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Comprehensive dataset for performance evaluation of SCM-blended concrete
Weilin Liu1, Huadong Mo2, Chi King Lee1
1School of Engineering and Technology, University of New South Wales, Canberra, Australia.
Data in Brief
|June 12, 2025
Summary
Supplementary cementitious materials (SCMs) reduce concrete's carbon footprint. A new global dataset of SCM-blended concrete aids machine learning models for sustainable, high-performance mixtures.
Area of Science:
- Materials Science
- Civil Engineering
- Sustainable Construction
Background:
- Supplementary cementitious materials (SCMs) are crucial for reducing the environmental impact of concrete by replacing Portland cement.
- SCMs enhance concrete's mechanical properties and durability due to their pozzolanic reactions.
- Machine learning (ML) offers an efficient alternative to experimental methods for predicting concrete properties.
Purpose of the Study:
- To compile a comprehensive, global dataset of SCM-blended concrete properties.
- To overcome limitations of existing region-specific datasets.
- To facilitate the development of sustainable and high-performance concrete mixtures using ML.
Main Methods:
- Compiled data from 136 publications across 27 countries.
- Included concrete incorporating fly ash (FA), silica fume (SF), and ground granulated blast furnace slag (GGBFS).
- Focused on both mechanical properties and durability.
Main Results:
- Created an extensive dataset covering diverse geographical and environmental conditions.
- The dataset encompasses mechanical properties and durability indicators.
- Provides a valuable resource for ML model training and validation.
Conclusions:
- The compiled dataset addresses the limitations of region-specific data.
- It enables more accurate ML-based predictions for SCM-blended concrete globally.
- Facilitates advancements in sustainable and high-performance concrete technology.
Keywords:
DurabilityFly ashGround granulated blast furnace slagMachine learningMechanical propertiesSCM-based concreteSilica fumeMore Related Videos
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