Faculty of Engineering, Design and Technology

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A Comparative Study Between the Conventional Method and the Use of Powdered Activated Carbon in Iron Reduction from Groundwater
(Uganda Christian University, 2024) Andrew Mbabaali
This report was specifically looking at the comparison between the conventional treatment method (aeration followed by settling and filtration) and the use of Powdered Activated Carbon (PAC) in an adsorbent filter in total iron reduction from groundwater sources. The PAC was prepared from sawdust. The water sample was collected from iron contaminated ground water source (borehole with hand pump). Both treatment methods indicated above (conventional and adsorption) were applied to check the total iron reduction from the ground water source and the results were compared in order to determine which method could be more effective in total iron reduction. Total iron levels were tested both before and after the treatment and the results showed that the conventional method was able to reduce the total iron concentration by 95.22% (from 41.44mg/L to 1.98mg/L) while on the other hand, PAC layer thicknesses of 2.5cm, 5cm, and 7.5cm were able to reduce the total iron concentration by 99.08%, 99.20%, and 99.40% respectively between 41.44mg/L and 0.38mg/L, 41.44mg/L and 0.33mg/L and 41.44mg/L and 0.25mg/L respectively . However, the reduced iron levels to (1.98mg/L and 0.33mg/L for conventional and PAC respectively) were still above the permissible limits compared to the Uganda National Standards for treated potable water which is 0.3mg/L. But since 0.33mg/L total iron is also within the permissible limit of the Uganda National Standards for untreated potable water (1mg/L), it was considered to be safe at household level.
A Comparative Study Between the Conventional Method and the Use of Powdered Activated Carbon in Iron Reduction from Groundwater
(Uganda Christian University, 2024-04-12) Eric Manede
This report was specifically looking at the comparison between the conventional treatment method (aeration followed by settling and filtration) and the use of Powdered Activated Carbon (PAC) in an adsorbent filter in total iron reduction from groundwater sources. The PAC was prepared from sawdust. The water sample was collected from iron contaminated ground water source (borehole with hand pump). Both treatment methods indicated above (conventional and adsorption) were applied to check the total iron reduction from the ground water source and the results were compared in order to determine which method could be more effective in total iron reduction. Total iron levels were tested both before and after the treatment and the results showed that the conventional method was able to reduce the total iron concentration by 95.22% (from 41.44mg/L to 1.98mg/L) while on the other hand, PAC layer thicknesses of 2.5cm, 5cm, and 7.5cm were able to reduce the total iron concentration by 99.08%, 99.20%, and 99.40% respectively between 41.44mg/L and 0.38mg/L, 41.44mg/L and 0.33mg/L and 41.44mg/L and 0.25mg/L respectively . However, the reduced iron levels to (1.98mg/L and 0.33mg/L for conventional and PAC respectively) were still above the permissible limits compared to the Uganda National Standards for treated potable water which is 0.3mg/L. But since 0.33mg/L total iron is also within the permissible limit of the Uganda National Standards for untreated potable water (1mg/L), it was considered to be safe at household level.
A Digital Predictive Healthcare Management System for Sickle Cell Disease Using Machine Learning and Data Visualization Techniques: A Case Study of Uganda
(Uganda Christian University, 2025-05-06) Tirza Atwiine
This project presents the design and development of a Digital Predictive Healthcare Management System for Sickle Cell Disease (SCD) using Machine Learning and Data Visualization techniques, with a focus on Uganda. Sickle Cell Disease remains a significant public health challenge in Uganda, with limited access to timely diagnosis, treatment monitoring, and personalized care. The proposed system leverages machine learning algorithms; Random Forest Classifier and LSTM(Long-Short-Term-Memory), to predict potential health risks, analyse and predict future patient data, and support early interventions. Interactive dashboards and visual tools created using React provide healthcare professionals and patients with actionable insights for better disease management. This project aims to enhance decision-making, improve patient outcomes, and support national efforts in digital health transformation, particularly in under-resourced settings.
A Machine Learning Based Web Application for Pre-Eclampsia Risk Prediction, Awareness and Management
(Uganda Christian University, 2025-05-06) Angela Nina Twine Mukiiza
Pre-eclampsia is a critical condition affecting pregnant women that is characterized by high blood pressure and potential damage to vital organs. This research focuses on developing a machine learning-based web application designed to predict the risk of pre-eclampsia, enhance awareness and provide management strategies. Utilizing patient data, the application aims to offer accurate predictions and a recommendation. The project involves data collection, model training and application deployment emphasizing the integration of user- friendly interfaces and real-time data processing. The research underscores the importance of early detection and intervention potentially reducing the adverse outcomes associated with pre-eclampsia. By leveraging machine learning algorithms and web technologies, this application aspires to empower healthcare providers and expectant mothers with actionable insights fostering better health outcomes and informed decision-making. This work represents a significant stride towards improving maternal health care through innovative technological solutions.
A Sacco Loan Management System: The Case Study of Kisoboka Farmer’s Sacco Ltd
(Uganda Christian University, 2024-05-27) Getrude Adrupio; Kevin Taimwa; Priscillar Nakintu
The SACCO Loan Management Information System is a computerized database of financial information organized and programmed in such a way that it produces regular reports on operations for every level of management in a SACCO; it is usually also possible to obtain special reports from the system easily. The general objective of the study was to design a loan management information system that could improve loan record handling and bring about timely Loan processing. The study's specific objectives of study were to investigate and gather data about the existing manual system being used at Kisoboka Farmers SACCO Ltd, analyze the requirements for the new system. Develop a loan management information system for Kisoboka Farmers' SACCO Ltd, and test and implement the new system. After gathering the requirements, entity relation diagrams and data flow diagrams were used to design the loan management information system. Microsoft Visual Basic 2005.net was used to create user interfaces and Microsoft Access was used to create the database. The SACCO loan management information system developed authenticates System users and enables them to capture and store loan records and client banking records, the system allows the users to view loan reports and Client payment records, also the system calculates payment denominations for loans [1]. The SACCO loan management information system reduces the burden of over-compiling papers and the need for more shelves which reduces congestion in Kisoboka Farmers' Sacco Ltd [2].
Accessing the Use of Calcined Quartzite Dust as a Fillertoenhance Rut Resistance in Asphalt Concrete
(Uganda Christian University, 2025-04-17) Moise Lokorto Amule
Worldwide flexible pavements are mainly constructed with aggregates, bitumen and filler. This study aims to assess the use of Calcined quarzite dust (CQD) to enhance rut resistance in the asphalt concrete. The methods used were mechanical,physical, chemical to determine the engineering properties of aggregate, bitumen and filler used in this study; also Marshall test was conducted, as well as the Indirect tensile Strength (ITS) in relation with the Wheel Tracking Tests (WTT) to assess the performance on the asphalt mixture. Various tests were done during the study, various factors such as stability, flow, air voids,voids filled with binder (VFB), and voids in the mineral aggregate (VMA) were evaluated. The study was conducted with CDQ percentage variation of 2%, 4% and 6% and 4.4% optimum bitumen content was obtained from the neat asphalt. The results of the study showed that the use of Calcined quarzite dust led to an increase in Marshall stability from 15kN to 21.1 kN, increased Indirect Tensile Strength Strength ratio from 82% to 88%, ITS dry from 900.2kPa to 1,130.6kPa and ITS wet from 740kPa to 990kPa. With relation to wheel tracking tests the ITS value greater than 800kPa expect less than 10mm rutting depth for a 20,000 cycles passes. This study therefore shows that asphalt mixes with 4.5% CQD in the asphalt mixture can be used as a means to enhance the stability and stiffness.
Accessing the use of crumb rubber and kaolinite to the resistance of asphalt to deformation
(Uganda Christian university, 2025-04-26) Mustafa Anthony Pitia
Flexible pavement performance is often reduced by rutting, fatigue and lack of durability especially under heavy traffic. This study aimed to improve bitumen by modifying it with Crumb rubber (CR) and kaolinite clay to enhance resistance to fatigue cracking. Waste tires were used due to their high elasticity. CR was added to 60-70 pen grade bitumen in proportions of 5%, 10%, 15% and 20%. Tests showed that 5% CR gave the best balance of thermal, fatigue and moisture resistance. To improve storage stability, kaolinite was added in amounts of 1%, 1.5%, 2% and 2.5% with 2% showing the best results.
Agrilink- a Web-based Platform Connecting Farmers to Buyers
(Uganda Christian University, 2025-05-17) Emmanuel Vangamoi
Smallholder farmers and buyers in agricultural markets often face a disconnect, with farmers losing profits to intermediaries and buyers struggling to find suitable products. This report presents AgriLink: A Web-Based Platform Connecting Farmers to Buyers, crafted by our team to revolutionize this dynamic. AgriLink is a digital bridge that not only links farmers and buyers directly but also leverages machine learning (ML) to deliver personalized recommendations matching farmers with interested buyers and suggesting products to buyers based on their preferences Built with cutting-edge web technologies, AgriLink offers a user-friendly dashboard featuring real-time sales trends, competitor pricing comparisons, and buyer engagement insights. Its standout AI/ML engine analyzes transaction data and behavior to recommend high-potential buyers to farmers and tailored products to buyers, enhancing trade efficiency. A smart-matching system further ensures pricing clarity by grouping similar product names, like ‘Maize’ and ‘Corn.’ Our collaborative approach fused data integration, algorithmic innovation, and user-centric design. AgriLink empowers farmers to bypass middlemen, optimize pricing, and grow sales, while buyers discover relevant offerings effortlessly. This platform redefines agricultural connectivity blending technology and intelligence to uplift rural livelihoods and market vitality.
Agrogetaway Web Application
(Uganda Christian University, 2024-05-15) Ddumba, Timothy; Gortland, Jim James; Tryphine, Nalule; Josephine, Namuwonge; Joan, Nabirye; Adam, Murungi
The Agrogetaway project is an innovative initiative aimed at transforming the agricultural landscape by leveraging technology to facilitate knowledge exchange between farmers and agricultural enthusiasts. This web-based platform serves as a conduit for sharing best practices, advanced farming techniques, and practical knowledge through an interactive and user-friendly interface. The project's cornerstone is the creation of a digital community where users can book farm tours, access a wealth of educational resources, and engage in real-time discussions with experienced model farm managers. Agrogetaway addresses the critical challenge of improving agricultural produce quality in Uganda, which directly impacts the nation's competitiveness in the global market and its food security. By connecting users with model farmers and providing a repository of multimedia educational content, the platform fosters a collaborative environment for learning and growth. The project also introduces a novel feature, 'Communities,' structured around model farmers, where users can subscribe for exclusive insights and direct mentorship. The platform's development followed a mixed-methods approach, incorporating both qualitative and quantitative research to ensure comprehensive user-centric design and functionality. Evaluations indicate user satisfaction and a gradual increase in engagement, although challenges such as a lack of scientific depth and device compatibility were identified. Recommendations for future enhancements include infrastructure improvements, user education programs, and advanced security measures. Agrogetaway's vision is to empower farmers with the knowledge and tools necessary to enhance productivity and sustainability, ultimately contributing to the advancement of the agricultural sector. The project's ongoing development and research aim to expand its reach, improve user experience, and solidify its position as an indispensable resource for modern agriculture.
AI Image-based System for Lumpy Skin Disease Detection in Cattle
(Uganda Christian University, 2025) Amos Mugabi
Lumpy Skin Disease (LSD) remains a significant threat to cattle health across Uganda, with conventional disease detection methods being slow, centralized, and reliant on clinical expertise that is often unavailable in field settings. This project proposes an innovative solution through an AI-powered, image-based detection system capable of identifying LSD from cattle images. The system employs a two-stage deep learning architecture: a YOLOv8 object detection model locates individual cattle within images, followed by a convolutional neural network (CNN) that classifies each animal as either healthy or infected based on visible skin lesions. Trained on a diverse dataset of annotated cattle images, the integrated model achieved a high detection precision and classification accuracy, demonstrating strong reliability in recognizing signs of LSD. Furthermore, the system offers real-time feedback via an interactive web interface, enabling farmers and veterinary personnel to quickly assess cattle health with images. This approach not only enhances detection and control measures but also sets the stage for broader adoption of AI in livestock health management within low-resource environments. The system’s design aligns with global goals of smart agriculture, offering a scalable tool that supports both food security and disease resilience.
AI-Powered Robotics for Post-Disaster Survivor Detection and Rescue Path Mapping
(Uganda Christian University, 2025-05-06) Diana Nansubuga
Natural and man-made disasters often highlight the weaknesses in traditional emergency response efforts, especially in countries with limited resources like Uganda. Challenges such as slow response times, difficulty reaching affected areas, and a lack of real-time information frequently result in lost lives and misused resources. This study introduces a robotic dog system enhanced by artificial intelligence, designed to improve the effectiveness of search and rescue missions following disasters. The system incorporates real-time human detection using AI, SLAM for creating environmental maps, and the A* algorithm to plan efficient rescue routes. The robot is powered by a Raspberry Pi and managed through a Flask-based web platform. It includes multiple sensors, such as GPS, ultrasonic detectors, a night vision camera, and directional microphones, allowing it to navigate independently, avoid obstacles, and communicate wirelessly with remote rescue teams. Testing the system in a controlled, disaster-like setting confirmed its ability to locate victims, generate accurate maps, and determine safe paths with high reliability. The results suggest that. This low-cost, locally adaptable solution could play a vital role in speeding up rescue efforts and minimising risks to human responders. By addressing key limitations in current practices, the project adds valuable insights to the field of disaster robotics, particularly In settings where advanced tools are not readily available.
An Embedded and Machine Learning Based Early Flood Monitoring and Warning System, the Case of River Manafwa
(Uganda Christian University, 2025-05-06) Daniel Lukyamuzi Wavamunno
Flooding remains a serious threat in many parts of Uganda, especially in regions with limited access to early warning systems. This project introduces a practical solution that combines embedded hardware and machine learning to monitor and predict flood events in real time. Using a flow sensor and an ultrasonic sensor connected to an ESP32 device, the system captures data on water movement and levels. These readings are automatically logged to Google Sheets, allowing for easy data management and access. A backend built with FastAPI processes this information, using a trained Random Forest algorithm to forecast potential flood risks. The results, along with past records, are displayed on an interactive dashboard developed in React. By merging simple electronics with predictive analytics, the system provides an affordable and adaptable tool to support timely flood response efforts in vulnerable areas.
An Evaluation of Horizontal Sub-drainage Systemsinmitigating Landslide Hazard in Shikuruwe Village, Bududa District
(Uganda Christian University, 2025-04-14) Cynthia Mayala Mamfa
Rainfall-induced landslides pose a significant threat in BUDUDA district in Eastern Uganda, due to intense precipitation, geological instability, and increasing population density. This study assessed the suitability of horizontal sub-drainage systems to mitigate landslide risk in SHIKURUWE village. The research involved site characterization, a hydrological study, and the design of a horizontal sub-drainage system. Key findings revealed that moderately steep slopes, low permeability, and fine-grained soils contributed to water stagnation in the soil profile. High rainfall patterns were found to be influenced by global meteorological phenomenon such as the El Niño. Further, land use and soil properties significantly influenced water losses in the watershed. Finally, the introduction of a horizontal sub-drainage system down slope was observed to improve drainage in both steady state and transient conditions, hence reducing the pore water pressure, and increasing the factor of safety of the slope.
Assessing the Effectiveness of Biotechinical Slope Stabilization Methods in Landslide Prone Areas
(Uganda Christian University, 2025-04-17) Prisca Nakinkunda
This research was conducted in Kasika Village with the primary objective of assessing the effectiveness of various biotechnical slope stabilization methods in landslide- prone areas. The study was motivated by the increasing problem of slope instability in the region, which has led to loss of lives and the destruction of critical community infrastructure, including roads. The research aimed to identify sustainable and environmentally friendly solutions for stabilizing vulnerable slopes. The study was guided by three key objectives: to evaluate the susceptibility of slopes to landslides in the affected areas, to determine the factor of safety of the slope before failure, and to assess the effectiveness of biotechnical slope stabilization techniques. The Analytical Hierarchy Process (AHP) was employed to develop a landslide susceptibility map achieving an AUC of 0.85 while the Morgenstern-Price method of slices was used to calculate the factor of safety under both saturated and normal soil conditions. Findings revealed that the studied slope was highly unstable and prone to landslide with a factor of safety of 0.885 for normal conditions and 0.955 for saturated conditions. The research recommended the implementation of brush layering using bamboo live cuttings (Oldeania alpina) as a sustainable biotechnical stabilization method to enhance slope stability in the area.
Assessing the Effectiveness of Biotechinicalslopestabilization Methods in Landslide Proneareas
(Uganda Christian University, 2025-04-17) Michael Vladimir
This research was conducted in Kasika village with a main aim of assessing the effectiveness of the different biotechnical slope stabilization methods in landslide prone areas. The study was triggered by the increasing challenge of slope instability in the area which led to loss of lives and destruction of community infrastructure such as roads, the study aimed at coming up with sustainable and environmentally friendly measures of stabilizing the slopes in the area. The study was conducted basing on three objectives that is to evaluate the slope susceptibility of the slopes to landslides in the landslide prone areas , to determine the factor of safety of the slope before failure and to assess the effectiveness of the biotechnical slope stabilization techniques. The study used (Analytical Hierarchy Process)AHP to come up with the landslide susceptibility map and morgenstern price method of slices to determine the factor of safety of the slope before failure under saturated and normal soil conditions. The study found out that the endangered slope was unstable and prone to landslides and recommended the use of the biotechinical slope stabilization method of brush layering with bamboo live cuttings(Oldenia Alpina) to stabilize the slope.
Assessing the Production of Algal Biodiesel as a supplement to Fossil Fuel
(Uganda Christian University, 2025-04-11) Angel Prisca Nabakka
The increase in dependence on fossil fuels in Uganda has been associated to the ever increasing population and is contributed to by rural-urban migration, as well as increased urbanization that causes increase in activities such as industrialization. The increasing strain on these fossil fuels, and the increase in their effects on the environment, infrastructure, as well as human beings has led to the need for renewable energy sources and the significant interest in algal bio-diesel. This study investigates the potential of three algae species, - Nannochloropsis, Chlorella Vulgaris and Botryococcus Braunii, for bio-diesel production. The algae was cultivated in similar conditions and the growth rates were monitored for each species using a manual stick measurement technique. Lipid extraction was performed using solvent extraction method to determine the lipid yield for each species with different solvent mixes and bio-diesel was synthesized through trans-esterification. The results showed that Nannochloropsis and Chlorella Vulgaris exhibited the highest growth rate while Nannochloropsis also had the highest lipid content and bio-diesel yield. Botryococcus Braunii had the lowest growth rate and lipid yield. A multi-criteria decision analysis using Analytical Hierarchy Process (AHP) was conducted to evaluate the best species based on growth performance, ease of culturing, lipid content and bio-diesel yield. The analysis identified Nannochloropsis as the most suitable option for bio-diesel production and the obtained bio-diesel was analyzed for key fuel properties, including cetane number, density, kinematic viscosity, flash point and copper strip corrosion following ASTM and EN standards. The study highlights the potential of algae as a sustainable feed-stock for bio-diesel production and emphasizes the need for further research on large scale cultivation and process optimization to enhance feasibility.
Assessing the Stabilization of Laterite Soils Using Steel Slag and Quarry Dust for Road Subbase Layer
(Uganda Christian University, 2025-04-16) Elvis Ssekkalaba
Lateritic soils are often weak and unsuitable for road construction without stabilisation. This study assesses the suitability of stabilising lateritic soil using steel slag and quarry dust, focusing on the Nasuuti-Nakabago-Ntawo road, where soil from the Nsambwe borrow pit had a CBR of 24.7 %, below the required 45 % for a G45 granular subbase material. Proctor test, CBR, Atterberg limits, and particle size distribution, were performed on varying percentages of laterite, steel slag, and quarry dust. The results showed that 40 % steel slag addition increased the CBR by 160 % and reduced CBR swell by 70 %, improving load-bearing capacity. 10 % quarry dust with 40 % steel slag effectively reduced the plasticity index by 27 %, liquid limit by 13 %, and linear shrinkage by 33 %, enhancing workability and durability. These findings confirm that a combination of steel slag and quarry dust can significantly improve laterite soil properties, making it suitable for subbase applications in road construction.
Assessing the Stabilization of Marginal Laterite Soils Using Steel Slag and Quarry Dust for the Construction of a Subbase Road Layer
(Uganda Christrian University, 2025-04-10) Timothy Gary Kyaligonza
The research report focuses on the stabilization of marginal laterite soils using steel slag and quarry dust for the construction of a subbase road layer. Laterite soils, commonly found in tropical and subtropical regions, are characterized by their physical nature, chemical composition, and geological characteristics. The report highlights the challenges associated with using laterite soils in road construction, particularly their variability in quality and the need for stabilization to meet strength requirements. The research aims to assess the suitability of using steel slag, a byproduct of the steelmaking process, and quarry dust, a byproduct of stone crushing, as stabilizers to improve the engineering properties of laterite soils. The methodology section details the various tests conducted to evaluate the physical and mechanical properties of the soil samples, including the California Bearing Ratio (CBR), Atterberg limits, and particle size distribution. The results indicate that the addition of steel slag improves the load-bearing capacity of the soil sample by 160% at 40% of steel slag. Quarry dust reduces the liquid limit by 13%, the plastic index by 27%, and linear shrinkage by 33% at 10% of quarry dust in the blend. The report concludes that the use of these industrial byproducts not only enhances the performance of the soil but also offers environmental benefits by recycling waste materials and reducing the need for natural resources.
Assessing the Stiffness Properties of Asphalt Concrete Modified With Waste Diesel Engine Oil. Acase Study of Kampala- Masaka Road
(Uganda Christian University, 2025-04-29) Peace Natangaza
This study explored the possibility of modifying bitumen with Waste Engine Oil (WEO) to help mitigate cracking in asphalt pavements. Laboratory investigations were conducted on bitumen of 50/70 penetration grade with WEO additions of 0-6%. In all experiments with WEO, the penetration increased from 62.2 to 73.4 mm, and all other physical properties measured decreased; notably the softening point (from 54.2 to 45.5° C) and density of the bitumen (from 1.018 to 0.999 g/cm³). Marshall Mix Design implied that 4.6% was the optimal bitumen content, while the 3.5% WEO mixture had superior performance, workability and fatigue resistivity to the other mixtures. The results showed a very good moisture resistivity with a Tensile Strength Ratio of 92%. The findings from this study implied that for areas like Kampala- Masaka, Uganda, it is feasible to modify bitumen with up to 4% WEO to sustainably enhance pavement performance, improving the longevity of the pavement and delaying maintenance requirements. It is indicated that wide scale field studies be undertaken to monitor the impact of these laboratory studies in the field.
Assessing the Stiffness Properties of Asphalt Concrete Modified With Waste Diesel Engine Oil. Acase Study of Kampala- Masaka Road
(Uganda Christian University, 2025-04-24) Tyson Mugume
This research explored the modification of 50/70 penetration grade bitumen with waste diesel engine oil (WDEO) to enhance asphalt concrete properties for the Kampala-Masaka highway. The highway exhibits significant pavement deterioration, especially cracking, potentially caused by heavy traffic loads and temperature variations, which necessitates improved pavement materials. The study aimed to assess the impact of DWEO content on stiffness and workability by blending bitumen with DWEO at ratios of 0%, 2%, 3.5%, 4%, and 6%, and subjecting the mixtures to physical and mechanical tests. The 3.5% DWEO blend demonstrated the most favorable performance. Key findings from Marshall and Volumetric Analysis indicated a 22% improvement in stiffness and a 17% improvement in workability with DWEO modification compared to the control. This suggests that incorporating DWEO enhances the asphalt's resistance to cracking and deformation, addressing the observed deterioration issues. While DWEO modification shows promise for improving asphalt properties, further research is recommended to thoroughly address observed stability issues. Optimization of DWEO content is crucial to achieve a balance between improved stiffness and workability and ensure long-term pavement performance and durability on the Kampala-Masaka highway.

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