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Optimization of the Granular Mixture of Natural Rammed Earth Using Compressible Packing Model

Author

Listed:
  • Atar Dialmy

    (ERIC (LAGCEH), Hassania School of Public Works, Casablanca BP 8108, Morocco)

  • Mustapha Rguig

    (ERIC (LAGCEH), Hassania School of Public Works, Casablanca BP 8108, Morocco)

  • Mehdi Meliani

    (ERIC (LAGCEH), Hassania School of Public Works, Casablanca BP 8108, Morocco)

Abstract

Rammed earth (RE) construction is an ancestral technique that allows for the building of durable and resistant constructions. RE buildings are sustainable and environment-friendly, and ensure energy optimization during the construction cycle. For these reasons, many of the following RE characteristics are studied: mechanical strength, seismic resistance, and thermal performance. However, the mix design of RE soils has been rarely studied. There is practically no scientific approach that allows for defining precise dosages of clay, silt, sand, and gravel used in RE materials. The broader aim of this article is to determine a scientific mix design method to find an optimal RE granular mixture. The compressible packing model (CPM) is applied to study the effect of every granular class on compactness and define the optimum mixture. Many tests have been conducted such as the Modified Proctor, compression test, and ultrasonic velocity pulse test to evaluate the relevance of this model. The results suggest many granular corrections for RE material that considerably enhance compactness and unconfined compressive strength (UCS). It was found that one granular correction provides a 137% increase in the initial UCS. Therefore, this approach enables the defining of which granular class is to be added or reduced to optimize the mechanical properties of RE.

Suggested Citation

  • Atar Dialmy & Mustapha Rguig & Mehdi Meliani, 2023. "Optimization of the Granular Mixture of Natural Rammed Earth Using Compressible Packing Model," Sustainability, MDPI, vol. 15(3), pages 1-17, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2698-:d:1055284
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