Cogent Engineering (Dec 2022)

Geotechnical investigation and statistical relationship of subsoil properties derived from two parent rock types in Southwestern Nigeria

  • Temitayo Olamide Ale,
  • Tolulope Henry Ogunribido,
  • Modupe Anne Kolawole,
  • Taiwo Ayomide Ale

DOI
https://doi.org/10.1080/23311916.2022.2132654
Journal volume & issue
Vol. 9, no. 1

Abstract

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This study is aimed at using geotechnical properties and statistical relationship between geotechnical parameters to evaluate the performance of subsoil derived two different rock types for pre- and post-engineering construction planning and design of roads. Twelve soil samples underlain by two rock types (granite gneiss & grey gneiss) were taken at the depth of 1 m for analysis. The geotechnical properties of these soils were tested. The atterberg limit, grain size analysis, California bearing ratio (CBR) and compaction tests of both soils types met the Nigeria specifications of good subgrade material. Soil derived from grey gneiss has better ratings in linear shrinkage, grain size analysis, specific gravity, compaction and CBR tests. Twelve parameters of the soil samples were correlated against each other and sixty-six values of correlation coefficient were recorded for each rock type. Soil derived from grey gneiss has more positive pairwise Pearson’s (r) correlation (37) than the soil derived from granite gneiss (34). Granite gneiss derived soil has better ratings in thirty-three (33) out of sixty-six (66) Pearson’s correlation (r) values to thirty-two (32) of grey gneiss with one of the variables correlation having the same value. There exist five strong positive and five strong negative correlation variables that are common to the two rock types’ derived soils. The Pearson’s correlation coefficient (r) values that are within the critical (r) value are negligible. For regression analysis, granite gneiss derived soil has better ratings than the grey gneiss derived soil with both rock types derived soils having these two pairwise correlation variables (GRV-SAND and OMC-MDD) for excellent prediction.

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