Scheffe’s Simplex Optimization of Flexural Strength of Quarry Dust and Sawdust Ash Pervious Concrete for Sustainable Pavement Construction

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Cassava peel ash (CPA) is an abundant agricultural byproduct that has shown promise as an additional cementitious material in concrete manufacturing. This research study aims to optimize the incorporation of CPA in concrete blends using the central composite design (CCD) methodology to determine the most effective combination of ingredients for maximizing concrete performance. The investigation involves a physicochemical analysis of CPA to assess its pozzolanic characteristics. Laboratory experiments are then conducted to assess the compressive and flexural strengths of concrete mixtures formulated with varying proportions of CPA, cement, and aggregates. The results show that a mix ratio of 0.2:0.0875:0.3625:0.4625 for cement, CPA, fine, and coarse aggregates, respectively, yields a maximum compressive strength of 28.51 MPa. Additionally, a maximum flexural strength of 10.36 MPa is achieved with a mix ratio of 0.2:0.0875:0.3625:0.525. The experimental data were used to develop quadratic predictive models, followed by statistical analyses. The culmination of the research resulted in the identification of an optimal concrete blend that significantly enhances both compressive and flexural strength. To ensure the reliability of the model, rigorous validation was conducted using student’s t-test, revealing a strong correlation between laboratory findings and simulated values, with computed p-values of 0.9987 and 0.9912 for compressive and flexural strength responses, respectively. This study underscores the potential for enhancing concrete properties and reducing waste through the effective utilization of CPA in the construction sector.

Section: Results Discussion and Analysismentioning

confidence: 84%

“…After twenty-eight days of curing, three samples from each experimental run will be subjected to testing, and the average flexural strength will be determined. This process will be repeated for each mix proportion, testing three specimens per proportion and calculating the average flexural strength for each 40 .…”

Section: Methodsmentioning

confidence: 99%

Optimization of cassava peel ash concrete using central composite design method

Iro,

Alaneme,

Attah

et al. 2024

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“…Silicon (Si), prevailing predominantly in the mixture with an atomic concentration of 58.05, emanates from both the GBFS component and potentially other constituents. This substantial presence notably shapes the composite’s structural behaviour, playing a pivotal role in contributing to overall mechanical properties 65 , 66 . Similarly, Aluminium (Al) holds a significant atomic concentration of 12.76, underscoring its substantial involvement in the composite.…”

Section: Resultsmentioning

confidence: 99%

Compaction and compressibility characteristics of snail shell ash and granulated blast furnace slag stabilized local bentonite for baseliner of landfill

Fadugba,

Adeniran,

Alaneme

et al. 2024

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This study comprehensively explores the compaction and compressibility characteristics of snail shell ash (SSA) and ground-granulated blast-furnace slag (GBFS) in stabilizing local bentonite for landfill baseliner applications. The untreated soil, with a liquid limit of 65%, plastic limit of 35%, and plasticity index of 30%, exhibited optimal compaction at a moisture content of 32% and a maximum dry density of 1423 kg/m3. SSA revealed a dominant presence of 91.551 wt% CaO, while GBFS contained substantial 53.023 wt% SiO2. Treated samples with 20% GBFS and 5% SSA exhibited the highest maximum dry density (1561 kg/m3) and optimal moisture content (13%), surpassing other mixtures. The 15% SSA-treated sample demonstrated superior strength enhancement, reaching an unconfined compressive strength of 272.61 kPa over 28 days, while the 10% GBFS-treated sample achieved 229.95 kPa. The combination of 15% SSA exhibited the highest shear strength (49 kPa) and elastic modulus (142 MPa), showcasing robust mechanical properties. Additionally, the 15% SSA sample displayed favourable hydraulic conductivity (5.57 × 10–8 cm/s), outperforming other mixtures. Notably, the permeability test, a critical aspect of the study, was meticulously conducted in triplicate, ensuring the reliability and reproducibility of the reported hydraulic conductivity values. Treated samples with SSA and GBFS showed reduced compressibility compared to the control soil, with the 15% SSA-treated sample exhibiting a more consistent response to applied pressures. Scanning Electron Microscopy analysis revealed substantial composition changes in the 15% SSA mixture, suggesting its potential as an effective base liner in landfill systems. In conclusion, the 15% SSA sample demonstrated superior mechanical properties and hydraulic conductivity, presenting a promising choice for landfill liner applications.

“…To compare heavy metals' concentration levels between samples within and outside the clusters (control), an analysis of variance (ANOVA) statistical is conducted for soil obtained from 0–30 cm and 30–60 cm depth. This involves collecting data from soil samples within and outside the clusters, defining two groups, formulating hypotheses, selecting the appropriate ANOVA model, calculating the F-statistic and p-value, and interpreting the results 82 , 83 . This analysis helps determine if there's a significant difference in heavy metals' concentrations between the two sample groups, providing valuable insights for environmental monitoring and management as shown in Tables 8 and 9 .…”

Section: Resultsmentioning

confidence: 99%

Spatial variability of heavy metals concentrations in soil of auto-mechanic workshop clusters in Nsukka, Nigeria

Duru,

Echiegu,

Anyadike

et al. 2024

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The indiscriminate disposal of spent engine oils and other hazardous waste at auto mechanic workshops clusters in Nsukka, Enugu State, Nigeria is an environmental concern. This study examines the concentration of heavy metals in the soil inside the workshop cluster and in the unpolluted soil outside the workshop cluster at approximately 100 m. Ten sampling points were randomly selected from within the cluster and another ten from outside the cluster. Using a hand-held Global Positioning System, the coordinates of the selected points were established and used to create a digital map. Soil samples at depths of 0–30 cm and 30–60 cm, were analyzed for Cu, Fe, Zn, Pb, As and Cd using Spectrophotometer. Moisture content determination and particle size analysis were also done on the samples. Spatial variability of heavy metals concentrations of the studied site was also mapped with ArcGIS 10.2.2 using interpolation methods. Results showed that the soil ranged from sandy loam to sandy clay loam. Cadmium and Zinc had the lowest and highest concentration, respectively, in the studied area. Comparing the concentrations of heavy metals in soils within and outside the auto mechanic cluster revealed notable differences across various depths (0–30 cm and 30–60 cm). The analysis results for soil samples within the cluster exhibited concentration levels (mg/kg) ranging from 0.716–0.751 (Cu), 2.981–3.327 (Fe), 23.464–30.113 (Zn), 1.115–1.21 (Pb), 2.6–2.912 (As), and 0.133–0.365 (Cd) demonstrating a variation pattern in the order of Zn > Fe > As > Pb > Cu > Cd. Conversely, for soil samples outside the cluster, concentration levels (mg/kg) ranged from 0.611–0.618 (Cu), 2.233–2.516 (Fe), 12.841–15.736 (Zn), 0.887–0.903 (Pb), 1.669–1.911 (As), and 0.091–0.091 (Cd). To assess the disparity in heavy metal concentration levels between samples collected within and outside the clusters, ANOVA test was performed. The test showed significant difference in heavy metal concentrations between samples within and outside the auto mechanic cluster (p < 0.05), implying auto mechanic activities significantly impact heavy metal levels within the cluster compared to outside areas. The assessment of soil pollution utilized indices including the Geo-accumulation Index (Igeo), Contamination factor (Cf), and anthropogenic metal concentration (QoC). Zinc, Cadmium, and Arsenic showed the highest contamination factors, indicating significant soil contamination likely due to anthropogenic activities. The concentrations of the metals analyzed were within WHO permissible limits while the metals concentrations were also observed to decrease as depth was increased. Using ArcGIS 10.2.2, spatial maps showing heavy metal distribution were developed, with the Kriging method proving superior. This study suggests that heavy metal levels in the soil at the area be monitored on a regular basis.