Abstract
This project proposed an integrated approach for the non-destructive evaluation of the health status – represented by compressive strength – of any concrete, including well-cured (or intact) and degraded (or partially damaged) due to freeze-thaw cycling. To use this approach, no pre-recorded information (e.g., mixture proportions of concrete, curing and maintenance history, and service history) is needed. Instead, several conventional non-destructive testing methods (NDTs), assisted by hyperspectral camera, can be employed to generate a dataset that informs a pre-trained artificial intelligence (AI) to predict strength. As the core of this approach, the dedicated AI was trained through this project. To this end, hundreds of concrete specimens of 17 distinct mixture proportions were prepared and cured to an age of 56 days. They were then subjected to freeze-thaw cycles. The NDTs were used to trace the development of various physicochemical features of the concrete mixtures and compressive strength was tested following the curing and degradation processes to curate a first-of-its-kind database, and the AI was trained and validated using this database.
Recommended Citation
Ma, Hongyan; Chen, Genda; Cai, Kangyi; and Hafiz, Rezwana, "Final Report - Health Inspection of Concrete Pavement and Bridge Members Exposed to Freeze-Thaw Service Environment" (2024). Project SN-7. 1.
https://scholarsmine.mst.edu/project_sn-7/1
Department(s)
Civil, Architectural and Environmental Engineering
Research Center/Lab(s)
INSPIRE - University Transportation Center
Sponsor(s)
Office of the Assistant Secretary for Research and Technology U.S. Department of Transportation 1200 New Jersey Avenue, SE Washington, DC 20590
Keywords and Phrases
Concrete, Compressive Strength, Freeze-Thaw, Hyperspectral
Report Number
INSPIRE-28
Document Type
Technical Report
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2025 Missouri University of Science and Technology, All rights reserved.
Publication Date
June 30, 2024
Comments
Principal Investigator: Hongyan Ma, Ph. D.
Co-Principal Investigator: Genda Chen, Ph. D., P. E.
Grant #: USDOT # 69A3551747126
Grant Period: 11/30/2016 - 09/30/2024
Project Period: 01/01/2020 - 06/30/2024
The investigation was conducted under the auspices of the INSPIRE University Transportation Center.