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Publication
Numerical Design and Optimisation of Self-Compacting High Early-Strength Cement-Based Mortars
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Advisor(s)
Abstract(s)
The use of SCC in Europe began in the 1990s and was mainly promoted by the precast
industry. Precast companies generally prefer high early-strength concrete mixtures to accelerate
their production rate, reducing the demoulding time. From a materials science point of view,
self-compacting and high early-strength concrete mixes may be challenging because they present
contradicting mixture design requirements. For example, a low water/binder ratio (w/b) is key to
achieving high early strength. However, it may impact the self-compacting ability, which is very
sensitive to Vw/Vp. As such, the mixture design can be complex. The design of the experimental
approach is a powerful tool for designing, predicting, and optimising advanced cement-based
materials when several constituent materials are employed and multi-performance requirements
are targeted. The current work aimed at fitting models to mathematically describe the flow ability,
viscosity, and mechanical strength properties of high-performance self-compacting cement-based
mortars based on a central composite design. The statistical fitted models revealed that Vs/Vm
exhibited the strongest (negative) effect on the slump-flow diameter and T-funnel time. Vw/Vp
showed the most significant effect on mechanical strength. Models were then used for mortar
optimisation. The proposed optimal mixture represents the best compromise between self-compacting
ability—a flow diameter of 250 mm and funnel time equal to 10 s—and compressive strength higher
than 50 MPa at 24 h without any special curing treatment.
Description
Keywords
Self-compacting concrete High early strength Mixture design Design of experiments Response model Faculdade de Ciências Exatas e da Engenharia
Citation
Cangussu, N.; Matos, A.M.; Milheiro-Oliveira, P.; Maia, L. Numerical Design and Optimisation of Self-Compacting High Early-Strength Cement-Based Mortars. Appl. Sci. 2023, 13, 4142. https://doi.org/10.3390/ app13074142
Publisher
MDPI