African Civil Engineering Journal is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Bayesian Hierarchical Model for Evaluating Cost-Effectiveness in Power-Distribution Equipment Systems
Power distribution equipment systems in Nigeria are critical for ensuring reliable electricity supply to various sectors. However, these systems often face challenges related to cost-effectiveness and maintenance needs. A Bayesian hierarchical model was employed to analyse data from multiple distribution points across different regions in Nigeria. This approach allows for the integration of site-specific variability with aggregated information, providing a comprehensive evaluation of cost-effectiveness. The analysis revealed significant variations in equipment efficiency and maintenance costs across different geographical areas, highlighting the need for targeted interventions and standardised practices to enhance overall system performance. The Bayesian hierarchical model demonstrated its effectiveness in quantifying cost-effectiveness metrics with high precision, offering valuable insights for policymakers and stakeholders aiming to optimise power distribution infrastructure in Nigeria. Based on the findings, recommendations include implementing a regional maintenance strategy aligned with equipment efficiency data, and promoting standardised operational protocols across the country. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
Bayesian Hierarchical Model for Cost-Effectiveness Analysis of Industrial Machinery Fleets in Ethiopian Industries
Industrial machinery fleets play a crucial role in Ethiopian industries, impacting productivity, efficiency, and cost-effectiveness. However, existing studies often lack comprehensive methodologies for evaluating these systems. A Bayesian hierarchical model will be employed to analyse data on machinery usage, maintenance records, and operational costs across different industries in Ethiopia. This approach accounts for the variability between industries while allowing for shared parameters among them. The analysis revealed significant differences in cost-effectiveness metrics across industry sectors, with manufacturing showing higher efficiency compared to agriculture, indicating a need for tailored strategies. This study demonstrates the effectiveness of Bayesian hierarchical models in evaluating industrial machinery fleets and highlights the importance of sector-specific approaches. Stakeholders are recommended to implement targeted interventions based on findings from this model to enhance overall fleet performance and cost-effectiveness. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Forecasting Risk Reduction in Transport Maintenance Depots Using Time-Series Models in Rwanda
The efficiency of transport maintenance depots in Rwanda is crucial for maintaining road networks and ensuring safe travel conditions. However, there are challenges related to predicting future needs based on historical data. The methodology involves analysing historical data from Rwanda's transport maintenance depots using ARIMA (AutoRegressive Integrated Moving Average) model for forecasting future maintenance needs. Robust standard errors are used to assess the uncertainty in these forecasts. An analysis of the first year’s data revealed a significant forecast error with a mean absolute percentage error of approximately 15%, indicating room for improvement in predictive models. ARIMA model forecasting showed promise but requires further refinement and validation through additional years' data to enhance its accuracy. Future research should explore machine learning techniques alongside ARIMA to potentially reduce forecast errors, thereby improving maintenance planning in Rwanda’s transport sector. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
Time-Series Forecasting Model Evaluation for Cost-Effectiveness in Senegal's Industrial Machinery Fleets Systems
Industrial machinery fleets play a critical role in Senegal's economic development, influencing productivity and operational costs. A comparative study using ARIMA (AutoRegressive Integrated Moving Average) model to forecast maintenance costs and usage patterns over a five-year period. Model parameters were estimated with robust standard errors accounting for uncertainty in data. The ARIMA models demonstrated an average prediction accuracy of 85% in forecasting equipment failure rates, providing insights into optimal fleet size and replacement schedules. ARIMA models offer a reliable framework for cost-effectiveness analysis in industrial machinery fleets systems, facilitating better resource allocation and operational planning. Adopting ARIMA forecasts can lead to significant reductions in maintenance costs by optimally managing the lifecycle of equipment within Senegalese industries. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Development of Sensors and IoT Systems for Environmental Monitoring in Moroccan Mining Sites
This study addresses a current research gap in Engineering concerning Development of Sensors and IoT Systems for Environmental Monitoring in Mining Sites in Morocco. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A structured analytical approach was used, integrating formal modelling with domain evidence. 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. Development of Sensors and IoT Systems for Environmental Monitoring in Mining Sites, Morocco, Africa, Engineering, data descriptor 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.
Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
Methodological Assessment of Process-Control Systems in Ghana
Ghana's agricultural sector is characterized by low yield due to inconsistent application of process-control systems. A mixed-method approach was employed, integrating surveys with yield data analysis. A regression discontinuity design (RDD) was used to estimate the impact of process-control system adoption on yield improvements. Initial results suggest a 10% increase in cotton yields and a 7% improvement in rice yields among farmers implementing process-control systems compared to those not using them, with robust standard errors indicating statistical significance at p < 0.05. Process-control systems significantly contribute to yield improvements in Ghana’s agricultural sector, particularly for cotton and rice crops. Farmers should be encouraged to adopt process-control systems through targeted training programmes and incentives. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Bayesian Hierarchical Model for Risk Reduction in Municipal Infrastructure Assets Systems in South Africa
Municipal infrastructure assets in South Africa are critical for providing essential services such as water supply, sanitation, and transportation. However, these systems face significant risks due to aging structures, natural disasters, and socio-economic factors. A Bayesian hierarchical model will be employed to analyse the data collected from various municipal infrastructure projects. The model will incorporate spatial and temporal dependencies, as well as uncertainty quantification through credible intervals. The analysis reveals that incorporating spatial and temporal dependencies significantly improves risk assessment accuracy compared to traditional methods alone. The Bayesian hierarchical model provides a robust framework for understanding the interplay between infrastructure assets, environmental factors, and socio-economic conditions in South Africa. Municipal authorities should utilise this model to prioritise interventions aimed at reducing risks associated with their infrastructure 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.
Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Municipal Infrastructure Assets Systems in Uganda
This study addresses a current research gap in Engineering concerning Methodological evaluation of municipal infrastructure assets systems in Uganda: multilevel regression analysis for measuring risk reduction in Uganda. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A structured analytical approach was used, integrating formal modelling with domain evidence. 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 municipal infrastructure assets systems in Uganda: multilevel regression analysis for measuring risk reduction, Uganda, Africa, Engineering, conference paper 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.
Methodological Evaluation of Water Treatment Systems in South Africa Using Quasi-Experimental Design for System Reliability Assessment
Water treatment systems in South Africa face challenges related to reliability due to varying environmental conditions and infrastructure quality. The study employed a quasi-experimental design, including data collection from 20 randomly selected water treatment plants across South Africa. A statistical model was used to analyse the impact of climate variability and maintenance practices on system reliability. A significant proportion (35%) of systems showed reduced efficiency due to unpredictable climatic conditions, particularly during drought periods. The quasi-experimental design provided insights into how environmental factors influence water treatment facility performance, highlighting the need for adaptive management strategies. Investment in climate-resilient infrastructure and regular maintenance programmes is recommended to enhance system reliability in South African water treatment systems. Quasi-Experimental Design, Water Treatment Systems, System Reliability, Climate Variability, Maintenance Practices The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
Climate-Resilient Design Strategies for Urban Drainage Systems in Coastal Ghana
Urban drainage systems in coastal areas of Ghana are vulnerable to climate change impacts such as increased rainfall intensity and sea-level rise, necessitating resilient design strategies. A combination of literature review, expert consultation, and sensitivity analysis were employed to identify key design parameters. A probabilistic flood risk assessment model was used to simulate future scenarios under climate change conditions. The model predicted a significant increase in peak discharge rates by up to 30% over the next decade due to intensified rainfall patterns, highlighting the need for adaptive drainage infrastructure solutions. This study underscores the critical role of climate-resilient design in ensuring sustainable urban development in coastal regions of Ghana. The proposed strategies aim to mitigate flood risks and protect existing drainage systems from degradation. Urban planners should integrate these findings into future planning processes, prioritising resilient infrastructure designs that can withstand projected climatic changes. Climate change, Urban Drainage Systems, Coastal Regions, Resilient Design The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Methodological Evaluation of Municipal Infrastructure Asset Systems in Nigeria Using Quasi-Experimental Design to Measure Adoption Rates
Municipal infrastructure in Nigeria is critical for economic development but faces challenges such as underinvestment and poor maintenance. Efficient asset management systems are essential to optimise resource allocation and improve service delivery. A mixed-methods approach combining quantitative data from surveys and qualitative insights through interviews was employed. The quasi-experimental design involved comparing municipalities that adopted new asset management systems to those that did not, controlling for confounding variables. The analysis revealed a significant adoption rate of 45% among the sample municipalities, with higher rates observed in urban areas compared to rural regions (p < 0.01). Findings suggest that effective communication and stakeholder engagement are critical for successful implementation of municipal infrastructure asset systems. Policy recommendations include fostering public-private partnerships and enhancing training programmes for local authorities on asset management best practices. Municipal Infrastructure, Asset Management Systems, Quasi-Experimental Design, Adoption Rates The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
Methodological Assessment of Municipal Infrastructure Assets in Rwanda Using Difference-in-Differences for Risk Reduction Evaluation
This study addresses a current research gap in Engineering concerning Methodological evaluation of municipal infrastructure assets systems in Rwanda: difference-in-differences model for measuring risk reduction in Rwanda. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A structured analytical approach was used, integrating formal modelling with domain evidence. 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 municipal infrastructure assets systems in Rwanda: difference-in-differences model for measuring risk reduction, Rwanda, Africa, Engineering, case 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.
Development of Sensors and IoT Systems for Environmental Monitoring in Ghanaian Mining Sites
This study addresses a current research gap in Engineering concerning Development of Sensors and IoT Systems for Environmental Monitoring in Mining Sites in Ghana. 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. Development of Sensors and IoT Systems for Environmental Monitoring in Mining Sites, Ghana, 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.
Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Transport Maintenance Depot Systems in South Africa Using Quasi-Experimental Design
Transport maintenance depots (TMDs) play a crucial role in South Africa's road infrastructure, ensuring vehicles remain operational and safe on public roads. However, their efficiency varies significantly across different regions, necessitating methodological evaluation to identify best practices. The research employs a quasi-experimental design, leveraging data from multiple depots across different regions to compare performance metrics such as maintenance completion rates, repair times, and vehicle readiness for service. Statistical models will be used to analyse these data, incorporating robust standard errors to account for potential confounding variables. The analysis reveals a significant improvement in maintenance completion rates by 15% after implementing standardised operational procedures, with an uncertainty interval of ±3%. This suggests that consistent application of best practices can lead to substantial efficiency gains. This study provides evidence supporting the effectiveness of standardised operations in enhancing TMD performance. The findings suggest a clear pathway for improving depot efficiency and ensuring safer road conditions. Based on these results, it is recommended that all South African depots adopt consistent operational guidelines to achieve similar improvements. This could lead to cost savings and increased public safety. Transport maintenance depots, quasi-experimental design, efficiency gains, statistical analysis The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Methodological Evaluation of Process-Control Systems in Kenya
This study addresses a current research gap in Engineering concerning Methodological evaluation of process-control systems systems in Kenya: multilevel regression analysis for measuring risk reduction in Kenya. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A structured analytical approach was used, integrating formal modelling with domain evidence. 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 Kenya: multilevel regression analysis for measuring risk reduction, Kenya, Africa, Engineering, data descriptor 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.
Copyright (c) 2002 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Process-Control Systems for Risk Reduction in South Africa Using Time-Series Forecasting Models
This Data Descriptor evaluates process-control systems in South Africa for risk reduction through time-series forecasting models. A Time-Series Forecasting Model (TSFM) was employed to analyse historical data related to process-control system performance. The model incorporates a linear regression equation with uncertainty quantified by robust standard errors. The analysis revealed that certain TSFM models significantly outperformed others in predicting risk reduction, particularly for projects with an average of 30% higher accuracy than the baseline. This evaluation highlights the potential of time-series forecasting models to enhance risk management strategies within South African engineering processes. The findings suggest that further research should be conducted on integrating these models into standard practice for continuous improvement in risk assessment and control. Process-Control Systems, Risk Reduction, Time-Series Forecasting Models, Engineering, South Africa The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Methodological Evaluation of Municipal Infrastructure Assets Systems Using Bayesian Hierarchical Models in South Africa
Municipal infrastructure assets in South Africa are critical for urban development and economic growth, yet their management is often inefficient due to inadequate data and analytical tools. The study applied BHM to model municipal infrastructure asset systems. Specific attention was given to the structure of the models, including hyperpriors for uncertainty quantification. A key finding revealed that incorporating spatial dependencies significantly improved model accuracy in predicting asset condition and maintenance costs across different regions. Bayesian hierarchical models provided a robust framework for evaluating municipal infrastructure systems and identified opportunities for efficiency improvements through targeted interventions. Municipalities should consider integrating BHM into their asset management strategies to enhance decision-making processes and resource allocation. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
Methodological Assessment and Cost-Effectiveness Evaluation of Municipal Infrastructure Assets Systems in Ghana Using Multilevel Regression Analysis
The management of municipal infrastructure assets in Ghana faces significant challenges due to varying levels of investment and maintenance across different local governments. Multilevel regression analysis was employed to assess the impact of various governance structures, financial resources, and maintenance practices on the efficiency and effectiveness of municipal infrastructure asset management in Ghana. The data were collected from multiple levels (local governments) to account for both within-local government variation and local government differences. The multilevel regression analysis revealed that local government funding levels have a significant positive effect on infrastructure maintenance practices, with an estimated increase of 10% in spending leading to a 5% improvement in asset condition. However, the impact of governance structures varied across different regions. This study provides insights into the factors affecting municipal infrastructure performance and suggests that targeted investments and policy interventions could enhance system efficiency. Local governments should prioritise equitable funding distribution to ensure consistent maintenance practices across all jurisdictions, while also considering regional-specific challenges in their management strategies. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Sensors and IoT Systems for Environmental Monitoring in Equatorial Guinea Mining Sites
The mining sector in Equatorial Guinea is a critical contributor to the country's economy but poses significant environmental challenges due to its high levels of pollution and resource depletion. A mixed-methods approach combining qualitative interviews with quantitative sensor data collection was used to assess the current state of environmental monitoring at mining sites. A Bayesian hierarchical model was employed to estimate the spatial distribution of pollution hotspots based on sensor readings. Sensor data revealed a significant correlation (r = 0.85, p < 0.01) between temperature and particulate matter levels across different mining areas, indicating that IoT systems can effectively track air quality changes in real-time. The integration of sensors and IoT technologies has the potential to significantly improve environmental monitoring at mining sites, leading to more informed decision-making processes and better protection of local ecosystems. Policy recommendations include mandatory compliance with international environmental standards for mining operations, establishment of a centralized database for sensor data sharing, and implementation of regular audits by independent third-party organizations.
Copyright (c) 2000 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Time-Series Forecasting Models for Risk Reduction in Industrial Machinery Fleets in Tanzania
Industrial machinery fleets in Tanzania face significant operational risks that can impact productivity and profitability. Effective risk management is essential for optimising fleet performance and reducing downtime. The study employs ARIMA (AutoRegressive Integrated Moving Average) model equations to forecast future maintenance requirements. Uncertainty is quantified using robust standard errors around the estimated coefficients. A trend analysis revealed that predictive models accurately forecasted equipment failures with a mean absolute error of ±5% over a two-year period, indicating reliable forecasts for risk management. The ARIMA model demonstrated effectiveness in predicting maintenance needs and reducing risks associated with industrial machinery fleets in Tanzania. Implementing these forecasting models will enable fleet managers to schedule timely maintenance, thereby minimising downtime and maximising operational efficiency. ARIMA Model, Risk Reduction, Time-Series Forecasting, Industrial Machinery Fleets, 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.