Effect Of Desiccation On Hybrid Saw Dust Ash Treated Black Cotton Soil For Pavement Foundation: Artificial Intelligence Predictive Analysis Approach

THOMPSON FRIDAY | 16 pages (20848 words) | Theses
Civil Engineering | Co Authors: USUNGEDO

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.

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APA

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 Apr 15, 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

MLA 8th

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 15 Apr. 2024.

MLA7

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. 15 Apr. 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 >.

Chicago

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 15 Apr. 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

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