AAU Institutional Repository (AAU-ETD)
Addis Ababa University Institutional repository is an open access repository that collects,preserves, and disseminates scholarly outputs of the university. AAU-ETD archives' collection of master's theses, doctoral dissertations and preprints showcase the wide range of academic research undertaken by AAU students over the course of the University's long history.
How to Submit Your Work
The repository contains scholarly work, both unpublished and published, by current or former AAU faculty, staff, and students, including Works by AAU students as part of their masters, doctoral, or post-doctoral research
- All AAU faculty, staff, and students are invited to submit their work to the repository. Please contact the library at your college.
You may contact digirep@aau.edu.et.with any questions about the repository
Colleges,Institutes in AAU-ETD
Select a college,institute to browse its collections.
Recent Submissions
An Assessment of Practice and Challenges of Educational Material Resource Management in Government Primary Schools of Lemi Kura Sub City
(Addis Ababa University, 2025-09-01) Andualem Getachew; Demoze Degefa
The practices and challenges of educational material resource management (EMRM) are studied in government primary schools of Lemi Kura Sub-City Addis Ababa by assessing the effectiveness of planning, acquisition, utilization, control and maintenance with particular emphasis on controlling EMRM. The study utilized a descriptive survey design and followed mixed method research that combined qualitative and quantitative analysis. AS: BANDED SCORING In this manner, the data were obtained from 125 respondents (principals, vice-principals, heads of department and teachers) by using stratified and simple random sampling method. The main tool of data collection was questionnaires, and it was supported by semi-structured interviews and document review to ensure the triangulation of methods. Descriptive statistics such as means and standard deviations, percentages were used in the analysis of data.
Results EMRM practices were moderate on average with strong performance in planning and acquisition, and poor performance in controlling, using and maintenance of materials. Some of the main challenges facing EMRM were under-allocation by the budget, suspension in resource delivery, lack of proper storage facilities and poor coordination among management bodies coupled with weak staff training on resource management.
The study concluded that existing EMRM practices in the sub-city’s government primary schools are not sufficient to provide high-quality teaching and learning. Suggestions to improve the efficacy of resource management include consistent and adequate financial support, timelines distribution, improving storage facilities, and providing regular, targeted professional development opportunities for school staff
Assessment of Physical Properties of Ambo and Jema Sand and Evaluation of their Performance as Fine Aggregate in High Strength Concrete
(Addis Ababa University, 2023-06) Shimelis Gemechu; Biruktawit Taye
In Ethiopia, the construction of high-rise buildings, long span bridges and complex concrete structures which make use of high strength concrete is becoming common in recent times. In order to meet the ever increasing demand for fine aggregate in concrete construction, an alternative to the river sand is inevitable as river sand is quickly depleting and its quality from time to time is becoming questionable. The use of sand stone as a replacement to the fine aggregate for the production of High strength concrete in Ethiopia is an important study area since sufficient research on detail engineering characteristics and performance is not yet fully investigated. In this research, the performance and rate of strength development of high strength concrete made from sand stone sampled from Ambo and Jema river basin is studied. In addition, a comparison of strength development of High Strength Concrete was made between the sandstone fine aggregates and river sand (control sand). The experimental concrete is prepared and mixed in two separate sizes of coarse aggregate, 19 mm and 12.5 mm nominal maximum. The corresponding water cement ratio for 19 mm coarse aggregate was 0.27 and for the 12.5 mm coarse aggregate it was 0.29. Chemical admixture was used for all the concrete mixes in proportion to the cement content. The control concrete mix has attained the target strength for C70, C80 and C90 when 520 kg, 550 kg and 580 kg of cement per metric cube of concrete used and when 19 mm coarse aggregate used whereas it has not attained the C90 strength when 12.5 mm aggregate used. Concrete made with Ambo sand has attained the target strength of C70, C80 but failed for C90 for both types of coarse aggregate when used in the mix. Concrete made with Jema sands has not attained the target strength of C70, C80 and C90 for same amount of cement and coarse aggregate sizes that was used for the control mix. For Ambo sand concrete, when 19 mm aggregate is used, the percent of the compressive strength against the control concrete is 96.4%, 96.6%, 96.1% for C70, C80, C90 and it is in order of 97.1%, 97.7%, 94.4% when 12.5 mm aggregate used. The percent of the compressive strength of Jema sand concrete against the control concrete when 19 mm aggregate is used is 90.8%, 87.6%, 85% for C70, C80, C90 and it is in order of 89.5%, 87.5%, 87.3% when 12.5 mm aggregate used. The flexural test values, the modulus of elasticity values and mortar strength test results of concrete made from Ambo and Jema sands have met the requirement while Ambo sand produces a concrete with marginal increment than Jema sand. From the cost computation, to produce a unit volume of high strength concrete, the cost of concrete made from Ambo sand is 3.26% cheaper than Jema sand and it is 1.16% cheaper than the control sand.
Optimizing Bottle Filling Efficiency Through a Machine Learning Approach: a Case of National Alcohol and Liquor Factory
(Addis Ababa University, 2026-02) Jemera Melaku; Ameha Mulugeta
The bottle filling process is an important process in beverage production since it has direct
influence on the quality of the product, compliance with regulations, and the efficiency of
production. Nonetheless, major deviations on the bottle filling process are noticed in the National
Alcohol and Liquor Factory (NALF). Records of inspections of 35 production days in January-
February 2025 found that 27,972 bottles were underfilled and 18,648 overfilled, with an average
of about 1332 bottles in need of reprocessing per day. These variations raise manpower, energy
use, equipment depreciation, operation expenses and lowers the total output. The factory used in
the 2024 annual performance report has reached an output of 85 percent of the intended
production capacity, which indicates the necessity of the better monitoring and control of the
process. In this study, the aim was to maximize the efficiency of bottle filling based on a
supervised machine learning approach. The study involved both quantitative and qualitative
research methods using both exploratory and experimental research designs. Primary data were
gathered with the help of structured interviews, structured discussions with experts of the
company, and direct observations of the filling process, whereas secondary data contained
records of maintenance, production records, and reports on quality inspection. A supervised
machine learning approach was used in this investigation to identify filling results. A Random
Forest classifier was used to distinguish filling outcomes as underfill, correct fill, or overfill. The
model had a training accuracy of 99.6% and a testing accuracy of 99.3%. A macro and weighted
average performance and reliability were used to validate model performance and reliability, and
out-of-bag error estimation and stratified k-fold cross-validation were used to validate. To
facilitate real-time monitoring and dynamic process control, the developed model was coupled
with the filling process by use of a three-layer architecture. The conclusions reveal that machine
learning can be used to enhance the stability of the process, minimize deviations during filling,
and decrease the use of resources, as well as assist in making decisions that are supported by
data. This Study adds a viable machine learning framework to enhance filling efficiency and
promote intelligent manufacturing behaviors in the production of beverages
Performance Evaluation of Karfe Small-Scale Irrigation Scheme: (A case Study of Karfe Small Scale Irrigation Scheme East Shoa Bishoftu,
(Addis Ababa University, 2026-05) Enanye Tadege; Daneal Fekeresslasie
The performance of small-scale irrigation schemes is necessary for increasing agricultural productivity
and ensuring sustainable water resources management. This study assessed the hydraulic, agronomic,
and structural performance of the Karfe Small-Scale Irrigation Scheme (65 ha), which diverts water
from the Mojo River using a broad-crested weir and serves 162 households through right (2,250 m) and
left (1,503 m) main canals. The evaluation was conducted during the main wheat cropping season (07
January–30 April 2026), considering spatial variability across head, middle, and tail sections of the
command area. A mixed-methods approach was applied, combining field measurements, laboratory soil
analysis, and farmer interviews. Soil were analysed for texture, bulk density, and soil moisture
characteristics using standard laboratory equipment, while irrigation performance parameters were
measured using bucket–stopwatch flow measurement techniques and canal observations. Performance
indicators were computed based on FAO, IWMI, Bos et al. Khosla, and Terzaghi standards. The results
show that the soils are dominantly clay (average 55.39%), with bulk density ranging from 1.10–1.13
g/cm³, field capacity of 0.37–0.41 m³/m³, and total available water of 130–170 mm/m. Application
efficiency averaged 54.82% (moderate), while storage efficiency averaged 89.10%. Water supply
indicators shown spatial imbalance, with SAR (1.08), RI (50% at tail), and DPR (0.58) confirming
over-supply at the head and deficits at the tail. Conveyance efficiency averaged 76.09% (moderate),
while distribution uniformity was 75%. Structural evaluation showed poor conditions with an average
SCI of 2.36 and EI of 33.93%, indicating significant infrastructure deterioration. Major hydraulic
failures were also observed, including misaligned weir (12.4° deviation), insufficient stilling basin
length (2.19m deficit), floor length (1.55m) ,Froude number (3.93m) and FS Sliding(1.45),FS
Overturning 1.88 the retaining wall instability under seismic loading and water Pressure. Based on
these findings, mitigation measures including redesigned the retaining wall in two options (option1) is
Masonry, option 2 reinforced concrete retaining wall) as alternative, canal rehabilitation, structural
repair, improved irrigation scheduling, and strengthening of water user management are recommended
to enhance overall system performance and sustainability.
A FUZZY DEMATEL SYSTEMATIC APPROACH FOR RISK ANALYSIS IN THE CASE OF ADDIS ABABA’S ROAD CONSTRUCTION PROJECTS ©
(Addis Ababa University, 2025-03) Enyew Lemma; Abraham Assefa
Construction industry is a complex, dynamic and challenging industry that each process and activities in creating new things are susceptible to risk. In its dynamic nature, there is always a change in interpretation of and compliance with many applicable laws, codes, and regulations; gathering of considerable amount of resources, including labor, equipment, and material; and communications with and coordination among multiple parties, such as the owner, the design professional, other contractors and subcontractors, and suppliers, all of whom may have different purposes and goals. In relation to the dynamic nature of the industry, many factors that are not planned and expected might arise during the construction process. In this context, both the Ethiopian road construction industry in general and Addis Ababa’s Road construction sector in particular demonstrate a significant need for an effective risk analysis approach to evaluate the impact of various risks and uncertainties on project performance. An effective risk analysis method should account for the dynamic nature of risks throughout the project life cycle and incorporate feedback loops that influence overall risk outcomes. This project paper proposes and applies a Fuzzy DEMATEL-based systematic approach; an enhanced and practical method that explicitly identifies, quantifies, and analyzes key risk influences. Forty-Five major risks were identified, grouped into 21 categories, and analyzed within a Systematic, hierarchical framework. The findings confirm that Procurement Risks, Economy-Related Uncertainties, Time Uncertainties/Risks, and Technological Uncertainties are the most critical risks impacting the achievements of the project goals. The identified risk factors have also been organized into a causal hierarchical risk structure, with the most prominent risk categories ranked accordingly.