Experimental study on optimum proportioning of Portland cements, limestone, metakaolin, and fly ash for obtaining quaternary cementitious composites
Source abstract
Abstract
This paper presents an experimental study, along with a multi-objective optimum of quaternary composite pastes that contain limestone, metakaolin, and fly ash using a response surface methodology. First, the three factors and three levels of the Box–Behnken design were used in a combination of quaternary composite pastes. The maximum limits of limestone, metakaolin, and fly ash are 10%, 15%, and 30%, respectively. Experimental studies around the workability, strength, hydration heat, electrical resistivity, and hydration products were carried out for these composite pastes. The experimental results were evaluated using a second-order polynomial model. The results show that with the increasing of the mineral admixtures, the flow, compressive strength, heat of hydration, and CO2 emission decrease, and the electrical resistivity is an exponential function as an amount of limestone and metakaolin. Moreover, XRD analysis of the changes in the carboaluminate peaks proved the synergistic effect between metakaolin, fly ash, and limestone. In addition, a multi-objective optimum design was carried out for the material design of composite pastes based on a composite desirability function. The optimum design objectives were aimed at 28-day compressive strength and flow, low CO2 emission and heat of hydration, and high resistivity. The trends of numerous qualities of optimal combinations are in agreement according to the experimental results. Multi-objective optimum design is essential for designing composite pastes that consider strength, workability, ecosystem, thermal performance, and durability.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Concrete and Cement Materials Research · Innovative concrete reinforcement materials · Recycled Aggregate Concrete Performance
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (46)
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