4.6 Article

Designing the Composition of Cement-Stabilized Rammed Earth with the Association Analysis Application

Journal

MATERIALS
Volume 14, Issue 6, Pages -

Publisher

MDPI
DOI: 10.3390/ma14061390

Keywords

cement-stabilized rammed earth; rammed earth; association analysis; market basket analysis; rule finding; rule extraction; data exploration

Funding

  1. University of Technology Research grant of Scientific Council of the Discipline of Civil Engineering and Transport

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CSRE is a sustainable and cost-saving structural composite material that is sensitive to other factors due to its low cement content. An innovative algorithm and statistical analysis are needed to achieve the required compressive strength by designing the composition of CSRE with high confidence.
The main advantage of the structural composite material known as cement-stabilized rammed earth (CSRE) is that it can be formulated as a sustainable and cost-saving solution. The use of the aggregates collected very close to a construction site allows economizing on transportation costs. Another factor that makes sustainability higher and the costs lower is a small addition of cement to the CSRE in comparison to the regular concrete. However, the low cement content makes the compressive strength of this structural material sensitive to other factors. One of them is the composition of the aggregates. Considering the fact that they are obtained locally, without full laboratory control of their composition, achieving the required compressive strength of CSRE is a challenge. To assess the possibility of achieving a certain compressive strength of CSRE, based on its core properties, the innovative algorithm of designing CSRE is proposed. Based on 582 crash-test of CSRE samples of different composition and compaction levels, along with the use of association analysis, the spreadsheet application is created. Applying the algorithm and the spreadsheet, it is possible to design the composition of CSRE with high confidence of achieving the required compressive strength. The algorithm considers a random character of aggregates locally collected and proposes multiple possible ways of increasing the confidence. They are verified through innovatively applied association analyses in the enclosed spreadsheet.

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