Soil characterization, CBR modeling, and spatial variability analysis for road subgrade: a case study of Danchuwa – Jajere Road, Yobe State, Nigeria

Abstract

Road construction projects require a thorough understanding of soil properties to ensure the stability and longevity of the infrastructure. This study investigates soil properties along a proposed 34 km road alignment in Yobe State, Nigeria, to characterize soil variability for road construction and develop a predictive model for California Bearing Ratio (CBR). Of the 34 soil samples analyzed, 30 were classified as A-3(1) and four as A-1(1) according to the AASHTO system. Geotechnical testing, including particle size distribution (grading percentages: gravel 0.02%–75.34%, sand 15.5%–90.88%, fines 8.92%–34.84%), Atterberg limits (liquid limits 17%–33%, plastic limits 14%–27%, plasticity index <12%), specific gravity (2.01 to 2.73), compaction (maximum dry density 1.83–2.19 Mg m−3, optimum moisture content 7.29%–14.42%), and CBR tests (values ranging from 5%–62%), were conducted. Correlation analyses revealed strong positive relationships between maximum dry density (r = 0.82) and specific gravity (r = 0.89) with CBR values. Cluster analysis segmented the samples into four distinct groups: Cluster 0 (11 samples), Cluster 1 (9 samples), Cluster 2 (5 samples), and Cluster 3 (9 samples). A linear regression model predicted CBR using maximum dry density and specific gravity (mean squared error = 9.82, R2 = 0.92). Based on CBR criteria, 8 out of 34 samples (CBR 20%–53%) satisfied subbase requirements, while none met the recommended minimum CBR of 80% for base course materials. This study enhances road construction planning through soil variability analysis, effective soil categorization via cluster analysis, and a reliable CBR prediction model. While on-site materials are unsuitable for subgrade and subbase layers, alternative materials or ground improvement techniques are recommended for the base course layer to enhance bearing capacity.