Probability of Detection in Corrosion Monitoring with Fe-C Coated LPFG Sensors
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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, 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% lower confidence bounds, and develop and validate an analytical formulation of the most critical reduction in load capacity of the superstructure of steel-girder bridges based on limited sensor data. The two statistical methods are referred to as the Mass Loss-at- Detection (MLaD) method and the Random-Effects Generalization (REG) method. They will be evaluated in terms of computational efficiency, sensitivity to probability distribution assumptions, and robustness to departure from model assumptions. The one with overall superior performance will be recommended for corrosion monitoring in bridge applications.
Presentation Date
10 Aug 2021, 11:00 am - 11:30 am
Meeting Name
INSPIRE-UTC 2021 Annual Meeting
Department(s)
Civil, Architectural and Environmental Engineering
Document Type
Presentation
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, 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% lower confidence bounds, and develop and validate an analytical formulation of the most critical reduction in load capacity of the superstructure of steel-girder bridges based on limited sensor data. The two statistical methods are referred to as the Mass Loss-at- Detection (MLaD) method and the Random-Effects Generalization (REG) method. They will be evaluated in terms of computational efficiency, sensitivity to probability distribution assumptions, and robustness to departure from model assumptions. The one with overall superior performance will be recommended for corrosion monitoring in bridge applications.
