Ukpai, J., Okonkwo, U. (2025). Using artificial neural networks to predict the compressive strength of cement and sawdust ash-treated lateritic soil. , 43(5), 374-385. doi: 10.30684/etj.2025.156269.1873
James O. Ukpai; Ugochukwu N. Okonkwo. "Using artificial neural networks to predict the compressive strength of cement and sawdust ash-treated lateritic soil". , 43, 5, 2025, 374-385. doi: 10.30684/etj.2025.156269.1873
Ukpai, J., Okonkwo, U. (2025). 'Using artificial neural networks to predict the compressive strength of cement and sawdust ash-treated lateritic soil', , 43(5), pp. 374-385. doi: 10.30684/etj.2025.156269.1873
Ukpai, J., Okonkwo, U. Using artificial neural networks to predict the compressive strength of cement and sawdust ash-treated lateritic soil. , 2025; 43(5): 374-385. doi: 10.30684/etj.2025.156269.1873

Using artificial neural networks to predict the compressive strength of cement and sawdust ash-treated lateritic soil

Engineering and Technology Journal
Volume 43, Issue 5, May 2025, Pages 374-385    PDF (625.88 K)
Document Type: Research Paper
DOI: 10.30684/etj.2025.156269.1873
Authors
James O. Ukpai; Ugochukwu N. Okonkwo*
Civil Engineering Dept., Michael Okpara University of Agriculture Umudike, Abia State, Nigeria.
Abstract
Sawdust industrial residue could be hazardous to the environment, but it becomes pozzolanic when incinerated. Thus, harnessing sawdust ash (SDA) and lateritic soil as road construction materials for low-cost roads is apt. However, modeling and prediction of the properties of soils treated with SDA have received very little attention. This study predicted the compressive strength of lateritic soil treated with cement and SDA using an Artificial Neural Network (ANN). The soil was found to belong to A-2-4(0) in the AASHTO rating and clayey sand (SC) in the Unified Soil Classification System. The soil minerals composition was conducted using an x-ray diffraction technique, which comprises non-clay minerals such as quartz, albite, orthoclase, goethite, and muscovite, as well as clay minerals like clinochlore. The tests carried out on the treated soil were compaction tests, California Bearing Ratio (CBR), unconfined compressive strength (UCS), and durability tests. The maximum dry density of the reinforced soil was reduced, whereas the optimum moisture content increased with an increase in SDA content. At the addition of cement and SDA of up to 8% and 30%, respectively, the strength properties like the CBR and UCS at 7 days of curing increased by up to 1965.46% and 989.25%, respectively. Also, the reinforced soil satisfied the durability requirements. The treated soil could be used as the sub-base of road pavement structures. The ANN modeling dependably predicted the unconfined compressive strength at 7 days of curing of the reinforced soil with coefficients of correlation of 0.989 and 0.996 for training and testing, respectively.

Highlights
  • Machine learning (ANN) predicted the compressive strength of lateritic soil treated with cement and sawdust ash.
  • Maximum compressive strength of 1345.66 kN/m² was achieved with 8% cement and 30% sawdust ash treatment.
  • The machine learning model showed high accuracy and effectively handled non-linearity in predictions.
  • At 7 days, CBR and UCS rose by up to 1965.46% and 989.25% with 8% cement and 30% SDA additions respectively.
Keywords
Cement; Compressive strength; Machine learning; Lateritic soil and Sawdust ash
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