U.S. Department of Transportation
Federal Highway Administration
1200 New Jersey Avenue, SE
Washington, DC 20590
202-366-4000
Federal Highway Administration Research and Technology
Coordinating, Developing, and Delivering Highway Transportation Innovations
SUMMARY |
This report is an archived publication and may contain dated technical, contact, and link information |
Publication Number: FHWA-HRT-22-016 Date: January 2022 |
Publication Number: FHWA-HRT-22-016 Date: January 2022 |
PDF Version (6.25 MB)
Technical Report Documentation Page
1. Report No.
FHWA-HRT-22-016 |
2. Government Accession No. | 3 Recipient's Catalog No. | ||
4. Title and Subtitle
Machine Learning and Pozzolans for Concrete for Highway Pavements and Structures |
5. Report Date January 2022 |
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6. Performing Organization Code | ||||
7. Author(s)
Volpe National Transportation Systems Center |
8. Performing Organization Report No.
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9. Performing Organization Name and Address Volpe National Transportation Systems Center |
10. Work Unit No. (TRAIS) |
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11. Contract or Grant No.
693JJ320F000509 |
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12. Sponsoring Agency Name and Address
Federal Highway Administration |
13. Type of Report and Period Covered
Workshop Summary Report |
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14. Sponsoring Agency Code HRTM-30 |
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15. Supplementary Notes
The Contracting Officer's representative is Kelley McKinley (HRTM-20). The Technical Contacts are Robert Spragg and Jack Youtcheff (HRDI-10). |
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16. Abstract
On December 14 and December 15, 2020, the Federal Highway Administration's (FHWA) Office of Infrastructure Research and Development held a virtual workshop called "Machine Learning and Pozzolans for Concrete Infrastructure Materials" with 80 participants in attendance. The workshop, consisting of eight presentations, largely focused on three ongoing Exploratory Advanced Research Program projects and one National Cooperative Highway Research Program project related to the pressing issue of finding new sources of supplementary cementitious material (SCM) for use in concrete for our Nation's roadway infrastructure. |
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17. Key Words
SCM, fly ash, NNP, machine learning, cement, concrete, reclaimed fly ash, artificial intelligence |
18. Distribution Statement
No restrictions. This document is available to the public through the National Technical Information Service, Springfield, VA 22161. |
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19. Security Classification Unclassified |
20. Security Classification Unclassified |
21. No. of Pages 12 |
22. Price N/A |
Form DOT F 1700.7 (8-72) | Reproduction of completed page authorized |
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