{"refrec":{"BRefID":449134,"RR":"<b>Lu, H.; Haris, M.; Chinchilla, S.; Fang, C.; Soccol, D.; Gryllias, K.; Chronopoulos, D.</b> (2025). Uncertainty-quantified unsupervised transfer learning for ultrasonic-based corrosion detection in underwater steel pipes. <i>Automation in Construction 178</i>: 106385. <a href=\"https://dx.doi.org/10.1016/j.autcon.2025.106385\" target=\"_blank\">https://dx.doi.org/10.1016/j.autcon.2025.106385</a>","BEntID":446972,"PublicFlag":1,"CheckedFlag":1,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>Automation in Construction 178</i>: 106385. <a href=\"https://dx.doi.org/10.1016/j.autcon.2025.106385\" target=\"_blank\">https://dx.doi.org/10.1016/j.autcon.2025.106385</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":0,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Lu, H.; Haris, M.; Chinchilla, S.; Fang, C.; Soccol, D.; Gryllias, K.; Chronopoulos, D.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Lu, H. <i>et al.</i>","Englishabstract":"Automation-ready subsea and coastal asset inspection requires reliable, low-touch structural health monitoring, critical for digitalized underwater pipeline management. This paper introduces a deep-ensemble unsupervised transfer-learning (DE-UTL) framework to automatically quantify corrosion-induced thickness loss from ultrasonic signals while quantifying aleatoric and epistemic uncertainties. DE-UTL employs an enhanced transformer architecture with adaptive weighting and multi-scale feature extraction, integrated with a Wasserstein-distance domain adapter supported by consistency regularization and reconstruction, ensuring environmental transferability and mitigating retraining needs. Ten UTL models are integrated via a deep ensemble to evaluate uncertainty. Underwater corrosion and ultrasonic experiments on two pipe types are conducted, revealing that pipe thickness, weight, and ultrasonic signal amplitude and phase exhibit trend-like variation with corrosion severity. Experimental validation confirms DE-UTL's effective corrosion thickness quantification in both geometry-only and geometry-environmental (air-to-water) transfer scenarios. Integrating corrosion experimentation, unsupervised transfer learning, and uncertainty quantification, DE-UTL shows strong potential for automated, scalable, real-time underwater asset inspection.","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"Uncertainty-quantified unsupervised transfer learning for ultrasonic-based corrosion detection in underwater steel pipes","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2026-10-05 01:50:03.718606","timezone_type":1,"timezone":"+02:00"},"AuthorKeywords":"Unsupervised transfer learning;Uncertainty quantification;Automation;Underwater ultrasonic wave;Underwater corrosion;Marine structural health monitoring;transport","OtherDescriptors":null,"Notes":null,"AnaPub":2025,"MonPub":null,"DateUpdate":"2026-09-28","DateCreate":"2026-09-28","SecASFANote":null,"ConfID":null,"PeerRev":1,"VlizCoreFlag":1,"WoScode":"WOS:001540630500001","VABBcode":null,"OpenAcc":0,"DOI":"10.1016/j.autcon.2025.106385"},"refs":null,"anarec":{"AnaID":449134,"PubliDate":2025,"Pagination":"106385","XtraPublOfAnaID":null,"ISBN":null,"Volume":"178","Issue":null,"BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":259777,"SerRR":"Automation in Construction. 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