Event Title

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 2020, 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

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Aug 10th, 11:00 AM Aug 10th, 11:30 AM

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.