
Concrete Grand Challenge Experimental Database
The Concrete Grand Challenge Experimental Database is a community-driven initiative aimed at creating a comprehensive, curated, and machine-readable experimental database for concrete and related infrastructure materials. The goal is to support the calibration, validation, and benchmarking of computational models through standardized, traceable, and reusable experimental data contributed by researchers, laboratories, industry, and government agencies worldwide.
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The initiative seeks to transform fragmented experimental datasets into a unified and interoperable scientific infrastructure capable of supporting predictive modeling, uncertainty quantification, digital twins, AI-assisted scientific discovery, and next-generation engineering simulations. This webpage provides instructions for the curation and submission of experimental datasets.
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The structure, terminology, parametrization schemes, controlled vocabularies, and data standards adopted by the database are being developed and continuously refined through the collaborative efforts of the Concrete Grand Challenge Working Group, which brings together researchers and experts from academia, industry, and government laboratories.
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The Concrete Grand Challenge Working Group thanks the organizations that are supporting this effort!
Concrete Grand Challenge Working Group
The database structure and collection strategy is the result of the concerted effort of the Concrete Grand Challenge Working Group. Particular credit goes to M.Z. Naser (Clemson University) for the development of the AI-assisted curation workflow.​​
First Name | Last Name | Institution | Country |
|---|---|---|---|
Ab | van den Bos | NLyseConsultants | NED |
Ali | Behnood | University of Missisipi | USA |
Anca | Ferche | UT at Austin | USA |
Ann | Goodell | Cal Poly | USA |
Christian | Carloni | Case Western University | USA |
Constantino | Menna | University of Naples | ITA |
Dimitrios | Kalliontzis | University of Huston | USA |
Ebenezer | Fanijo | Georgia Tech | USA |
Ehab | Hamed | The Un. of New South Wales | AUS |
Erik | Schlangen | TU Delft | NED |
Fabio | Matta | University of South Carolina | USA |
Gabriel | Ackall | Virginia Tech | USA |
Gianluca | Cusatis | Northwestern University | USA |
Giovanni | Di Luzio | Politecnico di Milano | ITA |
Jan | Cervenka | Cervenka Consulting | CZE |
Jia-Liang | Le | University of Minnesota | USA |
Jiaqi | Liu | Northwestern University | USA |
John | Bolander | UC Davis | USA |
Jorg | Unger | BAM | DEU |
Luna | Hasani | Kiewit Corporation | USA |
Matthew | Soltani | Bradley University | USA |
Mohammed | Alnaggar | ORNL | USA |
MZ | Naser | Clemson University | USA |
Reza | Moini | Princeton University | USA |
Shady | Gomaa | University of Alabama | USA |
Stephanie | Paal | Texas A&M University | USA |
Viktor | Gribniak | Vilnius Gediminas Tech. Un. | LTU |
Yuchen | Li | Northwestern University | USA |
For additional information or expressions of interest regarding participation in the Concrete Grand Challenge Working Group, please contact: Gianluca Cusatis, Northwestern University, g-cusatis@northwestern.edu.
Sponsors and Supporting Organizations
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IA-ConCeep - thank you for hosting this initiative.
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IA-FraMCoS - thank you for contributing initial resources for the AI-assisted data extractions.
Disclaimer
The Concrete Grand Challenge Experimental Database is intended solely for research, educational, and scientific purposes. Contributors retain ownership of their original data and are responsible for ensuring that all submitted material can be legally shared and redistributed. The database administrators and the Concrete Grand Challenge Working Group make no warranties regarding the completeness, accuracy, reliability, or suitability of the contributed data and assume no liability for errors, omissions, interpretations, or consequences arising from the use of the database or associated material. Users are responsible for independently verifying all data, metadata, and derived results before use in research, engineering, design, regulatory, or commercial applications. Any redistribution or reuse of contributed datasets should appropriately acknowledge the original data sources and contributors.