Description
This project aims to develop two statistical methods for determining the probability of detection in corrosion monitoring using long period fiber gratings (LPFG) sensors with thin Fe-C coating, validate these methods from independent laboratory tests, and determine the steel mass loss at 90% probability of detection and the largest steel mass loss that may miss from a corrosion inspection at 95% upper confidence bounds. LPFG sensors could reflect the corrosion process by the wavelength shift in the transmission spectrum due to the change of the refractive index of the Fe-C coating. POD is a method used to determine the capability of an inspection as a function of defect type and defect size. The two statistical methods are referred to as the Mass Loss-at-Detection (MLaD) method and the Random- Effects Generalization (REG) method.
Presentation Date
10 Aug 2021, 1:00-1:10 pm
Meeting Name
INSPIRE-UTC 2021 Annual Meeting
Department(s)
Civil, Architectural and Environmental Engineering
Document Type
Poster
Document Version
Final Version
File Type
text
Language(s)
English
Probability of Detection in Corrosion Monitoring with Fe-C Coated LPFG Sensors
This project aims to develop two statistical methods for determining the probability of detection in corrosion monitoring using long period fiber gratings (LPFG) sensors with thin Fe-C coating, validate these methods from independent laboratory tests, and determine the steel mass loss at 90% probability of detection and the largest steel mass loss that may miss from a corrosion inspection at 95% upper confidence bounds. LPFG sensors could reflect the corrosion process by the wavelength shift in the transmission spectrum due to the change of the refractive index of the Fe-C coating. POD is a method used to determine the capability of an inspection as a function of defect type and defect size. The two statistical methods are referred to as the Mass Loss-at-Detection (MLaD) method and the Random- Effects Generalization (REG) method.
Comments
This work was funded by the INSPIRE University Transportation Center (UTC).