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Statistical Models Supporting the High-Performance Self-Compacting Concrete (HPSCC) Design Process for High Strength.

Aleksandra Kostrzanowska-Siedlarz¹, Jacek Gołaszewski¹

¹Faculty of Civil Engineering, Silesian University of Technology, 44-100 Gliwice, Poland.

Materials (Basel, Switzerland)

|January 21, 2022

Summary

This study presents universal statistical models to aid in designing self-compacting high-performance concrete (HPSCC) for high strength. The models analyze key ingredient factors impacting compressive strength, supporting optimized concrete mix design.

Keywords:

ANOVA HPSCC compressive strength high-performance self-compacting concrete

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Area of Science:

Civil Engineering
Materials Science
Statistical Modeling

Background:

Optimizing concrete mix design is crucial for achieving desired material properties.
Self-compacting high-performance concrete (HPSCC) requires careful selection of ingredients for superior performance.
Existing models may not universally apply to the diverse range of HPSCC formulations.

Purpose of the Study:

To develop universal statistical models for the design of high-strength self-compacting high-performance concrete (HPSCC).
To identify and quantify the impact of key ingredient factors on HPSCC compressive strength.
To provide a data-driven tool to support engineers in the HPSCC design process.

Main Methods:

Extensive statistical analysis of literature research data.
Multivariate selection of experimental points using five variable factors at three levels.
Analysis of Variance (ANOVA) to determine the significance of material factors on compressive strength.

Main Results:

Development of highly fit statistical models applicable to a typical range of HPSCC ingredients.
Identification of significant material factors influencing the compressive strength of HPSCC.
Quantification of the interactions between material factors and their effect on concrete strength.

Conclusions:

The developed statistical models offer a robust framework for designing HPSCC with enhanced compressive strength.
The findings provide valuable insights into the relationship between ingredient properties and HPSCC performance.
This research supports the advancement of HPSCC technology through data-driven design optimization.