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Description
The 2001 study sponsored by FHWA raised serious concern on the consistency and reliability of visual inspection. Although consistent ratings can be obtained with a good QA/QC program, based on a recent study by the PI, the concern for reliability of defect detection remains. With the adoption of the recent AASHTO Manuel for Bridge Element Inspection, the new inspection approach not only requires rating for bridge elements, but also the location and extent of deterioration. Since autonomous robotic systems generate an enormous amount of inspection data, deducing from the data to a simple rating along with the location and extent of deterioration is a significant challenge. For example, RABITTM has been used to inspect concrete bridge decks with six devices, including ground penetrating radar (GPR), impact-echo and ultrasonic surface wave. However, the probability of detection (POD) for damage has not been fully demonstrated to be significantly improved using multiple devices.
This project aims to develop new fusion strategies of data collected from multiple NDE devices for improved POD based on further understanding and modeling of damage detection mechanisms, and to develop algorithms for the derivation of bridge ratings from identified damage and visual inspection findings.
Presentation Date
03 Aug 2020, 11:30 am - 12:00 pm
Meeting Name
INSPIRE-UTC 2020 Annual Meeting
Department(s)
Civil, Architectural and Environmental Engineering
Document Type
Presentation
Document Version
Final Version
File Type
text
Language(s)
English
Quantitative Bridge Inspection Ratings using Autonomous Robotic Systems
The 2001 study sponsored by FHWA raised serious concern on the consistency and reliability of visual inspection. Although consistent ratings can be obtained with a good QA/QC program, based on a recent study by the PI, the concern for reliability of defect detection remains. With the adoption of the recent AASHTO Manuel for Bridge Element Inspection, the new inspection approach not only requires rating for bridge elements, but also the location and extent of deterioration. Since autonomous robotic systems generate an enormous amount of inspection data, deducing from the data to a simple rating along with the location and extent of deterioration is a significant challenge. For example, RABITTM has been used to inspect concrete bridge decks with six devices, including ground penetrating radar (GPR), impact-echo and ultrasonic surface wave. However, the probability of detection (POD) for damage has not been fully demonstrated to be significantly improved using multiple devices.
This project aims to develop new fusion strategies of data collected from multiple NDE devices for improved POD based on further understanding and modeling of damage detection mechanisms, and to develop algorithms for the derivation of bridge ratings from identified damage and visual inspection findings.
Comments
IM-2