ABSTRACT
Artificial
Intelligence (AI)-based bi-input predictive models have been executed to
forecast the bulk density, linear and volumetric shrinkages and desiccation
cracking of HSDA-treated black cotton soil (BCS) for sustainable sub-grade
construction purposes. The BCS was characterized and classified as A-7 group
soil with high plasticity and poorly graded condition. Sawdust ash was obtained
by combusting sawdust and sieving through 2.35 mm aperture sieve. It was
further activated by blending it with pre-formulated activator material (a
blend of 8M NaOH solution and NaSiO2 in 1:1 ratio) to derive HSDA.
The HSDA was further used in wt % of 3, 6, 9, and 12 to treat the BCS. The
treated samples were compacted in the standard proctor moulds, cured for 24
hours and extruded. The desiccation tests were then performed on the prepared
specimens by drying them at a temp of 102°C for 30 days and behavioural changes
in weight, height, diameter, average crack development, etc. were taken
throughout the period. Multiple data sets were collected for the references
test, and treated specimens of 3, 6, 9, and 12% wt HSDA of the soil for 30
drying days. XRF, XRD and SEM tests were also conducted to determine the
pozzolanic strength via the chemical oxide composition, three chemical moduli
(TCM) and the micro structural arrangement of the experimental materials and
the treated BCS. The XRF tests showed that the experimental materials had less
pozzolanic strength, which improved with the treated blends thereby forming
stabilized mass of BCS. Also, it showed the silica moduli of the TCM dominated
the stabilization of the soil with HSDA. SEM tests showed increased formation
of ettringite and gels with the addition of the HSDA. The data collected was
subjected to MLR analysis for the four outcomes, BD, CW, LS and VS of the
HSDA-treated BCS. The MLR performed with an accuracy of 69% for BD, 75% for CW,
95% for LS and 96% for VS. The addition
of the various percentages of admixture (HSDA) improved the measured
parameters as compared to the control soil thereby improving
the stability of the soil.
THOMPSON, F (2023). Effect Of Desiccation On Hybrid Saw Dust Ash Treated Black Cotton Soil For Pavement Foundation: Artificial Intelligence Predictive Analysis Approach. Repository.mouau.edu.ng: Retrieved Oct 30, 2024, from https://repository.mouau.edu.ng/work/view/effect-of-desiccation-on-hybrid-saw-dust-ash-treated-black-cotton-soil-for-pavement-foundation-artificial-intelligence-predictive-analysis-approach-7-2
FRIDAY, THOMPSON. "Effect Of Desiccation On Hybrid Saw Dust Ash Treated Black Cotton Soil For Pavement Foundation: Artificial Intelligence Predictive Analysis Approach" Repository.mouau.edu.ng. Repository.mouau.edu.ng, 15 May. 2023, https://repository.mouau.edu.ng/work/view/effect-of-desiccation-on-hybrid-saw-dust-ash-treated-black-cotton-soil-for-pavement-foundation-artificial-intelligence-predictive-analysis-approach-7-2. Accessed 30 Oct. 2024.
FRIDAY, THOMPSON. "Effect Of Desiccation On Hybrid Saw Dust Ash Treated Black Cotton Soil For Pavement Foundation: Artificial Intelligence Predictive Analysis Approach". Repository.mouau.edu.ng, Repository.mouau.edu.ng, 15 May. 2023. Web. 30 Oct. 2024. < https://repository.mouau.edu.ng/work/view/effect-of-desiccation-on-hybrid-saw-dust-ash-treated-black-cotton-soil-for-pavement-foundation-artificial-intelligence-predictive-analysis-approach-7-2 >.
FRIDAY, THOMPSON. "Effect Of Desiccation On Hybrid Saw Dust Ash Treated Black Cotton Soil For Pavement Foundation: Artificial Intelligence Predictive Analysis Approach" Repository.mouau.edu.ng (2023). Accessed 30 Oct. 2024. https://repository.mouau.edu.ng/work/view/effect-of-desiccation-on-hybrid-saw-dust-ash-treated-black-cotton-soil-for-pavement-foundation-artificial-intelligence-predictive-analysis-approach-7-2