Article Sidebar
Published:
Jan 15, 2018DOI:
10.4314/njt.v36i4.10Keywords:
Article Details
Copyright belongs to the Faculty of Engineering, University of Nigeria, Nsukka, Nigeria
The contents of the articles are the sole opinion of the author(s) and not of NIJOTECH.
NIJOTECH allows open access for distribution of the published articles in any media so long as whole (not part) of articles are distributed.
A copyright and statement of originality documents will need to be filled out clearly and signed prior to publication of an accepted article. The Copyright form can be downloaded from https://www.nijotech.com/downloads/COPYRIGHT%20FORM.pdf while the Statement of Originality is in Statement of Originality.pdf (nijotech.com)
For articles that were developed from funded research, a clear acknowledgement of such support should be mentioned in the article with relevant references. Authors are expected to provide complete information on the sponsorship and intellectual property rights of the article together with all exceptions.
It is forbidden to publish the same research report in more than one journal.
Main Article Content
Application of multilinear regression analysis in modeling of soil properties for geotechnical civil engineering works in Calabar South
Abstract
The application of Multi-Linear Regression Analysis (MLRA) model for predicting soil properties in Calabar South offers a technical guide and solution in foundation designs problems in the area. Forty-five soil samples were collected from fifteen different boreholes at a different depth and 270 tests were carried out for CBR, MC, SG, LL, PL test and GS with mechanical sieve analysis of sizes, 2.36 mm, 1.18 mm, 600 μ, 425 μ, 300μ, 212μ, 150μ, and 75μ. This study uses Multi Linear Regression Analysis to formulate a model that relates CBR to other soil parameters. The Multi Linear Regression Analysis was done using the Data Analysis tool of Microsoft Excel, which gave a good coefficient of correlation R2, 0.9454 implying that the model can be used to predict CBR at 50.9 % and with ±3.4% error. This conclusion is drawn since the value of R2 increases with increase in numbers of variables.
Keywords: Calabar, CBR, Model, Regression, Soil properties, Soil Samples
