Abstract
Superplasticizers (SPs), also known as naturally high-water reducers, are substances used to create high-strength concrete. Due to the system’s complexity, predicting concrete’s compressive strength can be difficult. In this study, a prediction model for the compressive strength with SP was developed to handle the high-dimensional complex non-linear relationship between the mixing design of SP and the compressive strength of concrete. After performing a statistical analysis of the dataset, a correlation analysis was performed and then 16 supervised machine learning regression techniques were used. Finally, by using the Extra Trees method and creating the SP variable values, it was shown that the compressive strength values of concrete increased with the addition of SP in the optimal dose. The results indicate that superplasticizers can often reduce the water content of concrete by 25 to 35 per cent and consequently resistivity increased by 50 to 75 per cent and the optimum amount of superplasticizers was up to 12 kg per cubic meter as well. From one point, the increase in superplasticizers does not lead to a rise in the concrete compressive strength, and it remains constant. According to the findings, SP additive has the most impact on concrete’s compressive strength after cement. Given the scant information now available on concrete-including superplasticizer, it is prudent to design a concrete mixing plan for future studies. It is also conceivable to investigate how concrete’s compressive strength is impacted by water reduction.
Original language | English |
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Article number | 21 |
Number of pages | 13 |
Journal | Infrastructures |
Volume | 8 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2 Feb 2023 |
Bibliographical note
Publisher Copyright:© 2023 by the authors.
Keywords
- compressive strength
- dynamic actions
- machine learning
- regression
- superplasticizer
ASJC Scopus subject areas
- Civil and Structural Engineering
- Building and Construction
- General Materials Science
- Geotechnical Engineering and Engineering Geology
- Computer Science Applications