, (n.d.)."},{"key":"10.1016\/j.aei.2026.104978_b0270","series-title":"Relation between hardness and ultrasonic velocity on pipeline steel welded joints","author":"Carre\u00f3n","year":"2016"},{"key":"10.1016\/j.aei.2026.104978_b0275","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijpvp.2022.104808","article-title":"Modelling and characterisation ultrasonic phased array transducers for pipe inspections","volume":"200","author":"Hampson","year":"2022","journal-title":"Int. J. Press. Vessels Pip."},{"key":"10.1016\/j.aei.2026.104978_b0280","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.engfailanal.2017.08.012","article-title":"Pitting corrosion failure analysis of a wet gas pipeline","volume":"82","author":"Mansoori","year":"2017","journal-title":"Eng. Fail. Anal."},{"key":"10.1016\/j.aei.2026.104978_b0285","unstructured":"Ultrasonic Circumferential Guided Wave for Pitting-Type Corrosion Imaging at Inaccessible Pipe-Support Locations | J. Pressure Vessel Technol. | ASME Digital Collection, (n.d.). https:\/\/asmedigitalcollection.asme.org\/pressurevesseltech\/article\/130\/2\/021502\/442768\/Ultrasonic-Circumferential-Guided-Wave-for-Pitting (accessed April 18, 2025)."},{"key":"10.1016\/j.aei.2026.104978_b0290","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1016\/j.ndteint.2009.06.009","article-title":"Development of a magnetic sensor for detection and sizing of internal pipeline corrosion defects","volume":"42","author":"Gloria","year":"2009","journal-title":"NDT E Int."},{"key":"10.1016\/j.aei.2026.104978_b0295","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1016\/j.jngse.2014.10.001","article-title":"A comprehensive review of solid particle erosion modeling for oil and gas wells and pipelines applications","volume":"21","author":"Parsi","year":"2014","journal-title":"J. Nat. Gas Sci. Eng."},{"key":"10.1016\/j.aei.2026.104978_b0300","doi-asserted-by":"crossref","unstructured":"G. Instanes, A.O. Pedersen, Corrosion-Erosion Monitoring Systems for Manageing Asset Integrity, in: n.d. https:\/\/dx.doi.org\/10.2118\/188949-MS (accessed April 18, 2025).","DOI":"10.2118\/188949-MS"},{"key":"10.1016\/j.aei.2026.104978_b0305","doi-asserted-by":"crossref","DOI":"10.1016\/j.apacoust.2021.107948","article-title":"Monitoring of corrosion effects in pipes with multi-mode acoustic signals","volume":"178","author":"Ju","year":"2021","journal-title":"Appl. Acoust."},{"key":"10.1016\/j.aei.2026.104978_b0310","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1080\/09715010.2018.1548309","article-title":"Pressure surges during filling of partially empty undulating pipelines","volume":"27","author":"Balacco","year":"2021","journal-title":"ISH J. Hydraul. Eng."},{"key":"10.1016\/j.aei.2026.104978_b0315","doi-asserted-by":"crossref","unstructured":"A. Hussein, H. Abd Alhameed, SCADA Based Pipelines Cathodic Protection Systems Remote Monitoring Project, in: n.d. https:\/\/dx.doi.org\/10.2118\/223174-MS (accessed April 18, 2025).","DOI":"10.2118\/223174-MS"},{"key":"10.1016\/j.aei.2026.104978_b0320","doi-asserted-by":"crossref","DOI":"10.1016\/j.engfailanal.2023.107581","article-title":"A review of valve health diagnosis and assessment: Insights for intelligence maintenance of natural gas pipeline valves in China","volume":"153","author":"Zhang","year":"2023","journal-title":"Eng. Fail. Anal."},{"key":"10.1016\/j.aei.2026.104978_b0325","doi-asserted-by":"crossref","DOI":"10.1016\/j.applthermaleng.2024.122535","article-title":"Thermal fatigue analysis of district heating pipeline under variable frequency regulation of circulating water pump","volume":"242","author":"Liu","year":"2024","journal-title":"Appl. Therm. Eng."},{"key":"10.1016\/j.aei.2026.104978_b0330","first-page":"54","article-title":"Flow induced vibrations of oil and gas piping systems: Wall pressure fluctuations and fatigue life assessment","author":"Bachoo","year":"2021","journal-title":"West Indian J. Eng."},{"key":"10.1016\/j.aei.2026.104978_b0335","doi-asserted-by":"crossref","unstructured":"A.S. Tijsseling, Q. Hou, B. Svingen, A. Bergant, Acoustic Resonance Experiments in a Reservoir-Pipeline-Orifice System, in: n.d. https:\/\/doi.org\/10.1115\/PVP2013-97534.","DOI":"10.1115\/PVP2013-97534"},{"key":"10.1016\/j.aei.2026.104978_b0340","doi-asserted-by":"crossref","unstructured":"H.G. Kunert, A.A. Marquez, P. Fazzini, J.L. Otegui, Chapter 5 - Failures and integrity of pipelines subjected to soil movements, in: A.S.H. Makhlouf, M. Aliofkhazraei (Eds.), Handb. Mater. Fail. Anal. Case Stud. Oil Gas Ind., Butterworth-Heinemann, 2016: pp. 105\u2013122. https:\/\/doi.org\/10.1016\/B978-0-08-100117-2.00020-0.","DOI":"10.1016\/B978-0-08-100117-2.00020-0"},{"key":"10.1016\/j.aei.2026.104978_b0345","doi-asserted-by":"crossref","first-page":"1304","DOI":"10.1016\/j.jrmge.2022.04.009","article-title":"Experimental study on uplift mechanism of pipeline buried in sand using high-resolution fiber optic strain sensing nerves","volume":"14","author":"Li","year":"2022","journal-title":"J. Rock Mech. Geotech. Eng."},{"key":"10.1016\/j.aei.2026.104978_b0350","doi-asserted-by":"crossref","unstructured":"Distributed Fiber Optic Strain Sensing Technology for Monitoring Soil Deformation Induced by Leakage in Buried Water Pipelines: A Model Test Study, (n.d.). https:\/\/www.mdpi.com\/1424-8220\/25\/2\/320 (accessed April 18, 2025).","DOI":"10.3390\/s25020320"},{"key":"10.1016\/j.aei.2026.104978_b0355","doi-asserted-by":"crossref","unstructured":"J. Bracic, R. McMahon, The Use of Remote Real-Time GNSS to Monitor a Pipeline in an Active Landslide, in: n.d. https:\/\/doi.org\/10.1115\/IPC2020-9751.","DOI":"10.1115\/IPC2020-9751"},{"key":"10.1016\/j.aei.2026.104978_b0360","doi-asserted-by":"crossref","DOI":"10.1016\/j.trgeo.2022.100786","article-title":"Mitigation strategies and measures for frost heave hazards of chilled gas pipeline in permafrost regions: a review","volume":"36","author":"Li","year":"2022","journal-title":"Transp. Geotech."},{"key":"10.1016\/j.aei.2026.104978_b0365","unstructured":"Arctic Pipeline Leak Detection using Fiber Optic Cable Distributed Sensing Systems | OTC Arctic Technology Conference | OnePetro, (n.d.). https:\/\/onepetro.org\/OTCARCTIC\/proceedings\/14OARC\/All-14OARC\/OTC-24589-MS\/172041 (accessed April 18, 2025)."},{"key":"10.1016\/j.aei.2026.104978_b0370","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2025.117390","article-title":"Optimized modal decomposition techniques for robust leakage detection in noisy environments: a comparative study","volume":"252","author":"Cui","year":"2025","journal-title":"Measurement"},{"key":"10.1016\/j.aei.2026.104978_b0375","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2024.104890","article-title":"Optimizing acoustic signal processing for localization of precise pipeline leakage using acoustic signal decomposition and wavelet analysis","volume":"157","author":"Ali","year":"2025","journal-title":"Digit. Signal Process."},{"key":"10.1016\/j.aei.2026.104978_b0380","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2025.117194","article-title":"Investigation into acoustic emission-based methodology for precise localization of pipeline leakage sources","volume":"250","author":"Cui","year":"2025","journal-title":"Measurement"},{"key":"10.1016\/j.aei.2026.104978_b0385","doi-asserted-by":"crossref","first-page":"41539","DOI":"10.1007\/s11042-023-15127-0","article-title":"Review of wavelet denoising algorithms","volume":"82","author":"Halidou","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"10.1016\/j.aei.2026.104978_b0390","doi-asserted-by":"crossref","DOI":"10.1016\/j.compstruc.2024.107497","article-title":"A wavelet-based denoising method for pipeline dent assessments","volume":"303","author":"Lin","year":"2024","journal-title":"Comput. Struct."},{"key":"10.1016\/j.aei.2026.104978_b0395","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1016\/j.psep.2020.11.053","article-title":"Gas leak detection in galvanised steel pipe with internal flow noise using convolutional neural network","volume":"146","author":"Song","year":"2021","journal-title":"Process Saf. Environ. Prot."},{"key":"10.1016\/j.aei.2026.104978_b0400","doi-asserted-by":"crossref","first-page":"9670","DOI":"10.3390\/app13179670","article-title":"A New Method for evaluating Natural Gas Pipelines based on ICEEMDAN-LMS: a View of Noise Reduction in Defective Pipelines","volume":"13","author":"Gao","year":"2023","journal-title":"Appl. Sci."},{"key":"10.1016\/j.aei.2026.104978_b0405","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2021.110368","article-title":"Pipeline leak and volume rate detections through Artificial intelligence and vibration analysis","volume":"187","author":"Yang","year":"2022","journal-title":"Measurement"},{"key":"10.1016\/j.aei.2026.104978_b0410","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2024.115937","article-title":"Real-time detection of urban gas pipeline leakage based on machine learning of IoT time-series data","volume":"242","author":"Yuan","year":"2025","journal-title":"Measurement"},{"key":"10.1016\/j.aei.2026.104978_b0415","unstructured":"Peng: Defect classification using PEC respones based... - Google Scholar, (n.d.). https:\/\/scholar.google.com\/scholar_lookup?title=Defect%20classification%20using%20PEC%20response%20based%20on%20power%20spectral%20density%20analysis%20combined%20with%20EMD%20and%20EEMD&publication_year=2016&author=Y.%20Peng&author=X.%20Qiu&author=J.%20Wei&author=C.%20Li&author=X.%20Cui (accessed April 7, 2025)."},{"key":"10.1016\/j.aei.2026.104978_b0420","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.ndteint.2018.04.008","article-title":"System identification-based frequency domain feature extraction for defect detection and characterization","volume":"98","author":"Li","year":"2018","journal-title":"NDT E Int."},{"key":"10.1016\/j.aei.2026.104978_b0425","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.measurement.2018.04.030","article-title":"Natural-gas pipeline leak location using variational mode decomposition analysis and cross-time\u2013frequency spectrum","volume":"124","author":"Xiao","year":"2018","journal-title":"Measurement"},{"key":"10.1016\/j.aei.2026.104978_b0430","doi-asserted-by":"crossref","unstructured":"M.S. Reza, J. Ma, ICA and PCA integrated feature extraction for classification, in: 2016 IEEE 13th Int. Conf. Signal Process. ICSP, IEEE, 2016: pp. 1083\u20131088. https:\/\/ieeexplore.ieee.org\/abstract\/document\/7877996\/ (accessed April 7, 2025).","DOI":"10.1109\/ICSP.2016.7877996"},{"key":"10.1016\/j.aei.2026.104978_b0435","unstructured":"K. O\u2019Shea, R. Nash, An Introduction to Convolutional Neural Networks, (2015). https:\/\/doi.org\/10.48550\/arXiv.1511.08458."},{"key":"10.1016\/j.aei.2026.104978_b0440","doi-asserted-by":"crossref","first-page":"826","DOI":"10.1177\/14759217211010270","article-title":"Gas pipeline event classification based on one-dimensional convolutional neural network","volume":"21","author":"An","year":"2022","journal-title":"Struct. Health Monit."},{"key":"10.1016\/j.aei.2026.104978_b0445","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2022.104890","article-title":"Evaluation of deep learning approaches for oil & gas pipeline leak detection using wireless sensor networks","volume":"113","author":"Spandonidis","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.aei.2026.104978_b0450","doi-asserted-by":"crossref","unstructured":"P. Feng, Y. Shen, W. Liu, Water leakage detection based on variation bayesian neural network autoencoder, in: J. Phys. Conf. Ser., IOP Publishing, 2021: p. 012110. https:\/\/iopscience.iop.org\/article\/10.1088\/1742-6596\/1948\/1\/012110\/meta (accessed April 7, 2025).","DOI":"10.1088\/1742-6596\/1948\/1\/012110"},{"key":"10.1016\/j.aei.2026.104978_b0455","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106062","article-title":"Leakage detection in water distribution networks via 1D CNN deep autoencoder for multivariate SCADA data","volume":"122","author":"Tornyeviadzi","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.aei.2026.104978_b0460","doi-asserted-by":"crossref","DOI":"10.1016\/j.tust.2024.105945","article-title":"Feature selection of acoustic signals for leak detection in water pipelines","volume":"152","author":"Xu","year":"2024","journal-title":"Tunn. Undergr. Space Technol."},{"key":"10.1016\/j.aei.2026.104978_b0465","doi-asserted-by":"crossref","first-page":"1857","DOI":"10.1109\/JSEN.2023.3337228","article-title":"Multisource multimodal feature fusion for small leak detection in gas pipelines","volume":"24","author":"Yan","year":"2023","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.aei.2026.104978_b0470","unstructured":"Battle of the Leakage Detection and Isolation Methods | Journal of Water Resources Planning and Management | Vol 148, No 12, (n.d.). https:\/\/ascelibrary.org\/doi\/10.1061\/%28ASCE%29WR.1943-5452.0001601 (accessed April 7, 2025)."},{"key":"10.1016\/j.aei.2026.104978_b0475","unstructured":"V. Asghari, vd1371\/MLLeakDetection, (2023). https:\/\/github.com\/vd1371\/MLLeakDetection (accessed April 7, 2025)."},{"key":"10.1016\/j.aei.2026.104978_b0480","unstructured":"KIOS-Research\/LeakDB, (2025). https:\/\/github.com\/KIOS-Research\/LeakDB (accessed April 7, 2025)."},{"key":"10.1016\/j.aei.2026.104978_b0485","doi-asserted-by":"crossref","DOI":"10.1016\/j.dib.2023.109148","article-title":"Benchmarking dataset for leak detection and localization in water distribution systems","volume":"48","author":"Aghashahi","year":"2023","journal-title":"Data Brief"},{"key":"10.1016\/j.aei.2026.104978_b0490","doi-asserted-by":"crossref","DOI":"10.1016\/j.dib.2022.108756","article-title":"Dataset for structural health monitoring of pipelines using ultrasonic guided waves","volume":"45","author":"El Mountassir","year":"2022","journal-title":"Data Brief"},{"key":"10.1016\/j.aei.2026.104978_b0495","unstructured":"UCI Machine Learning Repository, (n.d.). https:\/\/archive.ics.uci.edu\/ (accessed April 7, 2025)."},{"key":"10.1016\/j.aei.2026.104978_b0500","unstructured":"Zenodo, (n.d.). https:\/\/zenodo.org\/ (accessed April 7, 2025)."},{"key":"10.1016\/j.aei.2026.104978_b0505","doi-asserted-by":"crossref","unstructured":"IEEE DataPort, IEEE DataPort (n.d.). https:\/\/ieee-dataport.org\/ (accessed April 7, 2025).","DOI":"10.1109\/MSP.2025.3630250"},{"key":"10.1016\/j.aei.2026.104978_b0510","doi-asserted-by":"crossref","first-page":"7107","DOI":"10.1109\/TCYB.2020.3035518","article-title":"Insufficient data generative model for pipeline network leak detection using generative adversarial networks","volume":"52","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.aei.2026.104978_b0515","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109486","article-title":"Corrosion leakage risk diagnosis of oil and gas pipelines based on semi-supervised domain generalization model","volume":"238","author":"Miao","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104978_b0520","doi-asserted-by":"crossref","unstructured":"S. Suthaharan, Support Vector Machine, in: Mach. Learn. Models Algorithms Big Data Classif., Springer US, Boston, MA, 2016: pp. 207\u2013235. https:\/\/doi.org\/10.1007\/978-1-4899-7641-3_9.","DOI":"10.1007\/978-1-4899-7641-3_9"},{"key":"10.1016\/j.aei.2026.104978_b0525","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.neucom.2019.10.118","article-title":"A comprehensive survey on support vector machine classification: applications, challenges and trends","volume":"408","author":"Cervantes","year":"2020","journal-title":"Neurocomputing"},{"key":"10.1016\/j.aei.2026.104978_b0530","doi-asserted-by":"crossref","first-page":"3887","DOI":"10.3390\/math12243887","article-title":"Real-World Steam Powerplant Boiler Tube Leakage Detection using Hybrid Deep Learning","volume":"12","author":"Khalid","year":"2024","journal-title":"Mathematics"},{"key":"10.1016\/j.aei.2026.104978_b0535","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1016\/j.acme.2016.11.005","article-title":"Detection of fatigue cracking in steel bridge girders: a support vector machine approach","volume":"17","author":"Hasni","year":"2017","journal-title":"Arch. Civ. Mech. Eng."},{"key":"10.1016\/j.aei.2026.104978_b0540","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1016\/j.prostr.2019.05.089","article-title":"Application of wavelet analysis and machine learning on vibration data from gas pipelines for structural health monitoring","volume":"14","author":"Zajam","year":"2019","journal-title":"Procedia Struct. Integr."},{"key":"10.1016\/j.aei.2026.104978_b0545","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2020.101205","article-title":"Bottom-up image detection of water channel slope damages based on superpixel segmentation and support vector machine","volume":"47","author":"Chen","year":"2021","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.aei.2026.104978_b0550","doi-asserted-by":"crossref","first-page":"e2290","DOI":"10.1002\/stc.2290","article-title":"Pipeline leakage identification and localization based on the fiber Bragg grating hoop strain measurements and particle swarm optimization and support vector machine","volume":"26","author":"Jia","year":"2019","journal-title":"Struct. Control Health Monit."},{"key":"10.1016\/j.aei.2026.104978_b0555","doi-asserted-by":"crossref","DOI":"10.1016\/j.jlp.2019.103926","article-title":"Multipoint hoop strain measurement based pipeline leakage localization with an optimized support vector regression approach","volume":"62","author":"Jia","year":"2019","journal-title":"J. Loss Prev. Process Ind."},{"key":"10.1016\/j.aei.2026.104978_b0560","doi-asserted-by":"crossref","unstructured":"A. Parmar, R. Katariya, V. Patel, A Review on Random Forest: An Ensemble Classifier, in: J. Hemanth, X. Fernando, P. Lafata, Z. Baig (Eds.), Int. Conf. Intell. Data Commun. Technol. Internet Things ICICI 2018, Springer International Publishing, Cham, 2019: pp. 758\u2013763. https:\/\/doi.org\/10.1007\/978-3-030-03146-6_86.","DOI":"10.1007\/978-3-030-03146-6_86"},{"key":"10.1016\/j.aei.2026.104978_b0565","doi-asserted-by":"crossref","first-page":"1906","DOI":"10.1177\/14759217211036880","article-title":"Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights","volume":"21","author":"Malekloo","year":"2022","journal-title":"Struct. Health Monit."},{"key":"10.1016\/j.aei.2026.104978_b0570","first-page":"90","author":"Tavakoli","year":"2020","journal-title":"Prediction of Pipe Failures in Wastewater Networks Using Random Forest Classification"},{"key":"10.1016\/j.aei.2026.104978_b0575","doi-asserted-by":"crossref","DOI":"10.1016\/j.apacoust.2021.108255","article-title":"A framework combining acoustic features extraction method and random forest algorithm for gas pipeline leak detection and classification","volume":"182","author":"Ning","year":"2021","journal-title":"Appl. Acoust."},{"key":"10.1016\/j.aei.2026.104978_b0580","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijpvp.2022.104821","article-title":"Status diagnosis and feature tracing of the natural gas pipeline weld based on improved random forest model","volume":"200","author":"Wang","year":"2022","journal-title":"Int. J. Press. Vessels Pip."},{"key":"10.1016\/j.aei.2026.104978_b0585","doi-asserted-by":"crossref","first-page":"9087","DOI":"10.3390\/s23229087","article-title":"Leak State Detection and size Identification for Fluid Pipelines with a Novel Acoustic Emission Intensity Index and Random Forest","volume":"23","author":"Nguyen","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.aei.2026.104978_b0590","doi-asserted-by":"crossref","first-page":"155113","DOI":"10.1109\/ACCESS.2021.3129703","article-title":"Detailed Leak Localization in Water distribution Networks using Random Forest Classifier and Pipe Segmentation","volume":"9","author":"Lu\u010din","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.aei.2026.104978_b0595","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109062","article-title":"Acoustic localization approach for urban water distribution networks using machine learning method","volume":"137","author":"Zhang","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.aei.2026.104978_b0600","doi-asserted-by":"crossref","unstructured":"Z. Jia, L. Ren, H. Li, T. Jiang, W. Wu, Pipeline leakage identification and localization based on the fiber Bragg grating hoop strain measurements and particle swarm optimization and support vector machine, (n.d.). https:\/\/doi.org\/10.1002\/stc.2290.","DOI":"10.1002\/stc.2290"},{"key":"10.1016\/j.aei.2026.104978_b0605","doi-asserted-by":"crossref","unstructured":"O. Kramer, K-Nearest Neighbors, in: Dimens. Reduct. Unsupervised Nearest Neighbors, Springer Berlin Heidelberg, Berlin, Heidelberg, 2013: pp. 13\u201323. https:\/\/doi.org\/10.1007\/978-3-642-38652-7_2.","DOI":"10.1007\/978-3-642-38652-7_2"},{"key":"10.1016\/j.aei.2026.104978_b0610","first-page":"627","article-title":"Machine learning and acoustic method applied to leak detection and location in low-pressure gas pipelines, Clean Technol. Environ","volume":"22","author":"da Cruz","year":"2020","journal-title":"Policy"},{"key":"10.1016\/j.aei.2026.104978_b0615","doi-asserted-by":"crossref","unstructured":"Y. Hamed, A. Shafie, Z.B. Mustaffa, N.R.B. Idris, An application of K-Nearest Neighbor interpolation on calibrating corrosion measurements collected by two non-destructive techniques, in: 2015 IEEE 3rd Int. Conf. Smart Instrum. Meas. Appl. ICSIMA, 2015: pp. 1\u20135. https:\/\/doi.org\/10.1109\/ICSIMA.2015.7559030.","DOI":"10.1109\/ICSIMA.2015.7559030"},{"key":"10.1016\/j.aei.2026.104978_b0620","doi-asserted-by":"crossref","first-page":"1742","DOI":"10.1177\/14759217231191080","article-title":"Source location and anomaly detection for damage identification of buried pipelines using kurtosis-based transfer function","volume":"23","author":"Lee","year":"2024","journal-title":"Struct. Health Monit."},{"key":"10.1016\/j.aei.2026.104978_b0625","doi-asserted-by":"crossref","unstructured":"D. Berrar, Bayes\u2019 theorem and naive Bayes classifier, (2025). https:\/\/oro.open.ac.uk\/96521\/ (accessed April 17, 2025).","DOI":"10.1016\/B978-0-323-95502-7.00118-4"},{"key":"10.1016\/j.aei.2026.104978_b0630","doi-asserted-by":"crossref","first-page":"7156","DOI":"10.1016\/j.matpr.2022.03.035","article-title":"Data mining model and Gaussian Naive Bayes based fault diagnostic analysis of modern power system networks","volume":"62","author":"Venkata","year":"2022","journal-title":"Mater. Today Proc."},{"key":"10.1016\/j.aei.2026.104978_b0635","doi-asserted-by":"crossref","first-page":"320","DOI":"10.25007\/ajnu.v12n2a1612","article-title":"eXtreme gradient boosting algorithm with machine learning: a review","volume":"12","author":"Ali","year":"2023","journal-title":"Acad. J. Nawroz Univ."},{"key":"10.1016\/j.aei.2026.104978_b0640","doi-asserted-by":"crossref","first-page":"15889","DOI":"10.1038\/s41598-022-20149-z","article-title":"Gradient boosting decision tree becomes more reliable than logistic regression in predicting probability for diabetes with big data","volume":"12","author":"Seto","year":"2022","journal-title":"Sci. Rep."},{"key":"10.1016\/j.aei.2026.104978_b0645","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1007\/s11831-020-09422-4","article-title":"A Systematic Review of Hidden Markov Models and their applications","volume":"28","author":"Mor","year":"2021","journal-title":"Arch. Comput. Methods Eng."},{"key":"10.1016\/j.aei.2026.104978_b0650","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.3390\/modelling5040069","article-title":"Novel Adaptive Hidden Markov Model Utilizing Expectation\u2013Maximization Algorithm for Advanced Pipeline Leak Detection","volume":"5","author":"Zadehbagheri","year":"2024","journal-title":"Modelling"},{"key":"10.1016\/j.aei.2026.104978_b0655","doi-asserted-by":"crossref","first-page":"20","DOI":"10.38094\/jastt20165","article-title":"Classification based on decision tree algorithm for machine learning","volume":"2","author":"Charbuty","year":"2021","journal-title":"J. Appl. Sci. Technol. Trends"},{"key":"10.1016\/j.aei.2026.104978_b0660","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.aei.2026.104978_b0665","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1007\/s11831-024-10146-y","article-title":"Intelligent Computational Methods for damage Detection of Laminated Composite Structures for Mobility applications: a Comprehensive Review","volume":"32","author":"Azad","year":"2025","journal-title":"Arch. Comput. Methods Eng."},{"key":"10.1016\/j.aei.2026.104978_b0670","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.comcom.2022.08.001","article-title":"Defect identification for oil and gas pipeline safety based on autonomous deep learning network","volume":"195","author":"Zhang","year":"2022","journal-title":"Comput. Commun."},{"key":"10.1016\/j.aei.2026.104978_b0675","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2019.102849","article-title":"Underground sewer pipe condition assessment based on convolutional neural networks","volume":"106","author":"Hassan","year":"2019","journal-title":"Autom. Constr."},{"key":"10.1016\/j.aei.2026.104978_b0680","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1177\/14759217221080198","article-title":"A convolutional neural network for pipe crack and leak detection in smart water network","volume":"22","author":"Zhang","year":"2023","journal-title":"Struct. Health Monit."},{"key":"10.1016\/j.aei.2026.104978_b0685","doi-asserted-by":"crossref","first-page":"855","DOI":"10.3390\/s23020855","article-title":"A Novel Pipeline Corrosion monitoring Method based on Piezoelectric active Sensing and CNN","volume":"23","author":"Yang","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.aei.2026.104978_b0690","doi-asserted-by":"crossref","unstructured":"J. Li, Y. Liu, Y. Chai, H. He, M. Gao, A Small Leakage Detection Approach for Gas Pipelines based on CNN, in: 2019 CAA Symp. Fault Detect. Superv. Saf. Tech. Process. SAFEPROCESS, 2019: pp. 390\u2013394. https:\/\/doi.org\/10.1109\/SAFEPROCESS45799.2019.9213371.","DOI":"10.1109\/SAFEPROCESS45799.2019.9213371"},{"key":"10.1016\/j.aei.2026.104978_b0695","doi-asserted-by":"crossref","first-page":"2845","DOI":"10.3390\/app13052845","article-title":"Application of CNN Models to Detect and Classify Leakages in Water Pipelines using Magnitude Spectra of Vibration Sound","volume":"13","author":"Choi","year":"2023","journal-title":"Appl. Sci."},{"key":"10.1016\/j.aei.2026.104978_b0700","doi-asserted-by":"crossref","first-page":"8034","DOI":"10.3390\/app12168034","article-title":"Using Convolutional Neural Networks in the Development of a Water Pipe Leakage and Location Identification System","volume":"12","author":"Tsai","year":"2022","journal-title":"Appl. Sci."},{"key":"10.1016\/j.aei.2026.104978_b0705","doi-asserted-by":"crossref","first-page":"47565","DOI":"10.1109\/ACCESS.2021.3068292","article-title":"A Pipeline Leak Detection and Localization Approach based on Ensemble TL1DCNN","volume":"9","author":"Zhou","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.aei.2026.104978_b0710","doi-asserted-by":"crossref","DOI":"10.1016\/j.iot.2023.100995","article-title":"An efficient system for water leak detection and localization based on IoT and lightweight deep learning","volume":"24","author":"Boujelben","year":"2023","journal-title":"Internet Things"},{"key":"10.1016\/j.aei.2026.104978_b0715","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.jare.2021.03.015","article-title":"A review on modern defect detection models using DCNNs \u2013 deep convolutional neural networks","volume":"35","author":"Tulbure","year":"2022","journal-title":"J. Adv. Res."},{"key":"10.1016\/j.aei.2026.104978_b0720","doi-asserted-by":"crossref","first-page":"3233","DOI":"10.3390\/pr11113233","article-title":"Improving accuracy and interpretability of CNN-based fault diagnosis through an attention mechanism","volume":"11","author":"Huang","year":"2023","journal-title":"Processes"},{"key":"10.1016\/j.aei.2026.104978_b0725","series-title":"Methods Genomic Predict","first-page":"379","article-title":"Fundamentals of Artificial Neural Networks and Deep Learning","author":"Montesinos L\u00f3pez","year":"2022"},{"key":"10.1016\/j.aei.2026.104978_b0730","doi-asserted-by":"crossref","first-page":"7640","DOI":"10.1016\/j.egyr.2021.10.093","article-title":"Failure classification in natural gas pipe-lines using artificial intelligence: a case study","volume":"7","author":"Manan","year":"2021","journal-title":"Energy Rep."},{"key":"10.1016\/j.aei.2026.104978_b0735","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2022.109051","article-title":"Leakage diagnosis and localization of the gas extraction pipeline based on SA-PSO BP neural network","volume":"232","author":"Zhou","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104978_b0740","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.autcon.2014.05.003","article-title":"Artificial neural network models for predicting condition of offshore oil and gas pipelines","volume":"45","author":"El-Abbasy","year":"2014","journal-title":"Autom. Constr."},{"key":"10.1016\/j.aei.2026.104978_b0745","doi-asserted-by":"crossref","DOI":"10.1016\/j.dwt.2024.100685","article-title":"Leak detection and localization in water distribution systems using advanced feature analysis and an Artificial Neural Network","volume":"320","author":"Mahdi","year":"2024","journal-title":"Desalination Water Treat."},{"key":"10.1016\/j.aei.2026.104978_b0750","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s41324-022-00494-x","article-title":"The role of artificial neural network and machine learning in utilizing spatial information","volume":"31","author":"Goel","year":"2023","journal-title":"Spat. Inf. Res."},{"key":"10.1016\/j.aei.2026.104978_b0755","doi-asserted-by":"crossref","unstructured":"R. Miikkulainen, J. Liang, E. Meyerson, A. Rawal, D. Fink, O. Francon, B. Raju, H. Shahrzad, A. Navruzyan, N. Duffy, Evolving deep neural networks, in: Artif. Intell. Age Neural Netw. Brain Comput., Elsevier, 2024: pp. 269\u2013287. https:\/\/www.sciencedirect.com\/science\/article\/pii\/B9780323961042000026 (accessed April 18, 2025).","DOI":"10.1016\/B978-0-323-96104-2.00002-6"},{"key":"10.1016\/j.aei.2026.104978_b0760","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1016\/j.jlp.2016.06.018","article-title":"Pipeline leakage detection and isolation: an integrated approach of statistical and wavelet feature extraction with multi-layer perceptron neural network (MLPNN)","volume":"43","author":"Zadkarami","year":"2016","journal-title":"J. Loss Prev. Process Ind."},{"key":"10.1016\/j.aei.2026.104978_b0765","doi-asserted-by":"crossref","first-page":"1777","DOI":"10.3390\/math11081777","article-title":"Auto-encoders in deep learning\u2014a review with new perspectives","volume":"11","author":"Chen","year":"2023","journal-title":"Mathematics"},{"key":"10.1016\/j.aei.2026.104978_b0770","doi-asserted-by":"crossref","unstructured":"W.H. Lopez Pinaya, S. Vieira, R. Garcia-Dias, A. Mechelli, Chapter 11 - Autoencoders, in: A. Mechelli, S. Vieira (Eds.), Mach. Learn., Academic Press, 2020: pp. 193\u2013208. https:\/\/doi.org\/10.1016\/B978-0-12-815739-8.00011-0.","DOI":"10.1016\/B978-0-12-815739-8.00011-0"},{"key":"10.1016\/j.aei.2026.104978_b0775","doi-asserted-by":"crossref","first-page":"4665","DOI":"10.3390\/electronics12224665","article-title":"Simultaneous Pipe Leak Detection and Localization using Attention-based Deep Learning Autoencoder","volume":"12","author":"Karimanzira","year":"2023","journal-title":"Electronics"},{"key":"10.1016\/j.aei.2026.104978_b0780","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1016\/j.psep.2024.05.112","article-title":"Leak detection for natural gas gathering pipelines under corrupted data via assembling twin robust autoencoders","volume":"188","author":"Zhang","year":"2024","journal-title":"Process Saf. Environ. Prot."},{"key":"10.1016\/j.aei.2026.104978_b0785","doi-asserted-by":"crossref","DOI":"10.1016\/j.physd.2019.132306","article-title":"Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network","volume":"404","author":"Sherstinsky","year":"2020","journal-title":"Phys. Nonlinear Phenom."},{"key":"10.1016\/j.aei.2026.104978_b0790","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1093\/jigpal\/jzp042","article-title":"Perspectives and challenges for recurrent neural network training","volume":"18","author":"Gori","year":"2010","journal-title":"Log. J. IGPL"},{"key":"10.1016\/j.aei.2026.104978_b0795","doi-asserted-by":"crossref","unstructured":"R. DiPietro, G.D. Hager, Deep learning: RNNs and LSTM, in: Handb. Med. Image Comput. Comput. Assist. Interv., Elsevier, 2020: pp. 503\u2013519. https:\/\/www.sciencedirect.com\/science\/article\/pii\/B9780128161760000260 (accessed April 18, 2025).","DOI":"10.1016\/B978-0-12-816176-0.00026-0"},{"key":"10.1016\/j.aei.2026.104978_b0800","doi-asserted-by":"crossref","first-page":"9262","DOI":"10.3390\/su13169262","article-title":"Development of Leakage Detection Model and its Application for Water distribution Networks using RNN-LSTM","volume":"13","author":"Lee","year":"2021","journal-title":"Sustainability"},{"key":"10.1016\/j.aei.2026.104978_b0805","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijdrr.2024.104771","article-title":"Dynamic risk assessment of natural gas transmission pipelines with LSTM networks and historical failure data","volume":"112","author":"Xiao","year":"2024","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"10.1016\/j.aei.2026.104978_b0810","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1016\/j.psep.2023.04.020","article-title":"Real-time pipeline leak detection and localization using an attention-based LSTM approach","volume":"174","author":"Zhang","year":"2023","journal-title":"Process Saf. Environ. Prot."},{"key":"10.1016\/j.aei.2026.104978_b0815","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1016\/j.istruc.2021.10.088","article-title":"Vibration-based multiclass damage detection and localization using long short-term memory networks","volume":"35","author":"Sony","year":"2022","journal-title":"Structures"},{"key":"10.1016\/j.aei.2026.104978_b0820","article-title":"From applications to modeling techniques and beyond\u2014Systematic review, J. King Saud Univ.-Comput","author":"Al-Selwi","year":"2024","journal-title":"Inf. Sci."},{"key":"10.1016\/j.aei.2026.104978_b0825","unstructured":"T. Guo, T. Lin, Exploring the interpretability of LSTM neural networks over multi-variable data, (2018). https:\/\/openreview.net\/forum?id=HklVMnR5tQ (accessed April 18, 2025)."},{"key":"10.1016\/j.aei.2026.104978_b0830","doi-asserted-by":"crossref","first-page":"26102","DOI":"10.1109\/ACCESS.2019.2900371","article-title":"An ensemble model based on adaptive noise reducer and over-fitting prevention LSTM for multivariate time series forecasting","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.aei.2026.104978_b0835","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1177\/14759217221103016","article-title":"Unsupervised deep learning method for bridge condition assessment based on intra-and inter-class probabilistic correlations of quasi-static responses","volume":"22","author":"Xu","year":"2023","journal-title":"Struct. Health Monit."},{"key":"10.1016\/j.aei.2026.104978_b0840","doi-asserted-by":"crossref","first-page":"e2667","DOI":"10.1002\/stc.2667","article-title":"Relationship modeling between vehicle-induced girder vertical deflection and cable tension by BiLSTM using field monitoring data of a cable-stayed bridge","volume":"28","author":"Tian","year":"2021","journal-title":"Struct. Control Health Monit."},{"key":"10.1016\/j.aei.2026.104978_b0845","doi-asserted-by":"crossref","unstructured":"G.-P. Kousiopoulos, G.-N. Papastavrou, N. Karagiorgos, S. Nikolaidis, D. Porlidas, Pipeline leak detection in noisy environment, in: 2019 8th Int. Conf. Mod. Circuits Syst. Technol. MOCAST, IEEE, 2019: pp. 1\u20135. https:\/\/ieeexplore.ieee.org\/abstract\/document\/8741673\/ (accessed April 18, 2025).","DOI":"10.1109\/MOCAST.2019.8741673"},{"key":"10.1016\/j.aei.2026.104978_b0850","doi-asserted-by":"crossref","first-page":"1822","DOI":"10.28991\/cej-2020-03091585","article-title":"Structural behavior of pipelines buried in expansive soils under rainfall infiltration (Part I: transverse behavior)","volume":"6","author":"Bouatia","year":"2020","journal-title":"Civ. Eng. J."},{"key":"10.1016\/j.aei.2026.104978_b0855","doi-asserted-by":"crossref","unstructured":"S. Siddique, M.A. Haque, R.H. Rifat, L.R. Das, S. Talukder, S.B. Alam, K.D. Gupta, Challenges and opportunities of computational intelligence in industrial control system (ics), in: 2023 IEEE Symp. Ser. Comput. Intell. SSCI, IEEE, 2023: pp. 1158\u20131163. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10371954\/ (accessed April 18, 2025).","DOI":"10.1109\/SSCI52147.2023.10371954"},{"key":"10.1016\/j.aei.2026.104978_b0860","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.3390\/modelling5030059","article-title":"A Novel Hybrid Internal Pipeline Leak Detection and Location System based on Modified Real-Time Transient Modelling","volume":"5","author":"Tajalli","year":"2024","journal-title":"Modelling"},{"key":"10.1016\/j.aei.2026.104978_b0865","doi-asserted-by":"crossref","DOI":"10.1016\/j.ultras.2023.106931","article-title":"Unsupervised learning framework for temperature compensated damage identification and localization in ultrasonic guided wave SHM with transfer learning","volume":"130","author":"Sawant","year":"2023","journal-title":"Ultrasonics"},{"key":"10.1016\/j.aei.2026.104978_b0870","doi-asserted-by":"crossref","unstructured":"M. Ameli, V. Pfanschilling, A. Amirli, W. Maa\u00df, K. Kersting, Unsupervised Multi-sensor Anomaly Localization with Explainable AI, in: I. Maglogiannis, L. Iliadis, J. Macintyre, P. Cortez (Eds.), Artif. Intell. Appl. Innov., Springer International Publishing, Cham, 2022: pp. 507\u2013519. https:\/\/doi.org\/10.1007\/978-3-031-08333-4_41.","DOI":"10.1007\/978-3-031-08333-4_41"},{"key":"10.1016\/j.aei.2026.104978_b0875","doi-asserted-by":"crossref","DOI":"10.1016\/j.compgeo.2025.107389","article-title":"Application of physics-informed neural networks for nonlinear analysis of buried steel pipelines to support pipe reinforcement against ground movement","volume":"186","author":"Taraghi","year":"2025","journal-title":"Comput. Geotech."},{"key":"10.1016\/j.aei.2026.104978_b0880","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2024.132839","article-title":"Leak localization in District heating Networks integrating physical model-based and data driven-based methods: Impact of dataset construction on model performance","volume":"308","author":"Yang","year":"2024","journal-title":"Energy"},{"key":"10.1016\/j.aei.2026.104978_b0885","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.120021","article-title":"Spectral transient-based multiple leakage identification in water pipelines: an efficient hybrid gradient-metaheuristic optimization","volume":"224","author":"Keramat","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.aei.2026.104978_b0890","doi-asserted-by":"crossref","first-page":"7622","DOI":"10.1007\/s10489-021-02771-y","article-title":"A transfer-learning approach for corrosion prediction in pipeline infrastructures","volume":"52","author":"Canonaco","year":"2022","journal-title":"Appl. Intell."},{"key":"10.1016\/j.aei.2026.104978_b0895","first-page":"1","article-title":"Estimation of defect size and Cross-Sectional Profile for the Oil and Gas Pipeline using Visual deep transfer Learning Neural Network","volume":"72","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.aei.2026.104978_b0900","first-page":"1","article-title":"A Novel Deep Offline-to-Online transfer Learning Framework for Pipeline Leakage Detection with Small Samples","volume":"72","author":"Wang","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.aei.2026.104978_b0905","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2023.120012","article-title":"Leak detection and localization in water distribution networks using conditional deep convolutional generative adversarial networks","volume":"238","author":"Rajabi","year":"2023","journal-title":"Water Res."},{"key":"10.1016\/j.aei.2026.104978_b0910","doi-asserted-by":"crossref","DOI":"10.1016\/j.flowmeasinst.2024.102745","article-title":"Pipeline leak detection based on generative adversarial networks under small samples","volume":"101","author":"Wang","year":"2025","journal-title":"Flow Meas. Instrum."},{"key":"10.1016\/j.aei.2026.104978_b0915","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1109\/JLT.2017.2780126","article-title":"Real field deployment of a smart fiber-optic surveillance system for pipeline integrity threat detection: Architectural issues and blind field test results","volume":"36","author":"Tejedor","year":"2018","journal-title":"J. Light. Technol."},{"key":"10.1016\/j.aei.2026.104978_b0920","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2023.113004","article-title":"Comprehensive approach toward IIoT based condition monitoring of machining processes","author":"Ali Laghari","year":"2023","journal-title":"Measurement 217"},{"key":"10.1016\/j.aei.2026.104978_b0925","doi-asserted-by":"crossref","first-page":"6414","DOI":"10.1109\/JSEN.2024.3521453","article-title":"Prediction of Fatigue Crack Propagation in X80 Pipeline Steel using Acoustic Emission Sensing","volume":"25","author":"Yan","year":"2025","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.aei.2026.104978_b0930","year":"2025","journal-title":"Performance Evaluation, and Applications | Journal of Molecular and Engineering Materials, (n.d.)"},{"key":"10.1016\/j.aei.2026.104978_b0935","article-title":"Interpretable machine learning in damage detection using shapley additive explanations, ASCE-ASME J. Risk Uncertain. Eng. Syst. Part B","volume":"8","author":"Movsessian","year":"2022","journal-title":"Mech. Eng."},{"key":"10.1016\/j.aei.2026.104978_b0940","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2022.111037","article-title":"A new sensor fault diagnosis method for gas leakage monitoring based on the naive Bayes and probabilistic neural network classifier","volume":"194","author":"Tan","year":"2022","journal-title":"Measurement"},{"key":"10.1016\/j.aei.2026.104978_b0945","doi-asserted-by":"crossref","first-page":"17","DOI":"10.3390\/math13010017","article-title":"Advancements in Physics-Informed Neural Networks for Laminated Composites: a Comprehensive Review","volume":"13","author":"Khalid","year":"2024","journal-title":"Mathematics"},{"key":"10.1016\/j.aei.2026.104978_b0950","doi-asserted-by":"crossref","DOI":"10.1016\/j.infsof.2020.106368","article-title":"Large-scale machine learning systems in real-world industrial settings: a review of challenges and solutions","volume":"127","author":"Lwakatare","year":"2020","journal-title":"Inf. Softw. Technol."},{"key":"10.1016\/j.aei.2026.104978_b0955","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.iotcps.2023.02.004","article-title":"Edge AI: a survey","volume":"3","author":"Singh","year":"2023","journal-title":"Internet Things Cyber-Phys. Syst."},{"key":"10.1016\/j.aei.2026.104978_b0960","unstructured":"S.G. Vrachimis, M.S. Kyriakou, KIOS-Research\/BattLeDIM: BattLeDIM evaluation code and dataset generator, (2022). https:\/\/doi.org\/10.5281\/zenodo.6962143."},{"key":"10.1016\/j.aei.2026.104978_b0965","first-page":"152","article-title":"Framework for Smart SCADA Systems: Integrating Cloud Computing, IIoT, and Cybersecurity for Enhanced Industrial Automation, saudi","volume":"10","author":"Enam","year":"2025","journal-title":"J. Eng. Technol."},{"key":"10.1016\/j.aei.2026.104978_b0970","article-title":"Bridging the Transparency Gap: what can Explainable AI Learn from the AI Act?","author":"Gyevnar","year":"2023","journal-title":"In"},{"key":"10.1016\/j.aei.2026.104978_b0975","unstructured":"dns, PODS - Pipeline Open Data Standard Association, PODS - Pipeline Open Data Stand. Assoc. (n.d.). https:\/\/pods.org\/ (accessed April 7, 2025)."},{"key":"10.1016\/j.aei.2026.104978_b0980","unstructured":"Transformable Inspection Robot Design and Implementation for Complex Pipeline Environment | IEEE Journals & Magazine | IEEE Xplore, (n.d.). https:\/\/ieeexplore.ieee.org\/abstract\/document\/10508069 (accessed April 18, 2025)."},{"key":"10.1016\/j.aei.2026.104978_b0985","first-page":"4631","article-title":"Crack Detection in Civil Infrastructure using Autonomous Robotic Systems: a Synergistic Review of Platforms","volume":"25","author":"Dai","year":"2025","journal-title":"Cognition, and Autonomous Action, Sensors"},{"key":"10.1016\/j.aei.2026.104978_b0990","unstructured":"Selection of the Maximum Sampling Speed - The Technical Specification of Acoustic Emission (AE) System, (n.d.). https:\/\/www.aendt.com\/blog\/maximum-sampling-speed.html (accessed April 7, 2025)."},{"key":"10.1016\/j.aei.2026.104978_b0995","doi-asserted-by":"crossref","first-page":"3004","DOI":"10.1080\/10589759.2024.2390996","article-title":"Acoustic emission wave propagation in pipeline sections and analysis of the effect of coating and sensor location","volume":"40","author":"Rajendran","year":"2025","journal-title":"Nondestruct. Test. Eval."},{"key":"10.1016\/j.aei.2026.104978_b1000","doi-asserted-by":"crossref","first-page":"6990","DOI":"10.3390\/ma14226990","article-title":"Reconstruction of Lamb Wave Dispersion Curves in Different Objects using Signals measured at two Different Distances","volume":"14","author":"Draudvilien\u0117","year":"2021","journal-title":"Materials"},{"key":"10.1016\/j.aei.2026.104978_b1005","doi-asserted-by":"crossref","first-page":"6872","DOI":"10.3390\/s24216872","article-title":"Enhanced Fatigue Crack Detection in complex Structure with Large Cutout using Nonlinear Lamb Wave","volume":"24","author":"Zhang","year":"2024","journal-title":"Sensors"},{"key":"10.1016\/j.aei.2026.104978_b1010","unstructured":"admin, Types of Ultrasound Transducer Probes for NDT | Practical Guide, NDT-KITS (2025). https:\/\/ndt-kits.com\/types-of-ultrasound-transducer-probes-for-industrial-ndt\/ (accessed September 9, 2025)."},{"key":"10.1016\/j.aei.2026.104978_b1015","doi-asserted-by":"crossref","DOI":"10.1016\/j.compstruct.2020.112951","article-title":"Advances, limitations and prospects of nondestructive testing and evaluation of thick composites and sandwich structures: a state-of-the-art review","volume":"256","author":"Nsengiyumva","year":"2021","journal-title":"Compos. Struct."},{"key":"10.1016\/j.aei.2026.104978_b1020","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1016\/j.ultras.2013.03.007","article-title":"Ultrasonic monitoring of erosion\/corrosion thinning rates in industrial piping systems","volume":"53","author":"Honarvar","year":"2013","journal-title":"Ultrasonics"},{"key":"10.1016\/j.aei.2026.104978_b1025","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1007\/s10921-024-01096-3","article-title":"Phased Array Ultrasonic Testing on Thick Glass Fiber Reinforced Thermoplastic Composite Pipe Implementing the Classical Time-Corrected Gain Method","volume":"43","author":"Mohd Tahir","year":"2024","journal-title":"J. Nondestruct. Eval."},{"key":"10.1016\/j.aei.2026.104978_b1030","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1080\/00221686.2016.1168324","article-title":"Pressure surges caused by air release in water pipelines","volume":"54","author":"Fontana","year":"2016","journal-title":"J. Hydraul. Res."},{"key":"10.1016\/j.aei.2026.104978_b1035","unstructured":"T. Traudt, C. Bombardieri, E. Schleicher, C. Manfletti, INVESTIGATION OF PRESSURE HAMMER WITH WIRE MESH SENSOR AND HIGH SPEED IMAGING TECHNIQUES, (n.d.)."},{"key":"10.1016\/j.aei.2026.104978_b1040","doi-asserted-by":"crossref","DOI":"10.1061\/(ASCE)PS.1949-1204.0000574","article-title":"Review of Water Leak Detection and Localization Methods through Hydrophone Technology","volume":"12","author":"Bakhtawar","year":"2021","journal-title":"J. Pipeline Syst. Eng. Pract."},{"key":"10.1016\/j.aei.2026.104978_b1045","unstructured":"Review of Water Leak Detection and Localization Methods through Hydrophone Technology | Journal of Pipeline Systems Engineering and Practice | Vol 12, No 4, (n.d.). https:\/\/ascelibrary.org\/doi\/10.1061\/%28ASCE%29PS.1949-1204.0000574 (accessed September 9, 2025)."},{"key":"10.1016\/j.aei.2026.104978_b1050","doi-asserted-by":"crossref","first-page":"540","DOI":"10.3390\/app7060540","article-title":"Application of FBG based Sensor in Pipeline Safety monitoring","volume":"7","author":"Jiang","year":"2017","journal-title":"Appl. Sci."},{"key":"10.1016\/j.aei.2026.104978_b1055","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2023.110346","article-title":"An AI-based monitoring system for external disturbance detection and classification near a buried pipeline","volume":"196","author":"Chen","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.aei.2026.104978_b1060","unstructured":"Thermophysical Properties of Fluid Systems, (n.d.). https:\/\/webbook.nist.gov\/chemistry\/fluid\/ (accessed April 7, 2025)."},{"key":"10.1016\/j.aei.2026.104978_b1065","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ijleo.2018.09.048","article-title":"Pipeline leakage localization based on distributed FBG hoop strain measurements and support vector machine","volume":"176","author":"Jia","year":"2019","journal-title":"Optik"},{"key":"10.1016\/j.aei.2026.104978_b1070","doi-asserted-by":"crossref","first-page":"8659","DOI":"10.1109\/ACCESS.2017.2703122","article-title":"Leak Detection and Location of Pipelines based on LMD and Least Squares Twin support Vector Machine","volume":"5","author":"Lang","year":"2017","journal-title":"IEEE Access"},{"key":"10.1016\/j.aei.2026.104978_b1075","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2021.102819","article-title":"Multi-source information fusion to identify water supply pipe leakage based on SVM and VMD","volume":"59","author":"Wang","year":"2022","journal-title":"Inf. Process. Manag."},{"key":"10.1016\/j.aei.2026.104978_b1080","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110372","article-title":"A real-time early warning classification method for natural gas leakage based on random forest","volume":"251","author":"Tan","year":"2024","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104978_b1085","doi-asserted-by":"crossref","first-page":"849","DOI":"10.3390\/buildings13040849","article-title":"Application of Machine Learning for Leak Localization in Water Supply Networks","volume":"13","author":"Yussif","year":"2023","journal-title":"Buildings"},{"key":"10.1016\/j.aei.2026.104978_b1090","doi-asserted-by":"crossref","DOI":"10.1063\/5.0110105","article-title":"Automated system for concrete damage classification identification using Na\u00efve-Bayesian classifier","volume":"2532","author":"Malik","year":"2022","journal-title":"AIP Conf. Proc."},{"key":"10.1016\/j.aei.2026.104978_b1095","doi-asserted-by":"crossref","unstructured":"G. Mazaev, M. Weyns, F. Vancoillie, G. Vaes, F. Ongenae, S. Van Hoecke, Leak localization in Water Distribution Networks By Directly Fitting the Learning Parameters of a Gaussian Naive Bayes Classifier, in: 2022 IEEE Int. Conf. Big Data Big Data, 2022: pp. 4854\u20134859. https:\/\/doi.org\/10.1109\/BigData55660.2022.10020580.","DOI":"10.1109\/BigData55660.2022.10020580"},{"key":"10.1016\/j.aei.2026.104978_b1100","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103309","article-title":"A novel method for leakage monitoring in Network-Level urban medium- and Low-pressure natural gas pipelines combining information theory and Light Gradient Boosting","volume":"65","author":"Huang","year":"2025","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.aei.2026.104978_b1105","first-page":"1087","article-title":"Safety: Cloud-based Leak Prediction using XGBoost, in, IEEE 16th Int","volume":"2024","author":"Jagadeesh","year":"2024","journal-title":"Conf. Comput. Intell. Commun. Netw. CICN"},{"key":"10.1016\/j.aei.2026.104978_b1110","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijpvp.2022.104655","article-title":"XGBoost algorithm-based prediction of safety assessment for pipelines","volume":"197","author":"Liu","year":"2022","journal-title":"Int. J. Press. Vessels Pip."},{"key":"10.1016\/j.aei.2026.104978_b1115","doi-asserted-by":"crossref","first-page":"116452","DOI":"10.1109\/ACCESS.2023.3326075","article-title":"Multi-Parameter Maximum Corrosion Depth Prediction Model for buried Pipelines based on GSCV-XGBoost","volume":"11","author":"Wang","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.aei.2026.104978_b1120","doi-asserted-by":"crossref","first-page":"3138","DOI":"10.3390\/app11073138","article-title":"A Hybrid Hidden Markov Model for Pipeline Leakage Detection","volume":"11","author":"Zhang","year":"2021","journal-title":"Appl. Sci."},{"key":"10.1016\/j.aei.2026.104978_b1125","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1007\/s13349-021-00481-0","article-title":"Hidden Markov models for pipeline damage detection using piezoelectric transducers","volume":"11","author":"Zhang","year":"2021","journal-title":"J. Civ. Struct. Health Monit."},{"key":"10.1016\/j.aei.2026.104978_b1130","doi-asserted-by":"crossref","unstructured":"A. Mohamed, M.S. Hamdi, S. Tahar, Decision Tree-Based Approach for Defect Detection and Classification in Oil and Gas Pipelines, in: K. Arai, R. Bhatia, S. Kapoor (Eds.), Proc. Future Technol. Conf. FTC 2018, Springer International Publishing, Cham, 2019: pp. 490\u2013504. https:\/\/doi.org\/10.1007\/978-3-030-02686-8_37.","DOI":"10.1007\/978-3-030-02686-8_37"},{"key":"10.1016\/j.aei.2026.104978_b1135","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s44163-023-00069-1","article-title":"Image-based and risk-informed detection of Subsea Pipeline damage","volume":"3","author":"Spahi\u0107","year":"2023","journal-title":"Discov. Artif. Intell."},{"key":"10.1016\/j.aei.2026.104978_b1140","doi-asserted-by":"crossref","first-page":"146","DOI":"10.3390\/app8020146","article-title":"Pipeline Leak Localization based on FBG Hoop Strain Sensors combined with BP Neural Network","volume":"8","author":"Jia","year":"2018","journal-title":"Appl. Sci."},{"key":"10.1016\/j.aei.2026.104978_b1145","doi-asserted-by":"crossref","first-page":"661","DOI":"10.3390\/pr8060661","article-title":"A Feed-Forward Back Propagation Neural Network Approach to Predict the Life Condition of Crude Oil Pipeline","volume":"8","author":"Shaik","year":"2020","journal-title":"Processes"}],"container-title":["Advanced Engineering Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626006701?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626006701?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T14:37:56Z","timestamp":1782484676000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1474034626006701"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":229,"alternative-id":["S1474034626006701"],"URL":"https:\/\/doi.org\/10.1016\/j.aei.2026.104978","relation":{},"ISSN":["1474-0346"],"issn-type":[{"value":"1474-0346","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"When sensing meets computational Intelligence: A comprehensive survey on pipeline damage detection and localization","name":"articletitle","label":"Article Title"},{"value":"Advanced Engineering Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aei.2026.104978","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"104978"}}