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Publication
Exploring Design Optimization of Self-Compacting Mortars with Response Surface Methodology
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Advisor(s)
Abstract(s)
The ever-evolving construction sector demands technological developments to provide
consumers with products that meet stringent technical, environmental, and economic requirements.
Self-compacting cementitious mixtures have garnered significance in the construction market due to
their enhanced compaction, workability, fluidity, and mechanical properties. This study aimed to
harness the potential of statistical response surface methodology (RSM) to optimize the fresh proper ties and strength development of self-compacting mortars. A self-compacting mortar repository was
used to build meaningful and robust models describing D-Flow and T-Funnel results, as well as the
compressive strength development after 24 h (CS24h) and 28 days (CS28d) of curing. The quantitative
input factors considered were A (water/cement), B (superplasticizer/powder), C (water/powder),
and D (sand/mortar), and the output variables were Y1 (D-Flow), Y2 (T-Funnel), Y3 (CS24h), and
Y4 (CS28d). The results found adjusted response models, with significant R2 values of 87.4% for the
D-Flow, 93.3% for the T-Funnel, and 79.1% for the CS24h. However, for the CS28d model, a low R2 of
39.9% was found. Variable A had the greatest influence on the response models. The best correlations
found were between inputs A and C and outputs Y1 and Y2, as well as input factors A and D for
responses Y3 and Y4. The resulting model was enhanced, thereby resulting in a global desirability of
approximately 60%, which showcases the potential for the further refinement and optimization of
RSM models applied to self-compacting mortars.
Description
Keywords
Self-compacting mortars Design of experiments Fresh properties Compressive strength ANOVA . Faculdade de Ciências Exatas e da Engenharia
Citation
: Rocha, S.; Ascensão, G.; Maia, L. Exploring Design Optimization of Self-Compacting Mortars with Response Surface Methodology. Appl. Sci. 2023, 13, 10428. https://doi.org/ 10.3390/app131810428
Publisher
MDPI