Multivariate regression models for predicting the compressive strength of bone ash stabilized lateritic soil for sustainable building

Journal article


Jin, R. (2020). Multivariate regression models for predicting the compressive strength of bone ash stabilized lateritic soil for sustainable building. Construction and Building Materials. 263, p. 120677. https://doi.org/10.1016/j.conbuildmat.2020.120677
AuthorsJin, R.
Abstract

Substantial amount of time, financial resources and energy are required to obtain experimental data on the compressive strength of building components. Application of multivariate regression models in predicting the strength of stabilized lateritic soil has the potential of reducing the time and cost of construction projects in Nigeria. This study is aimed at developing multivariate models for predicting the strength of lateritic soils stabilized with bone ash for sustainable construction. Different percentages of bone ash were used to stabilize lateritic soil. The lateritic brick samples obtained were cured at different temperatures and ages. The samples were tested to obtain their compressive strength. Multivariate and non-linear models were used to predict the compressive strength of the lateritic bricks. A mixed model with R2 of 97% was identified as the best-fit statistical model. The usefulness of the models is limited to the range of variables used but for a more universal application, wider ranges of variables can be explored. This model has practical application for the prediction of compressive strength of stabilized lateritic soil for sustainable housing construction in Nigeria.

KeywordsCementitious materials; bone ash; compressive strength; lateritic soil; modelling
Year2020
JournalConstruction and Building Materials
Journal citation263, p. 120677
PublisherElsevier BV
ISSN0950-0618
Digital Object Identifier (DOI)https://doi.org/10.1016/j.conbuildmat.2020.120677
Web address (URL)https://www.sciencedirect.com/science/article/abs/pii/S0950061820326829
Publication dates
PrintDec 2020
Online04 Sep 2020
Publication process dates
Accepted18 Aug 2020
Deposited15 Sep 2020
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