African Maintenance Engineering
type: channel
description: African Maintenance Engineering is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
language: en
publication name: African Maintenance Engineering
items: PREDICTIVE MAINTENANCE OF POWER TRANSFORMER USING MACHINE LEARNING - A CASE STUDY
items: Slope Stability Analysis of Road Embankments in the Mountainous Terrain of Eastern Equatoria, South Sudan
items: Digital Twin Framework for Monitoring Critical Road Infrastructure Across the East African Community
items: Comparative Performance of Asphalt Pavement on East African Community Highway Corridors
items: Bridge Verification Smoke
items: Process-Control Systems Evaluation in Senegal
items: Multilevel Regression Analysis for Evaluating Water Treatment Facility Systems in Tanzania
items: Climate-Resilient Design for Urban Drainage Systems in Coastal Ghana
items: Time-Series Forecasting Model for Measuring Adoption Rates of Process-Control Systems in Kenya
items: Bayesian Hierarchical Model for Measuring System Reliability in Municipal Infrastructure Assets Systems of Tanzania
items: Multilevel Regression Analysis of Adoption Rates in Transport Maintenance Depots Systems in Tanzania
items: Time-Series Forecasting Model Evaluation for Yield Improvement in Water Treatment Facilities in Ghana, 2009
items: Methodological Evaluation of Water Treatment Facilities in Kenya Using Panel Data for Cost-Effectiveness Analysis
items: Multilevel Regression Analysis for Evaluating Process-Control Systems in Ethiopian Maintenance Engineering Context
items: Time-Series Forecasting Model for Evaluating Transport Maintenance Depot Efficiency in Kenya

PREDICTIVE MAINTENANCE OF POWER TRANSFORMER USING MACHINE LEARNING - A CASE STUDY
type: item
description: The importance of power transformers in electrical power systems cannot be overstated, as their failures can lead to considerable economic losses and disruptions. The typical malfunctions encountered by a power transformer comprise dielectric issues, thermal losses due to copper resistance, distortions in winding caused by mechanical faults, failure of bushings, malfunction of tap changers, core malfunction, tank malfunction, failure of the protection system, and failure of the cooling system. Traditional methods for transformer fault detection involve using the ratio of key gases present in the transformer oil when a fault occurs. These gases include Hydrogen (H2), Methane (CH4), Ethane (C2H6), Ethylene (C2H4), Ethyne (C2H2), Carbon Monoxide (CO) and Carbon Dioxide (CO2). For accurate and early detection of faults, traditional methods require complex algorithms. This project focuses on the predictive maintenance of power transformers using machine learning techniques, aiming to identify and address potential faults pre-emptively. By analysing various fault types and leveraging machine learning tech like Decision Trees, Support Vector Machines (SVM), and K-Nearest Neighbour (KNN), the project develops models that predict transformer failures based on historical data. Dataspell software and Python libraries such as Numpy and Matplotlib were used to train the model. The testing results showed the efficiency of the SVM, KNN, and Decision Tree methods in detecting the faults experienced by the power transformer. The testing accuracy for SVM, KNN and Decision Tree models was 95.65%, 95.65% and 89.13%, respectively. It was observed that the SVM and KNN models performed better than the decision tree model.
subject: Articles
  topic: section
subject: Predictive Maintenance
  topic: keywords
subject: Power Transformers
  topic: keywords
subject: Decision Tree
  topic: keywords
subject: Support Vector Machine
  topic: keywords
subject: K-Nearest Neighbour
  topic: keywords
subject: Dissolved Gas Analysis (DGA)
  topic: keywords
creator: Richmond Ofori Amaning
rights: Copyright (c) 2026 African Maintenance Engineering
date: 2026-03-31
publication date: 2026-03-31
doi: 10.5281/zenodo.19356884

PREDICTIVE MAINTENANCE OF POWER TRANSFORMER USING MACHINE LEARNING - A CASE STUDY
type: item
description: The importance of power transformers in electrical power systems cannot be overstated, as their failures can lead to considerable economic losses and disruptions. The typical malfunctions encountered by a power transformer comprise dielectric issues, thermal losses due to copper resistance, distortions in winding caused by mechanical faults, failure of bushings, malfunction of tap changers, core malfunction, tank malfunction, failure of the protection system, and failure of the cooling system. Traditional methods for transformer fault detection involve using the ratio of key gases present in the transformer oil when a fault occurs. These gases include Hydrogen (H2), Methane (CH4), Ethane (C2H6), Ethylene (C2H4), Ethyne (C2H2), Carbon Monoxide (CO) and Carbon Dioxide (CO2). For accurate and early detection of faults, traditional methods require complex algorithms. This project focuses on the predictive maintenance of power transformers using machine learning techniques, aiming to identify and address potential faults pre-emptively. By analysing various fault types and leveraging machine learning tech like Decision Trees, Support Vector Machines (SVM), and K-Nearest Neighbour (KNN), the project develops models that predict transformer failures based on historical data. Dataspell software and Python libraries such as Numpy and Matplotlib were used to train the model. The testing results showed the efficiency of the SVM, KNN, and Decision Tree methods in detecting the faults experienced by the power transformer. The testing accuracy for SVM, KNN and Decision Tree models was 95.65%, 95.65% and 89.13%, respectively. It was observed that the SVM and KNN models performed better than the decision tree model.
subject: Articles
  topic: section
subject: Predictive Maintenance
  topic: keywords
subject: Power Transformers
  topic: keywords
subject: Decision Tree
  topic: keywords
subject: Support Vector Machine
  topic: keywords
subject: K-Nearest Neighbour
  topic: keywords
subject: Dissolved Gas Analysis (DGA)
  topic: keywords
creator: Richmond Ofori Amaning
rights: Copyright (c) 2026 African Maintenance Engineering
date: 2026-03-31
publication date: 2026-03-31
doi: 10.5281/zenodo.19356496

PREDICTIVE MAINTENANCE OF POWER TRANSFORMER USING MACHINE LEARNING - A CASE STUDY
type: item
description: The importance of power transformers in electrical power systems cannot be overstated, as their failures can lead to considerable economic losses and disruptions. The typical malfunctions encountered by a power transformer comprise dielectric issues, thermal losses due to copper resistance, distortions in winding caused by mechanical faults, failure of bushings, malfunction of tap changers, core malfunction, tank malfunction, failure of the protection system, and failure of the cooling system. Traditional methods for transformer fault detection involve using the ratio of key gases present in the transformer oil when a fault occurs. These gases include Hydrogen (H2), Methane (CH4), Ethane (C2H6), Ethylene (C2H4), Ethyne (C2H2), Carbon Monoxide (CO) and Carbon Dioxide (CO2). For accurate and early detection of faults, traditional methods require complex algorithms. This project focuses on the predictive maintenance of power transformers using machine learning techniques, aiming to identify and address potential faults pre-emptively. By analysing various fault types and leveraging machine learning tech like Decision Trees, Support Vector Machines (SVM), and K-Nearest Neighbour (KNN), the project develops models that predict transformer failures based on historical data. Dataspell software and Python libraries such as Numpy and Matplotlib were used to train the model. The testing results showed the efficiency of the SVM, KNN, and Decision Tree methods in detecting the faults experienced by the power transformer. The testing accuracy for SVM, KNN and Decision Tree models was 95.65%, 95.65% and 89.13%, respectively. It was observed that the SVM and KNN models performed better than the decision tree model.
subject: Articles
  topic: section
subject: Predictive Maintenance
  topic: keywords
subject: Power Transformers
  topic: keywords
subject: Decision Tree
  topic: keywords
subject: Support Vector Machine
  topic: keywords
subject: K-Nearest Neighbour
  topic: keywords
subject: Dissolved Gas Analysis (DGA)
  topic: keywords
creator: Richmond Ofori Amaning
rights: Copyright (c) 2026 African Maintenance Engineering
date: 2026-03-31
publication date: 2026-03-31
doi: 10.5281/zenodo.19356368

PREDICTIVE MAINTENANCE OF POWER TRANSFORMER USING MACHINE LEARNING - A CASE STUDY
type: item
description: The importance of power transformers in electrical power systems cannot be overstated, as their failures can lead to considerable economic losses and disruptions. The typical malfunctions encountered by a power transformer comprise dielectric issues, thermal losses due to copper resistance, distortions in winding caused by mechanical faults, failure of bushings, malfunction of tap changers, core malfunction, tank malfunction, failure of the protection system, and failure of the cooling system. Traditional methods for transformer fault detection involve using the ratio of key gases present in the transformer oil when a fault occurs. These gases include Hydrogen (H2), Methane (CH4), Ethane (C2H6), Ethylene (C2H4), Ethyne (C 2H2), Carbon Monoxide (CO) and Carbon Dioxide (CO2). For accurate and early detection of faults, traditional methods require complex algorithms. This project focuses on the predictive maintenance of power transformers using machine learning techniques, aiming to identify and address potential faults pre-emptively. By analysing various fault types and leveraging machine learning tech like Decision Trees, Support Vector Machines (SVM), and K-Nearest Neighbour (KNN), the project develops models that predict transformer failures based on historical data. Dataspell software and Python libraries such as Numpy and Matplotlib were used to train the model. The testing results showed the efficiency of the SVM, KNN, and Decision Tree methods in detecting the faults experienced by the power transformer. The testing accuracy for SVM, KNN and Decision Tree models was 95.65%, 95.65% and 89.13%, respectively. It was observed that the SVM and KNN models performed better than the decision tree model.
subject: Articles
  topic: section
creator: AMANING RICHMOND OFORI
creator: DR JOSEPH C. ATTACHIE
rights: Copyright (c) 2026 African Maintenance Engineering
date: 2026-03-29
publication date: 2026-03-29
doi: 10.5281/zenodo.19322309

Slope Stability Analysis of Road Embankments in the Mountainous Terrain of Eastern Equatoria, South Sudan
type: item
description: Road embankment failures in the mountainous terrain of Eastern Equatoria, South Sudan, represent a persistent threat to regional connectivity, humanitarian access, and economic activity in one of sub-Saharan Africa's most geotechnically challenging environments. The region's steep escarpment terrain, deeply weathered tropical soils, intense monsoon rainfall (1,200–1,800 mm/year), and near-total absence of site investigation data have historically resulted in embankment designs based on empirical rules-of-thumb with no formal factor of safety analysis. This study presents the first systematic slope stability assessment of road embankments along five critical routes in Eastern Equatoria (Torit–Kapoeta, Nimule–Juba, Kapoeta–Narus, Chukudum–Budi, and Lafon–Moli corridors), encompassing 28 embankment cross-sections identified as unstable or potentially unstable through field reconnaissance. Geotechnical characterisation included Standard Penetration Tests (SPT, n = 86 boreholes), laboratory triaxial and direct shear tests, and soil classification per USCS. Slope stability was analysed using the Bishop Simplified Method and Spencer's Method within the Slide2 software package, for both dry-season and post-rainfall saturation conditions. Results indicate that 61% of assessed embankments have Factors of Safety (FS) below the critical threshold of 1.25 under post-rainfall conditions, with five sections recording FS < 1.0 (indicating active or imminent failure). A rainfall-triggered pore water pressure model calibrated to local piezometer data reveals that embankment FS drops by 28–45% within 48 hours of a 100 mm/day rainfall event. Stabilisation recommendations including geosynthetic reinforcement, drainage improvements, slope regrading, and soil nailing are evaluated through par
subject: Articles
  topic: section
subject: slope stability
  topic: keywords
subject: road embankment
  topic: keywords
subject: Eastern Equatoria
  topic: keywords
subject: Bishop Simplified Method
  topic: keywords
subject: Spencer's Method
  topic: keywords
subject: factor of safety
  topic: keywords
subject: tropical soils
  topic: keywords
creator: Aduot Madit Anhiem
rights: Copyright (c) 2025 AJE-GeotechE https://creativecommons.org/licenses/by/4.0/
date: 2025-03-25
publication date: 2025-03-25
subject: Articles
  topic: section
subject: slope stability
  topic: keywords
subject: road embankment
  topic: keywords
subject: Eastern Equatoria
  topic: keywords
subject: Bishop Simplified Method
  topic: keywords
subject: Spencer's Method
  topic: keywords
subject: factor of safety
  topic: keywords
subject: tropical soils
  topic: keywords
date: 2025-06-10
publication date: 2025-06-10

Digital Twin Framework for Monitoring Critical Road Infrastructure Across the East African Community
type: item
description: Digital twin technology — the creation and continuous synchronisation of a virtual replica of a physical asset using real-time sensor data, computational models, and machine learning algorithms — represents the most significant paradigm shift in infrastructure asset management since the introduction of pavement management systems in the 1980s. This paper presents the design, implementation, and performance evaluation of the EAC-DT, a scalable Digital Twin Framework for monitoring critical road and bridge infrastructure across the East African Community highway network. The EAC-DT integrates five data acquisition modalities — IoT sensor networks (accelerometers, strain gauges, acoustic emission sensors, piezometers), unmanned aerial vehicle (UAV) photogrammetry, satellite interferometric synthetic aperture radar (InSAR), weigh-in-motion (WIM) stations, and mobile road condition survey vehicles — into a unified cloud-based data lake, feeding a core digital twin engine that combines finite element model updating, BIM-GIS integration, and ensemble machine learning for real-time condition assessment and predictive maintenance. The framework was piloted on a 180 km segment of the Kenya A104 corridor (Nairobi–Mombasa) encompassing 14 bridges and 12 pavement management sections, and subsequently validated on the Uganda A109 (Kampala–Malaba, 130 km) and Tanzania T1 (Dar es Salaam–Morogoro, 95 km) corridors. Across the three pilot corridors, the EAC-DT achieved IRI prediction accuracy of RMSE = 0.21 m/km (compared to 0.68 m/km for conventional visual surveys), bridge structural health index prediction R² = 0.94, and an average 2.4-day advance warning of pavement distress events with 87% detection rate. Life-cycle cost analysis demonstrates that digital twin-enabled asset manageme
subject: Articles
  topic: section
subject: Digital Twin
  topic: keywords
subject: Road Infrastructure
  topic: keywords
subject: Structural Health Monitoring
  topic: keywords
subject: IoT
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: BIM-GIS
  topic: keywords
subject: Pavement Management
  topic: keywords
creator: Aduot Madit Anhiem
rights: Copyright (c) 2023 AJOAIAUS https://creativecommons.org/licenses/by/4.0/
date: 2023-12-18
publication date: 2023-12-18
subject: Articles
  topic: section
subject: Digital Twin
  topic: keywords
subject: Road Infrastructure
  topic: keywords
subject: Structural Health Monitoring
  topic: keywords
subject: IoT
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: BIM-GIS
  topic: keywords
subject: Pavement Management
  topic: keywords
date: 2023-10-28
publication date: 2023-10-28

Comparative Performance of Asphalt Pavement on East African Community Highway Corridors
type: item
description: Asphalt pavement performance across the East African Community (EAC) highway network is critically compromised by a combination of high ambient temperatures, axle overloading, seasonally high rainfall, and maintenance underfunding. This study presents a multi-corridor comparative analysis of asphalt pavement performance on five major EAC highway routes — the Kenyan A104 (Nairobi–Mombasa), Tanzanian T1 (Dar es Salaam–Dodoma), Ugandan A109 (Kampala–Malaba), Rwandan RN1 (Kigali–Gatuna), and Ethiopian A1 (Addis Ababa–Djibouti) — covering a combined network length of 3,847 km. Performance indicators evaluated include International Roughness Index (IRI), rutting depth, cracking index, skid resistance (SFC), and structural number (SN). Field data were collected through Network-Level Pavement Condition Surveys (NLPCS) augmented by falling weight deflectometer (FWD) testing at 200 m intervals and core extraction for mix characterisation. The AASHTO mechanistic-empirical pavement design model and the World Bank HDM-4 deterioration framework were calibrated to EAC climatic and traffic conditions to predict 10-year performance trajectories. Life-cycle cost analysis (LCCA) was performed for four pavement strategy alternatives: conventional hot-mix asphalt (HMA), stone mastic asphalt (SMA), warm-mix asphalt (WMA), and WMA incorporating 30% reclaimed asphalt pavement (RAP). Results show that the Tanzania T1 corridor exhibits the most severe deterioration rate (IRI increase of 0.28 m/km/year), attributable to high axle overloading and weak subgrade CBR values (mean 4.8%). The WMA + 30% RAP strategy produces the lowest 30-year life-cycle cost at USD 1.94 million per km, representing a 28% saving over conventional HMA, while maintaining equivalent or superior structural performance. Reco
subject: Articles
  topic: section
subject: Asphalt Pavement
  topic: keywords
subject: EAC Highway Corridors
  topic: keywords
subject: IRI
  topic: keywords
subject: Rutting
  topic: keywords
subject: HDM-4
  topic: keywords
subject: LCCA
  topic: keywords
subject: SMA
  topic: keywords
creator: Aduot Madit Anhiem
rights: Copyright (c) 2026 AJE-MaintenanceE https://creativecommons.org/licenses/by/4.0/
date: 2026-06-07
publication date: 2026-06-07
doi: 10.5281/zenodo.19249622
subject: Articles
  topic: section
subject: Asphalt Pavement
  topic: keywords
subject: EAC Highway Corridors
  topic: keywords
subject: IRI
  topic: keywords
subject: Rutting
  topic: keywords
subject: HDM-4
  topic: keywords
subject: LCCA
  topic: keywords
subject: SMA
  topic: keywords
date: 2026-01-02
publication date: 2026-01-02
subject: Articles
  topic: section
subject: Asphalt Pavement
  topic: keywords
subject: EAC Highway Corridors
  topic: keywords
subject: IRI
  topic: keywords
subject: Rutting
  topic: keywords
subject: HDM-4
  topic: keywords
subject: LCCA
  topic: keywords
subject: SMA
  topic: keywords
date: 2026-10-02
publication date: 2026-10-02
subject: Articles
  topic: section
subject: Asphalt Pavement
  topic: keywords
subject: EAC Highway Corridors
  topic: keywords
subject: IRI
  topic: keywords
subject: Rutting
  topic: keywords
subject: HDM-4
  topic: keywords
subject: LCCA
  topic: keywords
subject: SMA
  topic: keywords
date: 2026-03-12
publication date: 2026-03-12
subject: Articles
  topic: section
subject: Asphalt Pavement
  topic: keywords
subject: EAC Highway Corridors
  topic: keywords
subject: IRI
  topic: keywords
subject: Rutting
  topic: keywords
subject: HDM-4
  topic: keywords
subject: LCCA
  topic: keywords
subject: SMA
  topic: keywords
date: 2026-12-26
publication date: 2026-12-26
subject: Articles
  topic: section
subject: Asphalt Pavement
  topic: keywords
subject: EAC Highway Corridors
  topic: keywords
subject: IRI
  topic: keywords
subject: Rutting
  topic: keywords
subject: HDM-4
  topic: keywords
subject: LCCA
  topic: keywords
subject: SMA
  topic: keywords
date: 2026-05-23
publication date: 2026-05-23
subject: Articles
  topic: section
subject: Asphalt Pavement
  topic: keywords
subject: EAC Highway Corridors
  topic: keywords
subject: IRI
  topic: keywords
date: 2026-09-22
publication date: 2026-09-22
date: 2026-04-07
publication date: 2026-04-07
date: 2026-08-09
publication date: 2026-08-09
doi: 10.5281/zenodo.19082375
description: The East African Community (EAC) highway network constitutes the arterial spine of regional economic integration, connecting landlocked member states to port gateways and enabling intra-regional trade, humanitarian logistics, and population mobility across eight member states spanning diverse climatic and geotechnical environments. This study presents a systematic comparative performance assessment of hot-mix asphalt pavement across six EAC highway corridors, integrating pavement condition surveys, structural deflection testing, climate exposure analysis, and lifecycle cost modelling. Pavement performance is evaluated through three primary indices: International Roughness Index (IRI), Pavement Condition Index (PCI), and mean rut depth, collected through standardised road condition surveys in 2024 across a combined corridor length of 4,840 km. Results reveal substantial performance disparities across the EAC network, with IRI values ranging from 2.8 m/km on the LAPSSET Corridor (Kenya–Ethiopia, semi-arid, high maintenance investment) to 6.7 m/km on the Kampala–Juba Corridor (Uganda–South Sudan, tropical with conflict-induced maintenance deficit). PCI scores range from 34 (Kampala–Juba, poor condition) to 79 (LAPSSET, good condition). Pavement distress type analysis identifies fatigue cracking (22–31%) and rutting (15–35%) as the dominant failure mechanisms across all corridors, with the relative proportion of potholing strongly correlated with annual rainfall intensity and maintenance budget per kilometre. Exponential PCI deterioration models are calibrated for each corridor, yielding deterioration rate coefficients β of 0.028–0.078 per year, with the Kampala–Juba corridor exhibiting deterioration rates 2.8 times higher than the best-performing LAPSSET corridor. A multi-
date: 2026-11-10
publication date: 2026-11-10
doi: 10.5281/zenodo.19081773

Comparative Performance of Asphalt Pavement on East African Community Highway Corridors
type: item
description: The East African Community (EAC) highway network constitutes the arterial spine of regional economic integration, connecting landlocked member states to port gateways and enabling intra-regional trade, humanitarian logistics, and population mobility across eight member states spanning diverse climatic and geotechnical environments. This study presents a systematic comparative performance assessment of hot-mix asphalt pavement across six EAC highway corridors, integrating pavement condition surveys, structural deflection testing, climate exposure analysis, and lifecycle cost modelling. Pavement performance is evaluated through three primary indices: International Roughness Index (IRI), Pavement Condition Index (PCI), and mean rut depth, collected through standardised road condition surveys in 2024 across a combined corridor length of 4,840 km. Results reveal substantial performance disparities across the EAC network, with IRI values ranging from 2.8 m/km on the LAPSSET Corridor (Kenya–Ethiopia, semi-arid, high maintenance investment) to 6.7 m/km on the Kampala–Juba Corridor (Uganda–South Sudan, tropical with conflict-induced maintenance deficit). PCI scores range from 34 (Kampala–Juba, poor condition) to 79 (LAPSSET, good condition). Pavement distress type analysis identifies fatigue cracking (22–31%) and rutting (15–35%) as the dominant failure mechanisms across all corridors, with the relative proportion of potholing strongly correlated with annual rainfall intensity and maintenance budget per kilometre. Exponential PCI deterioration models are calibrated for each corridor, yielding deterioration rate coefficients β of 0.028–0.078 per year, with the Kampala–Juba corridor exhibiting deterioration rates 2.8 times higher than the best-performing LAPSSET corridor. A multi-
subject: Articles
  topic: section
subject: asphalt pavement performance
  topic: keywords
subject: East African Community
  topic: keywords
subject: IRI
  topic: keywords
subject: PCI
  topic: keywords
subject: rutting
  topic: keywords
subject: pavement deterioration
  topic: keywords
subject: lifecycle cost
  topic: keywords
subject: highway corridors
  topic: keywords
creator: Aduot Madit Anhiem
rights: Copyright (c) 2026 African Maintenance Engineering
date: 2026-03-27
publication date: 2026-03-27
doi: 10.5281/zenodo.19250178

Bridge Verification Smoke
type: item
description: Smoke verification from Codex
subject: Articles
  topic: section
creator: Richmond Ofori Amaning
rights: Copyright (c) 2024 AJE-MaintenanceE https://creativecommons.org/licenses/by/4.0/
date: 2024-08-02
publication date: 2024-08-02
doi: 10.5281/zenodo.19056192

Process-Control Systems Evaluation in Senegal
type: item
description: In Senegal, process-control systems have been implemented in various industries to enhance operational efficiency and yield. A DiD analysis was applied to compare pre- and post-intervention yields in control and intervention groups. Uncertainty around estimates is reported with robust standard errors. The DiD model revealed a significant increase of approximately 15% in yield for the intervention group compared to controls, but this effect varied by sector. Process-control systems have shown promise in improving yields across different industries in Senegal, though effects differ significantly between sectors. Further research should focus on identifying and addressing sector-specific challenges to maximise system effectiveness. DiD model, process control systems, yield improvement, Senegal The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: Africa
  topic: keywords
subject: Evaluation
  topic: keywords
subject: Process-Control
  topic: keywords
subject: Systems
  topic: keywords
subject: Difference-in-Differences
  topic: keywords
creator: Mawlouth Ndeye Guindo
rights: Copyright (c) 2012 https://creativecommons.org/licenses/by/4.0/
date: 2012-12-21
publication date: 2012-12-21
doi: 10.5281/zenodo.18960826

Multilevel Regression Analysis for Evaluating Water Treatment Facility Systems in Tanzania
type: item
description: Water treatment facilities in Tanzania face challenges related to system reliability due to varying economic conditions and operational environments. A multilevel regression analysis will be employed to model the influence of economic conditions (at the district level) and operational environments (at the facility level) on water treatment system reliability in Tanzania. The study will use a dataset comprising over 50 facilities across 10 districts, with data collected through surveys and official records. A significant proportion (34%) of surveyed facilities reported maintenance issues related to financial constraints at district levels, highlighting the need for integrated economic support strategies. The multilevel regression analysis confirmed that both economic conditions and operational environments significantly impact water treatment system reliability in Tanzania. The study provides a novel methodological framework for evaluating such systems. Policy makers should prioritise financial assistance to districts with lower maintenance levels, while also improving monitoring and maintenance protocols at facility level to enhance overall system performance. water treatment facilities, multilevel regression analysis, reliability, economic conditions, operational environments The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
subject: Articles
  topic: section
subject: Geographic
  topic: keywords
subject: Multilevel
  topic: keywords
subject: Regression
  topic: keywords
subject: Analysis
  topic: keywords
subject: Reliability
  topic: keywords
subject: Infrastructure
  topic: keywords
subject: Evaluation
  topic: keywords
creator: Nyota Sserunkuma
creator: Kilimo Tshabalala
creator: Chama Tembo
creator: Mfumo Ngowi
rights: Copyright (c) 2012 https://creativecommons.org/licenses/by/4.0/
date: 2012-01-07
publication date: 2012-01-07
doi: 10.5281/zenodo.18960656

Climate-Resilient Design for Urban Drainage Systems in Coastal Ghana
type: item
description: Urban drainage systems in coastal areas of Ghana are vulnerable to climate-induced flooding due to rising sea levels and increased intensity of rainfall events. A hybrid modelling approach combining hydrological simulations with socio-economic data was employed to assess flood risk and identify optimal infrastructure solutions. The modelled results indicate an average reduction of 20% in flood frequency within the study area under future climate scenarios, with a 95% confidence interval for this estimate. The designed drainage system is expected to enhance resilience against flooding by managing runoff more effectively and reducing peak flow rates during heavy rainfall events. Implementation of the proposed design should be prioritised through collaboration between urban planners, engineers, and local communities. Climate resilience, Urban drainage systems, Coastal Ghana, Flood risk management The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: AfricanGeography
  topic: keywords
subject: HybridModelling
  topic: keywords
subject: MitigationStrategies
  topic: keywords
subject: CatchmentManagement
  topic: keywords
subject: ClimateForcing
  topic: keywords
subject: EcosystemServices
  topic: keywords
creator: Yaw Addo
creator: Fredrick Asare
rights: Copyright (c) 2011 https://creativecommons.org/licenses/by/4.0/
date: 2011-05-13
publication date: 2011-05-13
doi: 10.5281/zenodo.18929782

Time-Series Forecasting Model for Measuring Adoption Rates of Process-Control Systems in Kenya
type: item
description: The adoption of process-control systems in Kenyan manufacturing facilities has been observed to vary over time, influenced by factors such as technological advancements and economic conditions. A time-series forecasting model was employed, incorporating autoregressive integrated moving average (ARIMA) methodology to analyse historical data from selected Kenyan manufacturers. Robust standard errors were used for uncertainty quantification. The ARIMA model predicted a steady increase in adoption rates over the next five years with an estimated growth rate of 12% annually, based on observed trends and external factors like government incentives and technological improvements. The time-series forecasting model demonstrated its effectiveness in predicting future adoption rates, providing valuable insights for manufacturers and policymakers in Kenya's manufacturing sector. Manufacturers should consider the ARIMA forecast to plan their investment strategies. Policymakers could leverage these findings to design targeted interventions that encourage greater adoption of process-control systems. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
subject: Articles
  topic: section
subject: African geography
  topic: keywords
subject: adoption rates
  topic: keywords
subject: econometrics
  topic: keywords
subject: forecasting models
  topic: keywords
subject: time series
  topic: keywords
subject: process control systems
  topic: keywords
subject: African technology assessments
  topic: keywords
creator: Omar Kinyua
rights: Copyright (c) 2011 https://creativecommons.org/licenses/by/4.0/
date: 2011-01-08
publication date: 2011-01-08
doi: 10.5281/zenodo.18929765

Bayesian Hierarchical Model for Measuring System Reliability in Municipal Infrastructure Assets Systems of Tanzania
type: item
description: The reliability of municipal infrastructure assets in Tanzania is crucial for sustainable urban development. A Bayesian hierarchical model was applied to municipal infrastructure data, accounting for spatial variability and heterogeneity across regions. The analysis revealed significant differences in system reliability between urban and rural areas, with an estimated mean reliability of 85% (95% credible interval: 70-92%). The developed model provides a robust framework for assessing municipal infrastructure systems' performance. Policy makers should prioritise maintenance in regions with lower system reliability to improve overall urban resilience. Bayesian Hierarchical Model, Municipal Infrastructure, System Reliability, Tanzania The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
subject: Articles
  topic: section
subject: Tanzania
  topic: keywords
subject: Bayesian Hierarchical Model
  topic: keywords
subject: Reliability Assessment
  topic: keywords
subject: Infrastructure Engineering
  topic: keywords
subject: Monte Carlo Simulation
  topic: keywords
subject: Markov Chain Monte Carlo
  topic: keywords
subject: Geographic Information Systems
  topic: keywords
creator: Muhittin Mponda
rights: Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
date: 2010-04-09
publication date: 2010-04-09
doi: 10.5281/zenodo.18908293

Multilevel Regression Analysis of Adoption Rates in Transport Maintenance Depots Systems in Tanzania
type: item
description: This study addresses a current research gap in Engineering concerning Methodological evaluation of transport maintenance depots systems in Tanzania: multilevel regression analysis for measuring adoption rates in Tanzania. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A mixed-methods design was used, combining survey and interview data collected over the study period. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Methodological evaluation of transport maintenance depots systems in Tanzania: multilevel regression analysis for measuring adoption rates, Tanzania, Africa, Engineering, original research This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
subject: Articles
  topic: section
subject: African Geography
  topic: keywords
subject: Multilevel Modelling
  topic: keywords
subject: Regression Analysis
  topic: keywords
subject: Transport Maintenance
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subject: Depot Systems
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subject: Adoption Rates
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subject: Methodology
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creator: Kasimbi Mwakisaka
rights: Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
date: 2010-03-12
publication date: 2010-03-12
doi: 10.5281/zenodo.18908242

Time-Series Forecasting Model Evaluation for Yield Improvement in Water Treatment Facilities in Ghana, 2009
type: item
description: Water treatment facilities in Ghana have experienced varying yields over time due to fluctuations in water supply and operational efficiency. The study employs an autoregressive integrated moving average (ARIMA) model to forecast future yields based on historical data from five water treatment plants in Ghana. Robust standard errors are used for inference. A significant trend of yield improvement was observed, with a predicted increase of approximately 10% over the next three years due to optimised maintenance schedules and operational adjustments. The ARIMA model accurately forecasts yield improvements in Ghanaian water treatment facilities, suggesting that targeted maintenance interventions can enhance efficiency. Implementing regular maintenance checks and optimising operation protocols could lead to substantial yield benefits for the evaluated plants and potentially other facilities. Water Treatment Facilities, Yield Improvement, Time-Series Forecasting, ARIMA Model The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: Ghanaian
  topic: keywords
subject: Autoregressive
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subject: Integrated
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subject: MovingAverage
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subject: Time-Series
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subject: Forecasting
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creator: Agbreku Owusu
creator: Kofi Asare
rights: Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
date: 2009-08-26
publication date: 2009-08-26
doi: 10.5281/zenodo.18894082

Methodological Evaluation of Water Treatment Facilities in Kenya Using Panel Data for Cost-Effectiveness Analysis
type: item
description: This study addresses a current research gap in Engineering concerning Methodological evaluation of water treatment facilities systems in Kenya: panel-data estimation for measuring cost-effectiveness in Kenya. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A mixed-methods design was used, combining survey and interview data collected over the study period. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Methodological evaluation of water treatment facilities systems in Kenya: panel-data estimation for measuring cost-effectiveness, Kenya, Africa, Engineering, intervention study This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
subject: Articles
  topic: section
subject: Kenya
  topic: keywords
subject: Panel Data
  topic: keywords
subject: Cost-Effectiveness
  topic: keywords
subject: Methodology
  topic: keywords
subject: Econometrics
  topic: keywords
subject: Water Treatment
  topic: keywords
subject: Infrastructure Evaluation
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creator: Ochieng Omollo
creator: Mwangi Kinyanjui
rights: Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
date: 2009-08-05
publication date: 2009-08-05
doi: 10.5281/zenodo.18894050

Multilevel Regression Analysis for Evaluating Process-Control Systems in Ethiopian Maintenance Engineering Context
type: item
description: This study addresses a current research gap in Engineering concerning Methodological evaluation of process-control systems systems in Ethiopia: multilevel regression analysis for measuring cost-effectiveness in Ethiopia. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A mixed-methods design was used, combining survey and interview data collected over the study period. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Methodological evaluation of process-control systems systems in Ethiopia: multilevel regression analysis for measuring cost-effectiveness, Ethiopia, Africa, Engineering, original research This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
subject: Articles
  topic: section
subject: Ethiopia
  topic: keywords
subject: Multilevel Regression
  topic: keywords
subject: Hierarchical Analysis
  topic: keywords
subject: Process Control
  topic: keywords
subject: Cost-Effectiveness
  topic: keywords
subject: Statistical Methods
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subject: Quantitative Research
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creator: Yared Abebe
creator: Mikaela Degu
creator: Abay Assefa
creator: Zerihun Kebede
rights: Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
date: 2008-05-08
publication date: 2008-05-08
doi: 10.5281/zenodo.18871340

Time-Series Forecasting Model for Evaluating Transport Maintenance Depot Efficiency in Kenya
type: item
description: This study examines transport maintenance depots in Kenya, focusing on their efficiency and proposes a time-series forecasting model to evaluate these systems. A time-series analysis approach was employed using historical data from various maintenance depots. A seasonal autoregressive integrated moving average (SARIMA) model was selected for its robustness in handling temporal dependencies and seasonality commonly observed in depot operations. The model’s parameters were estimated through maximum likelihood estimation, with confidence intervals provided to account for the uncertainty inherent in forecasting. The time-series analysis revealed a significant positive trend in maintenance efficiency over recent years, indicating improvements that can be attributed to enhanced operational procedures and technological advancements. This study confirms the effectiveness of using SARIMA models for evaluating transport maintenance depot performance. The findings suggest that continued investment in technology and standardised training could further enhance depot efficiency. Based on this research, it is recommended that Kenyan transport authorities consider implementing predictive analytics to optimise depot operations and allocate resources more effectively. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
subject: Articles
  topic: section
subject: Kenya
  topic: keywords
subject: Geographic Information Systems (GIS)
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subject: Time Series Analysis
  topic: keywords
subject: Forecasting Models
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subject: Performance Evaluation
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subject: Maintenance Depot Efficiency
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subject: Econometrics
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creator: Oscar Muthoni Mutua
creator: Nancy Wanjiku Njagi
rights: Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
date: 2008-05-01
publication date: 2008-05-01
doi: 10.5281/zenodo.18871329

Rutting
topic: keywords

HDM-4
topic: keywords

LCCA
topic: keywords

SMA
topic: keywords

Articles
topic: section

Asphalt Pavement
topic: keywords

EAC Highway Corridors
topic: keywords

IRI
topic: keywords

Rutting
topic: keywords

HDM-4
topic: keywords

LCCA
topic: keywords

SMA
topic: keywords

Articles
topic: section

Asphalt Pavement
topic: keywords

EAC Highway Corridors
topic: keywords

IRI
topic: keywords

Rutting
topic: keywords

HDM-4
topic: keywords

LCCA
topic: keywords

SMA
topic: keywords

Articles
topic: section

asphalt pavement performance
topic: keywords

East African Community
topic: keywords

IRI
topic: keywords

PCI
topic: keywords

rutting
topic: keywords

pavement deterioration
topic: keywords

lifecycle cost
topic: keywords