Welcome to NIJOTECH Homepage The Nigerian Journal of Technology is an international, multi-disciplinary and inter-disciplinary journal that publishes manuscripts that have application and relevance in Engineering. Specifically, we publish research articles in the following areas: Building, Civil and Geotechnical Engineering Chemical, Industrial, Petroleum and Production Engineering Mechanical, Materials and Metallurgical Engineering, Computer, Telecommunications, Software, Electrical and Electronics Engineering Agricultural, Bioresources, Biomedical and Food Engineering Water Resources and Environmental Engineering General and Emerging Engineering The journal has been publishing technical papers since 1975. The copyright owner of the journal is Faculty of Engineering, University of Nigeria, Nsukka. p-ISSN:0331-8443 | e-ISSN: 2467-8821 Quarterly Publications: MARCH, JUNE, SEPTEMBER and DECEMBER
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OPTIMIZATION OF AGRO-WASTE REINFORCED ALUMINIUM – BASED COMPOSITE USING D-OPTIMAL MIXTURE DESIGN
All designers desire to use durable and reliable materials in engineering applications. In this study, a new aluminium - based composite material was produced using some agro waste materials which are Palm Kernel Shell, Bamboo Fibre, Groundnut Shell and Rice Husk as the reinforcement while aluminium scrap is the matrix. The D-Optimal mixture design was used for the experimental design. Twenty – five (25) experimental runs of the specimens were obtained from the design expert and each run was performed three times and the average was taken. A stir casting method was used in the production of the specimens. The responses tested for are Tensile strength, hardness and wear index(rate). The Numerical optimization of the input and responses was done using the design expert. The produced material was modelled using SOLIDWORKS software. SEM analysis was carried out to appropriately look at the microstructure of the composite and it was found out that the porosity seen was as a result of the casting method. It was found out that the reinforcement particles of Palm Kernel Shell, Bamboo Fibre, Groundnut Shell and Rice Husk have improved the mechanical properties of the aluminium metal. From the optimal solution obtained by the design expert, 72.43% weight of aluminium, 13.27% weight of Bamboo fibre, 12.30% weight of Rice Husk, 1.00% weight of Groundnut Shell and 1.00% weight of Palm kernel shell can be used to produce an Engineering composite material with a tensile strength of 241.23 MPa, hardness of 114.07 HV and wear index(rate) of 0.18 x 10-3 mg/rpm with a desirability of 88.3% which is applicable in the production of automobile components.
Chemical, Industrial, Materials, Mechanical, Metallurgical, Petroleum & Production Engineering
Agro Waste Composite, Tensile Strength, Wear Rate, Hardness, Optimization
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THE SYNGENETIC INCORPORATION OF ZINC IN THE LATTICE OF TUNGSTEN MOLYBDATE (ZNXWMO3) FOR POTENTIAL PHOTOVOLTAIC APPLICATION
This study highlights Zn-doped tungsten-molybdenum oxide (WMo₃) as a significant and promising new material in photovoltaics. The global demand for renewable energy is pushing the development of affordable, reliable, and high-performing materials. Films were deposited using a three-electrode electrochemical technique. The films' photovoltaic parameters were characterized. Fill factor measurements showed that the solar cell's quality improved from 51% (WMo₃) to 59% (Zn₀.₀₃WMo₃). In other words, increased fill factors lead to improved solar cell efficiency and reduced voltage drops in circuits. A significant efficiency improvement is observed—a rise from 1.8% in WMo₃ to 6.3% in Zn₀.₀₃WMo₃. Zinc doping improves the cell's efficiency in converting sunlight into electricity. Undoped WMO₃ showed a uniform granular morphology with relatively smooth surfaces. The grains were well-crystallized and tightly packed, but the surface area may be low. The structure's small size decreases porosity and surface roughness, limiting light trapping and charge transport. Introducing Zn modifies the morphology, leading to larger grains and increased surface porosity. Enhanced intergranular connectivity facilitated greater charge transport; rising zinc concentration induced peak intensity shifts and alterations, signifying amplified crystal lattice distortion. Enhanced properties, such as improved charge carrier mobility, make this material better for photovoltaic applications. Zn doping raises the bandgap from 1.27 eV in WMO₃ to 1.66 eV in Zn₀.₀₃WMO₃. This shifted the optical absorption edge toward shorter wavelengths (from the near infrared region (NIR) to visible light), thus enhancing visible-light harvesting in photovoltaics.
EARLY PREDICTION OF ALZHEIMER'S DISEASE USING CONVOLUTIONAL NEURAL NETWORK
Alzheimer's disease (AD) is a progressive neurological disorder that shows considerable difficulties in both diagnosis and treatment. Achieving an early and precise diagnosis is crucial for alleviating symptoms and enhancing patient outcomes. Although technological advancements have been made, there is still a pressing need for automated decision-support tools to aid clinicians. Deep learning approaches have shown amazing gains and impressive results in medical image processing this recent years, offering encouraging answers to these problems. In this study, we propose a convolutional neural networks (CNNs) framework trained and tested on a publicly available magnetic resonance imaging (MRI) dataset. We employ extensive data augmentation, dropout regularization, and early stopping to mitigate overfitting. The proposed approach seeks to distinguish between different stages of AD, tackling one of the most intricate aspects of managing the disease. Experimental findings reveal that the proposed model achieved an overall accuracy of 99.3%, with precision, recall, and F1‑score exceeding 98% for all classes, and AUC values over 0.99. This result demonstrates the potential of the framework to delivers superior predictive accuracy and robustness compared to current leading methods, highlighting its potential as a dependable tool for clinical applications in AD detection and prognosis.
Computer, Telecommunications, Software, Electrical & Electronics Engineering
EFFECTS OF STORAGE TIME AND PACKAGING MATERIALS ON NUTRITIONAL AND MICROBIOLOGICAL CONTENT OF GARRI
Garri, a product of granulated cassava tuber, is a major staple food in Nigeria with huge potential for export. Garri has a short shelf life, which further deteriorates with time under poor packaging conditions. This study examined how packaging material and storage time affect the proximate composition, carotenoid content, cyanide content, energy content, and microbial content of the samples. Garri was stored in four packaging materials, namely, paper, nylon, plastic and jute bags. The responses were measured and recorded every 2 weeks for 24 weeks. The findings showed that the amounts of crude protein, crude fat, and ash in garri stored in the four packaging materials did not differ significantly (p > 0.05). There was a significant difference (p<0.05) in carbohydrate, moisture content, crude fibre, cyanide, carotenoid, energy, and total viable microbial count between samples stored in paper and jute bags compared to those stored in plastic and polythene materials. Storage time had a significant linear effect (p<0.01) on all the parameters measured and had a significant quadratic effect (p<0.05) on all the parameters except crude protein and energy content. The optimized results showed that samples stored in plastics gave the best results overall.
Agricultural, Bioresources, Biomedical, Food, Environmental & Water Resources Engineering
Agricultural and Bioresource Engineering
Modeling Contextual Understanding for Conversational Agents Development: A Systematic Review of Recent Advances and Challenges
Accurate capture of contextual information is a critical requirement for developing conversational agents capable of sustaining coherent and relevant multi-turn dialogues. By effectively understanding the context of an ongoing conversation, conversational agents can improve user experience through consistent dialogue flow, memory retention, and personalized responses. However, challenges persist in designing scalable and efficient context-aware models. This systematic review examines recent advancements, methodologies, and current challenges in context modeling for conversational agents, with a focus on techniques for enhancing coherence, contextual retention, and user-centered interactions. To conduct this review, a structured search was performed across multiple databases using terms such as contextual modeling, multi-turn dialogue, and contextual understanding in conversational AI. Studies meeting inclusion criteria were categorized based on their methodologies such as memory networks, attention mechanisms, graph-based approaches, and transformer-based models. Results reveal a strong reliance on deep learning architectures, particularly transformers, which have improved context retention across complex dialogues. Memory-augmented models, attention mechanisms, and graph-based approaches also show promise in handling context continuity and user-specific personalization. Despite these advancements, significant challenges remain in computational efficiency, scalability, and ethical considerations, especially regarding data privacy and user trust. In conclusion, while the field has made notable progress in enhancing context modeling, further work is required to address efficiency, scalability, and ethical implications. Future research in areas like dynamic context adaptation, multi-modal context integration, and session continuity holds potential for developing more sophisticated and responsible conversational agents.
Contextual Modeling, Conversational Agents, Multi-turn Dialogue, Natural Language Understanding, Deep Learning Techniques
Computer Science, Artificial Intelligence, Natural Language Processing
RECENT ADVANCES IN WIRELESS SENSOR NETWORKS LOAD-BALANCING OPTIMIZATION: A REVIEW OF APPROACHES AND EMERGING TRENDS
Wireless Sensor Networks (WSNs) play an essential role in applications such as environmental monitoring, smart cities, and industrial automation, where numerous sensor nodes are positioned in remote areas with limited energy resources. The longevity of these networks depends significantly on active load-balancing mechanisms to consistently distribute data processing and communication tasks, thereby preventing early energy depletion of individual nodes. This study employs a systematic review methodology to evaluate recent developments in load-balancing strategies for WSNs. We organize these strategies into three primary types: centralized, decentralized, and hybrid approaches. Centralized methods utilize a global view for optimal load distribution but are hindered by scalability and bottleneck issues. Decentralized methods, where nodes make independent decisions, improve scalability and fault tolerance but have a high likelihood of causing uneven load distribution. Hybrid methods incorporate features from both centralized and decentralized approaches, seeking a balance between global coordination and local adaptability. Additionally, this review examines emerging techniques that integrate machine learning and game theory, providing dynamic and real-time adaptations to changing network conditions. Results indicate that while each approach has distinct strengths, challenges remain, especially regarding energy efficiency, scalability, and adaptability to environmental changes. The analysis underscores the need for adaptive and context-aware load-balancing solutions that enhance WSN resilience in complex scenarios. In conclusion, this review provides insights into the latest advancements and identifies areas for future research in load-balancing strategies, aiming to support sustainable WSN deployments across diverse applications.
Computer Science, Computer Networking, Internet of Things
SOIL AND HUMAN HEALTH RELATIONSHIP: EXPLORING THE INTERCONNECTEDNESS OF ECOSYSTEM AND WELL-BEING – A REVIEW
This review examines the intricate connections between soil health and human well-being, highlighting both the benefits of healthy soils and the risks posed by soil contamination and degradation. A comprehensive literature review was conducted by synthesizing peer-reviewed articles, reports, and case studies published between 2000 and 2024, with earlier seminal works included where relevant. Sources were retrieved from databases such as Scopus, Web of Science, PubMed, and Google Scholar using keywords including soil health, human health, soil contamination, food security, and ecosystem services. The selected literature was thematically analyzed to identify ecological, nutritional, biomedical, and policy dimensions of the soil–human health nexus. The review reveals that healthy soils are fundamental to food security, nutrition, disease prevention, and ecosystem stability. Conversely, degraded or contaminated soils contribute to micronutrient deficiencies, exposure to toxic substances, and the spread of soil-borne diseases. Case studies further illustrate the long-term health risks associated with pollutants such as heavy metals and persistent organic chemicals. The review also highlights emerging evidence of soil’s role in mental health and its potential for disease prevention through soil-derived compounds and beneficial microorganisms. The findings emphasizes the urgent need for integrated policies and sustainable soil management practices that address both agricultural productivity and public health. Strengthening soil conservation, remediation, and monitoring strategies will be critical in reducing health risks and ensuring resilient food systems. The review calls for interdisciplinary collaboration among soil scientists, public health experts, and policymakers to safeguard human health and environment the through the protection of soil resources.
Agricultural and environmental sustainability
MACHINE LEARNING CLASSIFICATION TECHNIQUES TO PREDICT IN SITU COMPRESSIVE STRENGTH OF REINFORCED CONCRETE USING NDT DATA
Steel Reinforced Concrete (RC) is widely used in the construction industry due to its effective compressive and tensile strengths. This composite nature allows for withstanding diverse loading conditions. Assessing the structural condition of RC is essential for determining the extent of repair and retrofitting required for aged structures. Non-destructive testing (NDT) methods are commonly employed for this purpose. The objective of this study is to develop suitable correlations between compressive strengths of RC (fRC) and results obtained from two ND Tests - Schmidt rebound hammer (RS) test and Ultrasonic pulse velocity (Ѵ) test using machine learning techniques (ML) on the software platform of MATLAB. The experimental program involved casting 450 RC specimens, which included beam specimens with dimensions 70 cm × 15 cm × 15 cm and standard cube specimens with dimensions 15 cm × 15 cm × 15 cm, using concrete grades ranging from M25 to M35. The beams were reinforced with 2.68% longitudinal steel and provided with nominal concrete covers of 20 mm and 40 mm. NDT measurements (RS and Ѵ) were taken on the beam specimens, while compressive strength was determined from the companion cube specimens via destructive compression testing. The collected data were then analyzed using MATLAB’s Classification Learner app. A Support Vector Machine (SVM) model was used to establish the correlation between RS and fRC, while a Decision Tree classifier refined the Ѵ dataset with an accuracy of 99%. Using the ML approach, the data were effectively segregated through the developed models, which were further utilized to estimate the actual compressive strength of RC from NDT results. The study demonstrates that ML-based models can reliably estimate in-situ compressive strength from NDT results, yielding a practical approach for structural health assessment of RC.
Building, Civil & Geotechnical Engineering
INVESTIGATION OF EMISSION PROPERTIES OF A COMPRESSION IGNITION ENGINE USING WATER – DIESEL EMULSION FUEL
Abstract The growing environmental problems linked to regular diesel fuel, like greenhouse gas emissions and particulate matter, people are looking for cleaner, more sustainable options. Water-Diesel Emulsion (WDE) fuel has become a promising option because it can cut down on harmful pollutants without needing to change the structure of current engines. A study examined the emission profiles of a single-cylinder, four-stroke compression ignition engine utilising water-diesel emulsion fuels with different water concentrations. The findings indicated that elevating the water concentration in diesel fuel markedly influences combustion dynamics and emission characteristics. With 10 % WDE under full load conditions, NOx emissions went down by as much as 30 %. This was mostly because water cools things down. The smoke opacity went down by 10 %, which means that the combustion was more complete and the air-fuel mixing was better because of the micro-explosion effect. But there was a small rise in carbon monoxide and unburned hydrocarbon emissions, especially at lower loads. With neat diesel the 10 % water–diesel emulsion (WDE10) reduced NOx emissions by 30 % at full load and decreased smoke opacity by 10 % across the tested load range. However, CO and unburned hydrocarbons (HC) increased at certain operating points (CO rose 15 % for WDE10 under selected loads), while CO₂ showed an increase of 8% at high loads, indicating measurable trade-offs. The results indicate that WDE is a feasible and effective method for improving combustion efficiency and decreasing significant emissions in CI engines, providing an immediate and economic transition strategy towards cleaner fuels while utilising current diesel infrastructure.
OPTIMAL DEEP NEURAL NETWORK PARAMETERS FOR POWER LOSS MINIMIZATION ANALYTICS
Abstract Power loss remains a critical problem in power systems, leading to voltage instability, reduced transmission efficiency, equipment degradation, and significant financial losses for utility providers. These inefficiencies disrupt reliable electricity delivery, increase operational costs, and hinder the sustainability goals of smart grid infrastructure. Traditional analytical and optimization methods have often proven inadequate in accurately modeling the nonlinear and dynamic behaviors that characterize power networks. As a result, there is a growing need for advanced intelligent models capable of analyzing large-scale data, uncovering patterns, and predicting losses more effectively. This study addresses the power loss challenge by developing an optimized Deep Neural Network (DNN) framework for power loss minimization analytics in smart grids. Three separate power loss datasets were analyzed using the Orange data mining platform and the Python development environment. The DNN model was systematically tuned by optimizing key parameters such as activation functions, number of hidden layers, learning rates, and batch sizes. Among the configurations tested, the model employing the Rectified Linear Unit (ReLU) activation function with six hidden layers achieved the best performance. The optimized DNN produced a Mean Squared Error (MSE) of 1.0E-03, Root Mean Squared Error (RMSE) of 3.4E-02, Mean Absolute Error (MAE) of 1.9E-02, coefficient of determination (R²) of 0.94, and Mean Absolute Percentage Error (MAPE) of 4.8%. When compared with conventional models, the optimized DNN demonstrated a 20–25% improvement in predictive accuracy. These results confirm that optimizing DNN parameters significantly enhances power loss analytics in smart grid systems. The proposed model offers a robust and intelligent solution for minimizing losses, improving energy efficiency, and supporting informed decision-making in modern power networks.
DNN, Optimal Activation Function, Smart Grid, Power Loss, Model Performance Metrics, Parameter tuning
Computer Sciences, Artificial Intelligence
AUGMENTED MULTI-LABEL CLASSIFICATION FOR THE EARLY DETECTION OF CO-OCCURRING MENTAL HEALTH DISORDERS
Mental health disorders such as anxiety, depression, and schizophrenia often go undiagnosed due to limited awareness, social stigma, and reliance on subjective clinical evaluations. Traditional screening methods can be time-consuming, leading to delayed interventions and worsening conditions. This system aims to provide an early screening tool that helps individuals assess their mental health status and assists healthcare professionals in identifying disorders quickly and accurately. The system employs a multi-label classification approach to predict multiple co-existing mental health disorders simultaneously. The dataset is created using psychiatrist-approved questionnaires, and since real-world mental health data is often limited and biased, Generative Adversarial Networks (GANs) are used to generate synthetic data for improved model training. This enhances generalizability and reduces bias in predictions. By providing a user-friendly AI-powered screening tool, the system helps reduce the taboo around mental health conditions and bridges the gap between individuals and mental health professionals. It ensures faster, data-driven diagnosis, allowing for timely interventions and better treatment planning, ultimately improving mental healthcare accessibility and efficiency. The experimental results indicate that the Random Forest model achieved the best overall performance, with an F1-score weighted average of 0.40 and strong label-wise performance, particularly for obsessive-compulsive disorder (OCD) (F1 = 0.69), Post-Traumatic Stress Disorder (PTSD) (F1 = 0.62), and Normal (F1 = 0.38), demonstrating its effectiveness in multi-label mental health disorder detection.
MATHEMATICAL MODELLING OF COVID-19 TRANSMISSION DYNAMICS IN NIGERIA WITH CONTROL THROUGH VACCINATION, TREATMENT, AND AWARENESS CAMPAIGNS
The COVID-19 pandemic, though significantly reduced by 2023, still poses a risk of resurgence in Nigeria due to suboptimal vaccination uptake and weak adherence to preventive measures. To better understand and strengthen control strategies, this study develops a mathematical model of COVID-19 transmission dynamics with control through awareness campaigns, use of safe vaccines, and treatment of infected individuals. The proposed model is formed using a system of nonlinear ordinary differential equations with eight compartments. Analytical results established the boundedness and positivity of the solution, as well as the existence and stability conditions for both the disease-free and endemic equilibria. The basic reproduction number, derived using the next-generation matrix approach, was greater than one in the absence of interventions but declined below unity when vaccination and awareness coverage improved, indicating potential elimination of the disease. Sensitivity analysis revealed that vaccination completion rate, isolation with treatment, and awareness campaigns are the most influential parameters in reducing transmission. Numerical simulations, based on Nigerian data, showed that without awareness campaigns COVID-19 could persist with high infection levels, while increased vaccination awareness markedly reduced susceptible and infectious populations. Long-term projections further indicated that sustained vaccination, awareness campaigns, and treatment can eradicate the disease. The results of the analysis showed that the proposed controls are effective to eradicate and prevent a resurgence of COVID-19 in Nigeria. Hence, it is recommended that public health awareness, vaccination, and treatment be implemented at full scale for effective disease control.
OPTIMIZATION OF FCA AND MIG WELDING PARAMETERS FOR AISI-1045 STEEL
Welding is a vital industrial process for joining metals and thermoplastics, with Metal Inert Gas (MIG) and Flux Cored Arc (FCA) welding widely used due to their versatility and effectiveness. This study optimized FCA and MIG welding parameters for AISI 1045 alloy steel using Response Surface Methodology (RSM) via Central Composite Design (CCD) L20(5)3 in Minitab 20. The parent material and weldments were characterized through standard testing methods. X-ray fluorescence (XRF) analysis confirmed the material as galvanized medium-carbon steel containing 0.376% carbon and 22–25% iron, while revealing a crystalline structure with features indicating crystal size and possible defects. Mechanical testing showed UTS values for welds were generally lower than the base metal, high joint efficiencies confirmed good bonding. Also, optimal conditions for hardness (FCAW: 4.6 kgm/s, 73.2 A, 28 mm; MIG: 8 kgm/s, 73.2 A, 24.6 mm), UTS (FCAW: 10 kgm/s, 73.2 A, 26 mm; MIG: 8 kgm/s, 73.2 A, 24.6 mm), and impact strength (MIG: 6 kgm/s, 106.8 A, 26 mm), with gas flow rate as the dominant factor and refined microstructures confirming superior weld quality and bonding. Both welds were free from porosity and cracks, underscoring the importance of parameter optimization for superior weld quality and mechanical performance.
IMPACT OF WELDING CURRENT ON MICROSTRUCTURAL AND MECHANICAL CHARACTERISTICS OF CMT-WELDED 316L AUSTENITIC STAINLESS STEEL
316L Austenitic Stainless Steel (ASS) has an extensive demand in engineering applications including the chemical, petrochemical and nuclear sectors. These industries greatly benefit from the exceptional mechanical and corrosion-resistant qualities of 316L ASS welded joint. This study investigates 316L ASS welded joints using Cold Metal Transfer (CMT) welding with 100A, 110A and 120A welding currents at 11V constant voltage applying 309L filler material. The Base Metal (BM) exhibited majorly constant austenitic grains with increasingly higher current promoting the formation of wider Heat Affected Zone (HAZ), more dendritic 316L ASS weld microstructures which allows faster cooling rates; resulting in higher micro hardness, tensile strength and toughness. The result also showed that as the welding current increases from 100A to 120A the HAZ width increased from 26.2 µm to 50.6 µm. The highest hardness (173 HV) was observed at weld zone (WZ) of High Welding Current (HWC) weld joint as compared to Low Welding Current (LWC) and Medium Welding Current (MWC) weld joint with observed hardness of 150 HV and 167HV respectively. The HWC weld joint also exhibited higher tensile strength (665 MPa) as compared to LWC weld joint (597 MPa) and MWC weld joint (574 MPa). The toughness of MWC 45J) was found to be lowest as compared to LWC (66J) and HWC (128J) highest. With these higher mechanical properties due to favorable microstructure from different cooling rates, 316L ASS metal with weldments of 309L filler material is the most applicable material for higher corrosion resistant industrial designs.
Industrial and Manufacturing Engineering
INTRA AND INTERMOLECULAR HYDROGEN BONDING IN POLYSACCHARIDE REINFORCEMENTS: THEIR EFFECTS ON THE MECHANICAL PROPERTIES OF POLYLACTIDE
In this work, cellulose (CSC) and lignin (CSL), chemically sourced from cornstalk (CS), were separately used as reinforcement on polylactide (PLA). The aim was to evaluate the influence of intra and intermolecular hydrogen bond interactions in these polysaccharides on the mechanical properties of PLA. Cellulose was synthesized from 100 µm CS particles using 1 M NaOH, while 1 M HCl was used in extracting (CSL) from CS. Biocomposite containing 20 wt. % of each reinforcement was cast into tensile and flexural test specimens. The average hydrogen bonding EH calculated from Fourier Transform Infrared spectroscopy (FTIR) within the OH region of CSC was 4.63 kCal, while that from synthesized CSL was 4.47 kCal. Furthermore, the average bond distance R (evaluated from FTIR) was 2.78 and 2.83 Á for CSC and CSL, respectively. Both reinforcements improved the tensile properties of PLA, but the composite (PLA/CSC) with CSC as reinforcement gave a better result. The tensile strength increased from 30.1 MPa in PLA to 45.5 and 67 MPa in PLA/CSL and PLA/CSC, respectively. Unreinforced PLA began to yield from 15.1 MPa, while PLA/CSL and PLA/CSC commenced yielding from 39.4 and 54.8 MPa, respectively. The maximum elastic modulus (2.32 GPa) was maintained by PLA/CSC, while unreinforced PLA possessed a modulus of 1.96 GPa. The poorest response to elongation under tension (0.9 %) was recorded by unreinforced PLA, and PLA/CSC reached 4.7 % of its initial length before fracture. The addition of CSC to PLA raised the flexural modulus and strength of PLA by 61.2 and 187.8 %, respectively. Morphology of the PLA fractured surface, analyzed via Scanning Electron Microscope (SEM), revealed minor cracks with flakes; the fractured surface was not as rough as that of PLA/CSC and PLA/CSL. This showed that unreinforced cast PLA displayed a brittle-like feature.
SUITABILITY ASSESSMENT OF LATERITE SOIL AS ALTERNATIVE TO SHARP SAND IN CEMENT BLOCK PRODUCTION: PERFORMANCE EVALUATION AND PREDICTIVE ANALYSIS
To address the growing need for alternative building materials, this study investigated laterite soil as a partial replacement for sharp sand in cement block production, aiming to provide a sustainable and cost-effective building material. Laterite soil and sharp sand were characterized, and cement blocks with 0-30% laterite replacement were produced. Particle size distribution test was carried out to determine the grading characteristics of the aggregates. In addition, water absorption, bulk density, and compressive strength tests were evaluated at 7, 14, 21, and 28 days of curing. Statistical and predictive analysis, including ANOVA and machine learning models, was performed. Results showed laterite had a higher Coefficient of Uniformity (15.40) than sand (9.47), indicating better grading. Water absorption increased with laterite replacement, reaching 2.95% at 30% compared to 1.88% for the control mix. Bulk density decreased linearly from 2448 kg/m³ to 2180 kg/m³ at 30% replacement. Compressive strength declined with increased laterite, with significant reductions beyond 25%. ANOVA revealed significant differences in compressive strength (p < 0.000157), and Scheffé post-hoc analysis identified significant strength reductions at 25% and 30% laterite replacement. Predictive modeling using Artificial Neural Networks (ANN) achieved the highest accuracy (R² = 0.982, RMSE = 0.118), outperforming polynomial (R² = 0.962) and linear regression (R² = 0.854). The study concluded that laterite replacement up to 20% maintains acceptable block quality, balancing sustainability with structural performance.
Laterite soil, cement block production, compressive strength, sustainable building materials and partial sand replacement