Designing the Composition of Cement Stabilized Rammed Earth Using Artificial Neural Networks

Hubert Jerzy Anysz , Piotr Leon Narloch


Cement stabilized rammed earth (CRSE) is a sustainable, low energy consuming construction technique which utilizes inorganic soil, usually taken directly from the construction site, with a small addition of Portland cement as a building material. This technology is gaining popularity in various regions of the world, however, there are no uniform standards for designing the composition of the CSRE mixture. The main goal of this article is to propose a complete algorithm for designing CSRE with the use of subsoil obtained from the construction site. The article's authors propose the use of artificial neural networks (ANN) to determine the proper proportions of soil, cement, and water in a CSRE mixture that provides sufficient compressive strength. The secondary purpose of the paper (supporting the main goal) is to prove that artificial neural networks are suitable for designing CSRE mixtures. For this purpose, compressive strength was tested on several hundred CSRE samples, with different particle sizes, cement content and water additions. The input database was large enough to enable the artificial neural network to produce predictions of high accuracy. The developed algorithm allows us to determine, using relatively simple soil tests, the composition of the mixture ensuring compressive strength at a level that allows the use of this material in construction.
Author Hubert Jerzy Anysz (FCE / ICE)
Hubert Jerzy Anysz,,
- The Institute of Civil Engineering
, Piotr Leon Narloch (FCE / ICE)
Piotr Leon Narloch,,
- The Institute of Civil Engineering
Journal seriesMaterials, ISSN 1996-1944
Issue year2019
Publication size in sheets1.3
Keywords in Polishmateriały budowlane
Keywords in Englishrammed earth; cement stabilized rammed earth; artificial neural networks; sustainable building material
ASJC Classification2500 General Materials Science
Languageen angielski
materials-12-01396.pdf 4.09 MB
Score (nominal)140
Score sourcejournalList
ScoreMinisterial score = 140.0, 27-07-2020, ArticleFromJournal
Publication indicators Scopus Citations = 10; WoS Citations = 1; GS Citations = 14.0; Scopus SNIP (Source Normalised Impact per Paper): 2017 = 1.285; WoS Impact Factor: 2018 = 2.972 (2) - 2018=3.532 (5)
Citation count*14 (2020-07-21)
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
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