Proposed simplified methodological approach for designing geopolymer concrete mixtures.

The development of geopolymer concrete offers promising prospects for sustainable construction practices due to its reduced environmental impact compared to conventional Portland cement concrete. However, the complexity involved in geopolymer concrete mix design often poses challenges for engineers and practitioners. In response, this study proposes a simplified approach for designing geopolymer concrete mixtures, drawing upon principles from Portland cement concrete mix design standards and recommended molar ratios of oxides involved in geopolymer synthesis. The proposed methodology aims to streamline the mix design process while optimizing key factors such as chemical composition, alkali activation solution, water content, and curing conditions to achieve desired compressive strength and workability. By leveraging commonalities between Portland cement concrete and geopolymer concrete, this approach seeks to facilitate the adoption of geopolymer concrete in practical construction applications. The proposed mix design guidelines have been validated through examples for concrete cured under different conditions, including outdoor and oven curing. Future research should focus on validating the proposed methodology through experimental studies and exploring cost-effective alternatives for alkali activation solutions to enhance the feasibility and scalability of geopolymer concrete production. Overall, the proposed simplified approach holds promise for advancing the utilization of geopolymer concrete as a sustainable alternative in the construction industry.

Optimization of cassava peel ash concrete using central composite design method.

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.

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

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.

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

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.

Effects of aggregate sizes on the performance of laterized concrete.

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.

A study on waste PCB fibres reinforced concrete with and without silica fume made from electronic waste.

This research goal is to appraise the effect of electronic waste on concrete properties by examining the mechanical properties of concrete reinforced with waste printed circuit boards (PCBs). PCB fibres, each 50 mm long, were mixed in varying proportions (1-5% by weight of cement). Silica fume (SF) was used as a 12% weight replacement for cement to conserve the properties of PCB fibre-reinforced concrete while tumbling cement consumption. Following a 28-day curing period, the fresh and hardened characteristics of PCB fibre-reinforced concrete were juxtaposed with those of conventional concrete. The experimental results led to the conclusion that 5% by weight of cement is the most effective proportion of PCB fibres to include in both PCB fibre-reinforced concrete and silica fume-modified PCB fibre-reinforced concrete. The addition of PCB fibres and silica fume significantly increased the mechanical strength of the concrete, making it suitable for high-strength concrete applications. Based on a similar investigational research design, an artificial neural network model was created, and it played a critical role in predicting the mechanical properties of the concrete. The model produced accurate results, with an R-squared (R2) value greater than 0.99.

Effects of elevated temperatures on the mechanical properties of laterized concrete.

This study explored the impact of elevated temperatures on the residual structural properties of concrete made with a non-conventional fine aggregate such as laterite and quarry dust. In regions prone to high temperatures, such as tropical climates, the structural integrity of concrete can be compromised when exposed to elevated temperatures. Concrete samples were subjected to high temperatures (250 °C) and compared with control samples tested under normal conditions. In this research, the concrete mix was altered by replacing fine aggregates with different combinations of laterite (Lat) and quarry dust (QD) at varying percentages: 10%Lat:90%QD, 25%Lat:75%QD, 90%Lat:10%QD, 75%Lat:25%QD, and 50%Lat:50%QD. The physical properties of the constituent aggregates, including sand, laterite, quarry dust, and granite, were assessed, and an experimental mix was designed. The concrete samples underwent curing for 3, 7, 14, and 28 days, and their mechanical properties, specifically compression and flexural strength, were analyzed. The results demonstrated that as the percentage of laterite in the concrete matrix increased, there was a linear improvement in performance in terms of density, sorptivity, and strength gain. The maximum compressive strength reached 32.80 N/mm2 at 90% laterite replacement. However, flexural strength showed a different response, with the highest strength of 5.99 N/mm2 observed at 50% laterite replacement, after which strength declined with further increases in the laterite ratio. For economic and engineering considerations, it is recommended to use 25% laterite replacement with sand to produce grade 30 concrete, while 50% laterite replacement is suitable for grade-25 concrete. Importantly, the study found that a temperature of 250 °C did not significantly affect concrete strength, with changes of no more than 5%, which is consistent with expectations for conventional concrete. Furthermore, this research suggests that an optimal laterite replacement range of 25-50% should be considered when using laterite in concrete production.

Recycling and sustainable applications of waste printed circuit board in concrete application and validation using response surface methodology.

The present investigation aims to examine the mechanical and durability properties of concrete that has been reinforced with a waste printed circuit board (WPCB) towards a low-carbon built environment. It assessed the fresh and hardened characteristics of the low-carbon concrete reinforced with WPCB fibres, after a curing period of 7 and 28 days. The evaluation was done by quantifying slump, compressive strength, split tensile strength, flexural strength, sorptivity, rapid, and acid tests. It further analysed eleven discrete concrete mixes with WPCB fibres at a weight percentage ranging from 1 to 5% in the cement mixture. The results indicate that incorporating WPCB fibre into concrete improves its mechanical strength. The results revealed that incorporating 5% WPCB fibre yielded the most favourable outcomes. The properties of WPCB fibre-reinforced concrete have been theoretically validated through Response Surface Methodology (RSM), which employs various statistical and mathematical tools to analyse the experimental data. The results derived from RSM were compared with the experimental results. It was found that the RSM model demonstrated a high level of accuracy (R2 ≥ 0.98) in validating the mechanical properties of WPCB fibre concrete. The statistical model exhibited no indication of prediction bias and demonstrated a statistically significant outcome, with a p-value below 0.5.

Experimental investigation and modelling of the mechanical properties of palm oil fuel ash concrete using Scheffe's method.

This study explores the enhancement of mechanical properties in concrete blended with palm oil fuel ash (POFA) through Scheffe's optimization. The utilization of POFA as supplementary cementitious material in concrete has gained attention for its potential environmental benefits. Utilizing a (5,2) simplex-lattice design, a systematic approach is employed for optimizing mixture proportions based on response parameters. The laboratory tests to evaluate concrete's mechanical behavior were conducted using the computed mixture ratios from the design experimental points after 28 days of hydration. The results showed maximum flexural strength at 8.84 N/mm2 and compressive strength at 31.16 N/mm2, achieved with a mix of 0.65:0.54:2.3:3.96:0.35 for cement, water, coarse aggregate, fine aggregate, and POFA. Additionally, maximum splitting tensile strength reached 8.84 N/mm2 with a mix of 0.62:0.55:2.09:3.86:0.38 for the same components. Conversely, the minimum flexural, splitting tensile and compressive strength within the experimental factor space was 4.25, 2.08 and 19.82 N/mm2 respectively. The results obtained indicated a satisfactory mechanical strength performance at POFA replacement of 35 percent in the concrete mixture. The developed mathematical model was statistically validated using analysis of variance (ANOVA) at a 95% confidence interval which showed satisfactory prediction performance. The findings from this study provide valuable insights into optimizing POFA-blended concrete for enhanced mechanical performance, offering potential sustainable solutions for the construction industry.

Soft computing techniques for predicting the properties of raw rice husk concrete bricks using regression-based machine learning approaches.

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.

Application of neural networks and neuro-fuzzy models in construction scheduling.

Construction scheduling is a complex process that involves a large number of variables, making it difficult to develop accurate and efficient schedules. Traditional scheduling techniques rely on manual analysis and intuition, which are prone to errors and often fail to account for all the variables involved. This results in project delays, cost overruns, and poor project performance. Artificial intelligence models have shown promise in improving construction scheduling accuracy by incorporating historical data, site-specific conditions, and other variables that traditional scheduling methods may not consider. In this research study, application of soft-computing techniques to evaluate construction schedule and control of project activities in order to achieve optimal performance in execution of building projects were carried out. Artificial neural network and neuro-fuzzy models were developed using data extracted from a residential two-storey reinforced concrete framed-structure construction schedule and project execution documents. The evaluation of project performance indicators in earned value analysis from 0 to 100% progress at 5% increment with a total of seventeen tasks were carried out using Microsoft Project software and data obtained from the computation were utilized for model development. Using input-output and curve-fitting (nftool) function in MATLAB, a 6-10-1 two-layer feed-forward network with tansig activation-function (AF) for the hidden neurons and linear AF output neurons was generated with Levenberg-Marquardt (Trainlm) training algorithm. Similarly, with the aid of ANFIS toolbox in MATLAB software, the training, testing and validation of the ANFIS model were carried out using hybrid optimization learning algorithm at 100 epochs and the Gaussian-membership-function (gaussmf). Loss-function parameters namely MAE, RMSE and R-values were taken as the performance evaluation criteria of the developed models. The generated statistical results indicates no significant difference between model-results and experimental values with MAE, RMSE, R2 of 1.9815, 2.256 and 99.9% respectively for ANFIS-model and MAE, RMSE, R2 of 2.146, 2.4095 and 99.998% respectively for the ANN-model. The model performance indicated that the ANFIS-model outclassed the ANN-model with their results satisfactory to deal with complex relationships between the model variables to produce accurate target response. The findings from this research study will improve the accuracy of construction scheduling, resulting in improved project performance and reduced costs.

Role of extreme vertex design approach on the mechanical and morphological behaviour of residual soil composite.

This research work reports the usability of binary additive materials known as tile waste dust (TWD) and calcined kaolin (CK) in ameliorating the mechanical response of weak soil. The extreme vertex design (EVD) was adopted for the mixture experimental design and modelling of the mechanical properties of the soil-TWD-CK blend. In the course of this study, a total of fifteen (15) design mixture ingredients' ratios for water, TWD, CK and soil were formulated. The key mechanical parameters considered in the study showed a considerable rate of improvement to the peak of 42%, 755 kN/m2 and 59% for California bearing ratio, unconfined compressive strength and resistance to loss in strength respectively. The development of EVD-model was achieved with the aid of the experimental derived results and fractions of component combinations through fits statistical evaluation, analysis of variance, diagnostic test, influence statistics and numerical optimization using desirability function to analyze the datasets. In a step further, the non-destructive test explored to assess the microstructural arrangement of the studied soil-additive materials displayed a substantial disparity compared to the corresponding original soil material and this is an indicator of soil improvement. From the geotechnical engineering perspective, this study elucidates the usability of waste residues as environmental friendly and sustainable materials in the field of soil re-engineering.

Mathematical modeling and numerical simulation technique for selected heavy metal transport in MSW dumpsite.

The study focused on development of mathematical modeling and numerical simulation technique for selected heavy metal transport in Uyo municipal solid waste dumpsite in Akwa Ibom State to investigate the level in depth to which leachate from the dumpsite extends and the quantity of leachate at various depth of the dumpsite soil. Uyo waste dumpsite is operating open dumping system where provisions are not made for preservation and conservation of soil and water quality, hence, the need for this study. Three monitoring pits within Uyo waste dumpsite were constructed and infiltration runs were measured, and soil samples were collected beside infiltration points from nine designated depths ranging from 0 to 0.9 m for modeling heavy metal transport in the soil. Data collected were subjected to descriptive and inferential statistics while the COMSOL Multiphysics software 6.0 was used to simulate the movement of pollutants in the soil. It was observed that heavy metal contaminant transport in soil of the study area is in the power functional form. The transport of heavy metals in the dumpsite can be described by a power model from linear regression and a numerical model based on finite element. Their validation equations showed that the predicted and the observed concentrations yielded a very high R2 value of over 95%. The power model and the COMSOL finite element model show very strong correlation for all selected heavy metals. Findings from the study has identified level in depth to which leachate from the dumpsite extends and the quantity of leachate at various depth of the dumpsite soil which can be accurately predicted using leachate transport model of this study.

Evaluation of sisal fiber and aluminum waste concrete blend for sustainable construction using adaptive neuro-fuzzy inference system.

This research study presents evaluation of aluminum waste-sisal fiber concrete's mechanical properties using adaptive neuro-fuzzy inference system (ANFIS) to achieve sustainable and eco-efficient engineering works. The deployment of artificial intelligence (AI) tools enables the optimization of building materials combined with admixtures to create durable engineering designs and eliminate the drawbacks encountered in trial-and-error or empirical method. The features of the cement-AW blend's setting time were evaluated in the laboratory and the results revealed that 0-50% of aluminum-waste (AW) inclusion increased both the initial and final setting time from 51-165 min and 585-795 min respectively. The blended concrete mix's flexural strength tests also show that 10% sisal-fiber (SF) substitution results in a maximum flexural strength of 11.6N/mm2, while 50% replacement results in a minimum flexural strength of 4.11N/mm2. Moreover, compressive strength test results show that SF and AW replacements of 0.08% and 0.1%, respectively, resulted in peak outcome of 24.97N/mm2, while replacements of 0.5% and 0.45% resulted in a minimum response of 17.02N/mm2. The ANFIS-model was developed using 91 datasets obtained from the experimental findings on varying replacements of cement and fine-aggregates with AW and SF respectively ranging from 0 to 50%. The ANFIS computation toolbox in MATLAB software was adopted for the model simulation, testing, training and validation of the response function using hybrid method of optimization and grid partition method of FIS at 100 Epochs. The compressive strength behavior is the target response, and the mixture variations of cement-AW and fine aggregates-SF combinations were used as the independent variables. The ANFIS-model performance assessment results obtained using loss function criteria demonstrates MAE of 0.1318, RMSE of 0.412, and coefficient of determination value of 99.57% which indicates a good relationship between the predicted and actual results while multiple linear regression (MLR) model presents a coefficient of determination of 82.46%.

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

Pervious concrete provides a tailored surface course with high permeability properties which permit the easy flow of water through a larger interconnected porous structure to prevent flooding hazards. This paper reports the modeling of the flexural properties of quarry dust (QD) and sawdust ash (SDA) blended green pervious concrete for sustainable road pavement construction using Scheffe's (5,2) optimization approach. The simplex mixture design method was adapted to formulate the mixture proportion to eliminate the set-backs encountered in empirical or trials and the error design approach, which consume more time and resources to design with experimental runs required to evaluate the response function. For the laboratory evaluation exercise, a maximum flexural strength of 3.703 N/mm2 was obtained with a mix proportion of 0.435:0.95:0.1:1.55:0.05 for water, cement, QD, coarse aggregate and SDA, respectively. Moreover, the minimal flexural strength response of 2.504 N/mm2 was obtained with a mix ratio of 0.6:0.75:0.3:4.1:0.25 for water, cement, QD, coarse aggregate and SDA, respectively. The test of the appropriateness of the developed model was statistically verified using the Student' t-test and an analysis of variance (ANOVA), and was confirmed to be acceptable based on computational outcomes at the 95% confidence interval. Furthermore, the scanning electron microscopy (SEM) and energy dispersive X-ray (EDX) were used to evaluate the morphological and mineralogical behavior of green prior concrete samples with various additive mixture compositions. The addition of QD and SDA, on the other hand, aided the creation of porous microstructures in the concrete matrix due to fabric changes in the concrete mixture, potentially aided by the formation of cementitious compounds such as calcium aluminate hydrate and calcium silicate hydrate.