Abstract:In this research study, a constrained simplex optimization method was adapted for the evaluation of green concrete’s mechanical property constituting of two-component mixture problem of cement and saw dust ash (SDA), which is a derivative from industrial residue. This experiential research will provide avenue for the recycle and incorporation of waste materials to achieve sustainability and control indiscriminate disposal of waste. The formulated components constrained were realized from the relevant literatur… Show more
“…According to the actual and predicted response quantity, it is clear that the responses are very close together. The residual plots showed no significant deviation from normality, indicating that the chosen model effectively predicted the material’s strength and interaction 71 . Figure 4 Maximum Compressive strength ( a ), optimum compressive strength ( b ) and minimum Compressive strength ( c ) and actual and predicted ( d ) value from RSM.…”
In this study, the replacement of raw rice husk, fly ash, and hydrated lime for fine aggregate and cement was evaluated in making raw rice husk-concrete brick. This study optimizes compressive strength, water absorption, and dry density of concrete brick containing recycled aggregates via Response Surface Methodology. The optimized model's accuracy is validated through Artificial Neural Network and Multiple Linear Regression. The Artificial Neural Network model captured the 100 data's variability from RSM optimization as indicated by the high R threshold- (R > 0.9997), (R > 0.99993), (R > 0.99997). Multiple Linear Regression model captured the data's variability the decent R2 threshold confirming- (R2 > 0.9855), (R2 > 0.9768), (R2 > 0.9155). The raw rice husk-concrete brick 28-day compressive strength, water absorption, and density prediction were more accurate when using Response Surface Methodology and Artificial Neural Network compared to Multiple Linear Regression. Lower MAE and RMSE, coupled with higher R2 values, unequivocally indicate the model's superior performance. Additionally, employing sensitivity analysis, the influence of the six input parameters on outcomes was assessed. Machine learning aids efficient prediction of concrete's mechanical properties, conserving time, labor, and resources in civil engineering.
“…According to the actual and predicted response quantity, it is clear that the responses are very close together. The residual plots showed no significant deviation from normality, indicating that the chosen model effectively predicted the material’s strength and interaction 71 . Figure 4 Maximum Compressive strength ( a ), optimum compressive strength ( b ) and minimum Compressive strength ( c ) and actual and predicted ( d ) value from RSM.…”
In this study, the replacement of raw rice husk, fly ash, and hydrated lime for fine aggregate and cement was evaluated in making raw rice husk-concrete brick. This study optimizes compressive strength, water absorption, and dry density of concrete brick containing recycled aggregates via Response Surface Methodology. The optimized model's accuracy is validated through Artificial Neural Network and Multiple Linear Regression. The Artificial Neural Network model captured the 100 data's variability from RSM optimization as indicated by the high R threshold- (R > 0.9997), (R > 0.99993), (R > 0.99997). Multiple Linear Regression model captured the data's variability the decent R2 threshold confirming- (R2 > 0.9855), (R2 > 0.9768), (R2 > 0.9155). The raw rice husk-concrete brick 28-day compressive strength, water absorption, and density prediction were more accurate when using Response Surface Methodology and Artificial Neural Network compared to Multiple Linear Regression. Lower MAE and RMSE, coupled with higher R2 values, unequivocally indicate the model's superior performance. Additionally, employing sensitivity analysis, the influence of the six input parameters on outcomes was assessed. Machine learning aids efficient prediction of concrete's mechanical properties, conserving time, labor, and resources in civil engineering.
“…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 .…”
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.
“…Subsequently, the concrete samples are cured in a controlled environment at room temperature for a duration of 28 days. After this curing period, the samples are subjected to investigations aimed at assessing their structural properties 24 , 25 .…”
Due to the high costs of traditional concrete materials in Nigeria, such as river sand, there is an increasing demand to explore alternative materials like laterite for fine aggregates. Although laterite is abundant in Nigeria, its full potential in the construction industry remains untapped. Previous studies have shown that partially replacing river sand with laterite produces concrete with competitive strength properties. This research aims to validate and extend these findings, evaluating the impact of different aggregate sizes (12 mm, 20 mm, and 40 mm) on the strength of concrete with 10% and 25% laterite replacements for fine aggregate. Results revealed that as the laterite percentage increased, compressive, flexural, and split tensile strengths decreased. While 0% and 10% laterite replacements met the required strength, the mix with 25% laterite fell short. Increasing maximum coarse aggregate size led to higher strengths, with 40 mm sizes exhibiting the highest, and 12 mm the lowest. Compressive strengths ranged from 22.1 to 37.6 N/mm2, flexural strengths from 4.07 to 5.99 N/mm2 and split-tensile strengths from 2.93 to 4.30 N/mm2. This research highlights the need for meticulous mix design adjustments when using laterite, balancing workability with strength objectives. The developed regression models offer a valuable tool for predicting concrete properties based on mix parameters, providing insights for optimizing laterized concrete designs across diverse construction applications and supporting sustainable building practices.
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