Probability of Detection in Corrosion Monitoring with FE-C Coated LPFG Sensors (SN-8)

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% lower confidence bounds. 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 applications. To achieve the project objectives, three tasks will be planned and executed. First, standard test specimens and experimental designs will be prepared to provide relevant cases to sensors’ field applications in steel reinforced concrete (RC) structures. Second, the proposed statistical methods will be validated to determine the largest mass loss with 90% probability of detection at a 95% lower confidence level. Third and last, the required data, computational efficiency, probability distribution sensitivity and model robustness of the two methods will be compared to guide their selection in practice. Approach and Methodology: Sensor technologies can potentially improve operation efficiency, cost effectiveness, structural reliability, and inspector safety in bridge asset management. However, their implementation must be proven through statistically-viable laboratory performances and successful field operations. Key to assessing their performance is methods to enable the POD analysis for the sensors used in corrosion monitoring in bridges. Following is a presentation of two methods with illustrative examples in aerospace application. Overall Objectives: This project will qualify corrosion monitoring as an inspection tool for bridges. The statistical methods developed for critical mass loss are transferrable to evaluating the reliability of other measurement technologies. They can be applied to other structures. Once implemented, these data will make inspection more reliable and cost-effective. Scope of Work in Year 1: (1) Develop test protocols for corrosion sensor/mass loss combination, (2) Qualify the POD for corrosion monitoring with two statistical methods, (3) Compare the two statistical methods in application scenarios.

    Language

    • English

    Project

    • Status: Active
    • Contract Numbers:

      INSPIRE UTC: 0006925

      69A3551747126

    • Sponsor Organizations:

      Inspecting and Preserving Infrastructure through Robotic Exploration University Transportation Center

      Missouri University of Science and Technology
      Rolla, MO  United States  65409

      Office of the Assistant Secretary for Research and Technology

      University Transportation Centers Program
      Department of Transportation
      Washington, DC  United States  20590
    • Managing Organizations:

      Inspecting and Preserving Infrastructure through Robotic Exploration University Transportation Center

      Missouri University of Science and Technology
      Rolla, MO  United States  65409
    • Performing Organizations:

      Missouri University of Science & Technology, Rolla

      Department of Engineering
      202 University Center
      Rolla, MO    65409
    • Principal Investigators:

      Chen, Genda

    • Start Date: 20220101
    • Expected Completion Date: 20211231
    • Actual Completion Date: 0
    • USDOT Program: University Transportation Centers

    Subject/Index Terms

    Filing Info

    • Accession Number: 01847970
    • Record Type: Research project
    • Source Agency: Inspecting and Preserving Infrastructure through Robotic Exploration University Transportation Center
    • Contract Numbers: INSPIRE UTC: 0006925, 69A3551747126
    • Files: UTC, RIP
    • Created Date: Jun 5 2022 2:19PM