Over het archief
Het OWA, het open archief van het Waterbouwkundig Laboratorium heeft tot doel alle vrij toegankelijke onderzoeksresultaten van dit instituut in digitale vorm aan te bieden. Op die manier wil het de zichtbaarheid, verspreiding en gebruik van deze onderzoeksresultaten, alsook de wetenschappelijke communicatie maximaal bevorderen.
Dit archief wordt uitgebouwd en beheerd volgens de principes van de Open Access Movement, en het daaruit ontstane Open Archives Initiative.
Basisinformatie over ‘Open Access to scholarly information'.
Uncertainty-quantified unsupervised transfer learning for ultrasonic-based corrosion detection in underwater steel pipes
Lu, H.; Haris, M.; Chinchilla, S.; Fang, C.; Soccol, D.; Gryllias, K.; Chronopoulos, D. (2025). Uncertainty-quantified unsupervised transfer learning for ultrasonic-based corrosion detection in underwater steel pipes. Automation in Construction 178: 106385. https://dx.doi.org/10.1016/j.autcon.2025.106385
In: Automation in Construction. Elsevier. ISSN 0926-5805; e-ISSN 1872-7891, meer
|  |
| Author keywords |
Unsupervised transfer learning;Uncertainty quantification;Automation;Underwater ultrasonic wave;Underwater corrosion;Marine structural health monitoring;transport |
| Auteurs | | Top |
- Lu, H.
- Haris, M.
- Chinchilla, S.
- Fang, C.
|
- Soccol, D.
- Gryllias, K., meer
- Chronopoulos, D.
|
|
| Abstract |
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. |
IMIS is ontwikkeld en wordt gehost door het VLIZ.
