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.
Structural Performance ofTimber-Composite Bridges forRural Connectivity in South Sudan
South Sudan has an estimated rural bridge deficit of 680 crossings below 25 m span, representing the primary physical barrier to year-round agricultural market access, emergency humanitarian logistics, and community healthcare connectivity for approximately 4.2 million rural inhabitants. Conventional reinforced concrete and structural steel bridge solutions are prohibitively costly in this context — averaging USD 162,000 and USD 195,000 per linear metre respectively — due to the absence of domestic cement and steel production, high import logistics costs across landlocked supply chains, and the unavailability of skilled concrete construction labour in rural districts. Locally available timber species — including Swietenia macrophylla (mahogany), Tectona grandis (teak), Eucalyptus saligna, and cultivated Dendrocalamus giganteus bamboo — offer a structurally and economically viable alternative when configured as engineered timber-composite bridge systems combining glulam or laminated veneer lumber (LVL) primary girders with reinforced concrete deck overlays or steel flitch plate composites. This paper presents the structural performance characterisation of three timber-composite bridge types — TC-1 (glulam + RC deck), TC-2 (sawn timber + steel flitch plate), and TC-3 (bamboo LVL + GFRP composite) — through laboratory flexural testing of 24 full-scale beam specimens, FEA modelling in ABAQUS 2022, field load testing of six deployed bridges with spans of 8–16 m, and life-cycle cost analysis over a 50-year service horizon. TC-1 achieves an effective flexural stiffness of EI_eff = 6.85×10¹⁰ N·mm², a design moment resistance of M_Rd = 42.2 kN·m, and a self-weight ratio of 2.4 relative to an equivalent RC T-beam — enabling deployment by community labour with locally hired motori
Copyright (c) 2026 African Civil Engineering Journal
Structural Assessment of Petroleum Pipeline Crossings Over Flood-Prone Road Corridors in South Sudan
Petroleum pipeline crossings over road corridors represent structurally critical and economically vital infrastructure intersections in South Sudan, a nation where oil revenues constitute more than 90% of government income and where national road networks are subjected to severe seasonal flooding driven by the Sudd wetland hydrological system and intensifying climate variability. This study presents a comprehensive structural assessment framework for both aerial and buried petroleum pipeline crossings over flood-prone road corridors in South Sudan's three principal oil-producing states: Jonglei, Upper Nile, and Unity. Using three-dimensional finite element modelling (FEM) in ABAQUS/Standard, limit state design analysis, scour depth estimation, and field-calibrated hydraulic loading, eight representative crossing sites were evaluated under governing combined load combinations incorporating dead loads, traffic loads, hydrodynamic flood forces, internal operating pressure, and cyclic Vertisol shrink-swell soil loads. Results indicate that unanchored aerial crossings at Jonglei sites experience maximum Von Mises stresses of up to 312 MPa — reaching 87% of the API 5L Grade X52 yield strength — under peak flood and wind loading. Buried crossings are subjected to scour depths of 1.9–2.8 m that exceed minimum burial requirements and generate net uplift forces of up to 4.4 kN/m. A novel composite Pipeline-Road Crossing Vulnerability Index (PRCVI) is proposed, integrating structural, hydraulic, and geotechnical parameters through an Analytical Hierarchy Process (AHP)-weighted formulation. The PRCVI classifies two sites as High Risk and two as Moderate Risk, providing a scalable prioritisation tool for the SSNRA and pipeline operators. Targeted rehabilitation recommendations are p
Prioritization of Road Rehabilitation in Post-Conflict South Sudan Using Multi-Criteria Decision Analysis
Decades of civil conflict have left South Sudan with one of the most severely degraded road networks in sub-Saharan Africa, with fewer than 250 km of paved roads across a country of approximately 644,329 km². The post-conflict reconstruction challenge is compounded by severely constrained public financial resources, complex political geography, and extreme seasonal flooding that renders up to 40% of the road network inaccessible for extended periods. Effective prioritisation of limited rehabilitation budgets across competing road segments demands a rigorous, transparent, and defensible decision framework. This study develops and applies a Multi-Criteria Decision Analysis (MCDA) framework integrating the Analytic Hierarchy Process (AHP) with a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to prioritise road rehabilitation investments across 32 candidate road segments in South Sudan’s Central Equatoria, Jonglei, and Upper Nile states. Six evaluation criteria are considered: road condition index (RCI), traffic volume (AADT), socio-economic connectivity, flood vulnerability, cost-benefit ratio, and post-conflict accessibility urgency. AHP-derived weights are validated through sensitivity analysis and expert panel consultation (n = 7 engineers). The MCDA framework identifies 8 high-priority segments totalling 412 km and estimates optimal allocation of a hypothetical USD 180 million rehabilitation budget. Results are validated against Independent Roads Needs Assessment (IRNA) survey data, achieving 81.3% ranking concordance. The framework provides a replicable, evidence-based decision support tool for the Ministry of Roads and Bridges (SSNRA) and international development partners.
: Petroleum pipeline crossings over road corridors represent structurally critical and economically vital infrastructure intersections in South Sudan, a nation where oil revenues constitute more than 90% of government income and where national road networks are subjected to severe seasonal flooding driven by the Sudd wetland hydrological system and intensifying climate variability. This study presents a comprehensive structural assessment framework for both aerial and buried petroleum pipeline crossings over flood-prone road corridors in South Sudan's three principal oil-producing states: Jonglei, Upper Nile, and Unity. Using three-dimensional finite element modelling (FEM) in ABAQUS/Standard, limit state design analysis, scour depth estimation, and field-calibrated hydraulic loading, eight representative crossing sites were evaluated under governing combined load combinations incorporating dead loads, traffic loads, hydrodynamic flood forces, internal operating pressure, and cyclic Vertisol shrink-swell soil loads. Results indicate that unanchored aerial crossings at Jonglei sites experience maximum Von Mises stresses of up to 312 MPa — reaching 87% of the API 5L Grade X52 yield strength — under peak flood and wind loading. Buried crossings are subjected to scour depths of 1.9–2.8 m that exceed minimum burial requirements and generate net uplift forces of up to 4.4 kN/m. A novel composite Pipeline-Road Crossing Vulnerability Index (PRCVI) is proposed, integrating structural, hydraulic, and geotechnical parameters through an Analytical Hierarchy Process (AHP)-weighted formulation. The PRCVI classifies two sites as High Risk and two as Moderate Risk, providing a scalable prioritisation tool for the SSNRA and pipeline operators. Targeted rehabilitation recommendations are
Copyright (c) 2026 AJE-CivilEng https://creativecommons.org/licenses/by/4.0/
Comparative Field Trial of Maintenance Regimes for Industrial Machinery Fleets in Kenya
{ "background": "Maintenance regimes for industrial machinery fleets in sub-Saharan Africa are often based on fixed schedules or reactive practices, with limited empirical evaluation of their effectiveness in mitigating operational risks within specific regional contexts.", "purpose and objectives": "This study aimed to methodologically evaluate the comparative efficacy of a novel risk-based maintenance (RBM) regime against conventional time-based maintenance (TBM) for industrial machinery fleets, with the primary objective of quantifying associated reductions in critical failure risk.", "methodology": "A randomised field trial was conducted using a fleet of 42 heavy-duty vehicles from three industrial sites. Units were randomly assigned to either the RBM or TBM group. The RBM intervention utilised a proportional hazards model, $\\lambda(t|X) = \\lambda0(t) \\exp(\\beta1 X1 + \\beta2 X_2)$, to prioritise tasks based on real-time risk indicators. Key performance metrics, including mean time between critical failures (MTBCF) and downtime cost, were monitored over an operational period.", "findings": "The risk-based regime reduced the incidence of critical failures by 32% (95% CI: 24% to 40%) compared to the time-based approach. Furthermore, analysis using robust standard errors indicated a statistically significant reduction in total unplanned downtime costs per vehicle for the RBM cohort.", "conclusion": "The risk-based methodology demonstrated superior performance in mitigating high-consequence failure events within the studied operational environment, proving its practical viability for asset-intensive industries in the region.", "recommendations": "Industrial operators should adopt risk-based maintenance frameworks, supported by continuous data collection on machine condition and failure history, to optimise resource allocation and improve fleet reliability.", "key words": "risk-based maintenance, field trial, machinery fleet, proportional hazards model, comparative study, asset management", "contribution statement": "This study provides the first randomised field-trial evidence for the efficacy of a formal risk-based maintenance methodology applied to
Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation and Risk Reduction in Uganda's Power-Distribution System
Power-distribution systems in many developing nations face significant reliability challenges due to ageing infrastructure and environmental stressors. There is a pressing need for robust methodological frameworks to evaluate equipment performance and quantify the impact of interventions. This study aims to develop and apply a multilevel regression methodology to evaluate the performance of power-distribution equipment and measure the efficacy of targeted risk-reduction strategies within a national grid. We analysed a longitudinal dataset of equipment failures and maintenance records from a national utility. A three-level hierarchical linear model was specified: $y_{ij} = \beta_{0j} + \beta_{1j}X_{ij} + u_{0j} + u_{1j}X_{ij} + e_{ij}$, where levels represented components, feeders, and geographical regions. Model parameters were estimated using restricted maximum likelihood with robust standard errors. The multilevel analysis revealed that targeted insulator replacement programmes reduced the likelihood of weather-related outages by 34% (95% CI: 28% to 40%). Component age and vegetation encroachment were the most significant predictors of failure at the feeder level. The methodological approach provides a statistically rigorous framework for isolating the effects of specific interventions from systemic and regional variations, confirming that data-driven maintenance strategies can substantially enhance grid resilience. Utilities should adopt hierarchical modelling for asset management to prioritise investments. Regulatory frameworks should incentivise the collection and analysis of granular, component-level performance data. asset management, hierarchical linear model, infrastructure resilience, power distribution, predictive maintenance This paper presents a novel application of multilevel regression to disaggregate system-wide risk in electrical grids, providing utility managers with a precise tool for measuring the marginal benefit of specific equipment interventions.
Copyright (c) 2012 https://creativecommons.org/licenses/by/4.0/
A Randomised Field Trial Methodology for Risk Reduction in Nigerian Transport Depot Maintenance Systems
{ "background": "Maintenance systems in transport depots are critical for operational safety and efficiency, yet they are often managed using ad-hoc, reactive approaches. In Nigeria, the lack of structured, evidence-based methodologies for evaluating and improving these systems contributes to persistent operational risks and infrastructure failures.", "purpose and objectives": "This article presents a novel methodological framework for conducting randomised field trials (RFTs) specifically designed to evaluate engineering interventions within transport depot maintenance systems. The primary objective is to provide a rigorous protocol for measuring causal impacts on risk reduction metrics.", "methodology": "The proposed RFT methodology employs a cluster-randomised, stepped-wedge design where depots are randomised to receive a systematised preventive maintenance intervention at different sequential steps. The core impact is estimated using a generalised linear mixed model: $\\logit(P(Y{it}=1)) = \\beta0 + \\beta T{it} + \\gammat + ui + \\epsilon{it}$, where $Y{it}$ is a binary failure event for depot $i$ at time $t$, $T{it}$ is the treatment indicator, $\\gammat$ are time-fixed effects, and $ui$ are depot random effects. Inference is based on cluster-robust standard errors.", "findings": "As a methodology article, it presents no empirical results from a completed trial. However, the framework's simulation studies, based on parameter estimates from pilot data, indicate that the design can detect a minimum 30% reduction in critical failure rates with 80% power. A key thematic finding from the protocol development is the critical importance of embedding lead engineers as co-researchers to ensure intervention fidelity.", "conclusion": "The developed RFT protocol provides a viable and rigorous experimental framework for generating high-quality evidence on engineering interventions in complex, real-world depot environments. It addresses specific challenges such as contamination between units and sequential rollout constraints.", "recommendations": "Researchers and depot engineers should adopt this structured RFT approach to move
Copyright (c) 2017 https://creativecommons.org/licenses/by/4.0/
A Time-Series Forecasting Model for Reliability Diagnostics in Ghana's Transport Maintenance Depot Systems
The reliability of transport maintenance depot systems is a critical, yet under-modelled, component of national infrastructure policy in many developing economies. Persistent operational failures within these systems undermine transport network efficiency and economic development. This policy analysis develops and evaluates a novel time-series forecasting model to diagnose the reliability of transport maintenance depot systems. It aims to provide a robust, evidence-based tool for informing infrastructure maintenance policy and investment. The analysis employs a Seasonal Autoregressive Integrated Moving Average with Exogenous variables (SARIMAX) model, formalised as $\phi(B)\Phi(B^s)\nabla^d\nabla_s^D y_t = \theta(B)\Theta(B^s)\epsilon_t + \beta X_t$, applied to historical operational performance data. Model diagnostics include analysis of robust standard errors to assess parameter stability. The model forecasts a significant downward trend in systemic reliability, with a projected 22% increase in mean time between failures for critical depot machinery over the forecast horizon. Parameter estimates for maintenance budget allocation were statistically significant at the 95% confidence level, indicating a strong policy lever. The forecasting model provides a quantitatively rigorous diagnostic framework, revealing that current maintenance policies are insufficient to prevent a decline in depot system reliability. This necessitates a strategic policy revision. Policy must shift towards predictive, data-driven maintenance scheduling informed by the forecasting model. Immediate recommendations include ring-fencing budgetary allocations for pre-emptive component replacement and establishing a centralised reliability monitoring unit. infrastructure reliability, maintenance policy, SARIMAX, predictive maintenance, transport engineering This article provides the first application of a SARIMAX forecasting model for reliability diagnostics in transport depot systems, offering a novel evidence-based tool for infrastructure policy formulation.
Copyright (c) 2018 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation and Cost-Effectiveness of Industrial Machinery Fleet Systems in Nigeria
{ "background": "The management of industrial machinery fleets is critical for infrastructure development, yet robust empirical evidence on the cost-effectiveness of systematic fleet management in developing economies is scarce. Existing evaluations often lack rigorous counterfactual analysis.", "purpose and objectives": "This study aims to methodologically evaluate the impact of implementing structured fleet management systems on operational costs and to assess their cost-effectiveness within the Nigerian construction and industrial sectors.", "methodology": "A quasi-experimental difference-in-differences (DiD) design was employed, analysing panel data from firms with and without formalised systems. The core model is $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\delta (\\text{Treat}i \\times \\text{Post}t) + \\epsilon_{it}$, where $\\delta$ captures the causal effect. Inference is based on cluster-robust standard errors at the firm level.", "findings": "The DiD estimator revealed that firms adopting structured fleet systems achieved a statistically significant reduction in average monthly maintenance costs of 18.7% (95% CI: -22.3%, -15.1%) relative to the control group. The benefit-cost ratio for implementation was estimated at 3.2 over the study period.", "conclusion": "Formalised fleet management systems are a cost-effective intervention for industrial machinery operations, leading to substantial and significant reductions in operational expenditures.", "recommendations": "Industry stakeholders should prioritise investment in integrated fleet management systems. Policymakers are encouraged to develop frameworks and incentives to support their adoption across the sector.", "key words": "fleet management, difference-in-differences, cost-effectiveness, industrial machinery, maintenance, Nigeria", "contribution statement": "This paper provides the first application of a quasi-experimental DiD framework to isolate the causal effect of fleet management systems on costs in this context, introducing a robust methodological approach for
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
A Bayesian Hierarchical Model for Risk Reduction in South African Process-Control System Diagnostics
{ "background": "Process-control systems in critical national infrastructure are increasingly complex and vulnerable. Traditional diagnostic methods often fail to adequately quantify and propagate uncertainty in system performance, leading to suboptimal maintenance and risk management strategies.", "purpose and objectives": "This research aimed to develop and evaluate a novel Bayesian hierarchical modelling framework to improve diagnostic accuracy and quantify risk reduction for industrial process-control systems. The objective was to provide a robust probabilistic tool for engineers to prioritise maintenance interventions.", "methodology": "A Bayesian hierarchical model was formulated, integrating diagnostic test data with expert prior knowledge on component failure rates. The core model structure is $y{ij} \\sim \\text{Bernoulli}(\\theta{ij}), \\; \\text{logit}(\\theta{ij}) = \\alphai + \\beta X{ij}$, where $\\alphai \\sim \\text{Normal}(\\mu, \\sigma^2)$ represents plant-specific random effects. Inference was performed using Hamiltonian Monte Carlo sampling, with posterior credible intervals used for all risk estimates.", "findings": "Application to diagnostic data from three mineral processing plants demonstrated a quantifiable reduction in diagnostic uncertainty. The 95% posterior credible interval for mean system failure risk narrowed by approximately 34% compared to conventional frequentist methods, allowing for more precise identification of high-priority components.", "conclusion": "The proposed model provides a statistically rigorous framework for risk assessment that formally incorporates both data and engineering judgement. It represents a significant methodological advance over deterministic or simple probabilistic models currently in use.", "recommendations": "Adoption of this Bayesian hierarchical approach is recommended for asset management in sectors reliant on complex process-control systems. Further work should focus on developing user-friendly software interfaces to facilitate implementation by practising engineers.", "key words": "Bayesian inference, risk assessment, predictive maintenance, industrial diagnostics, probabilistic modelling", "contribution statement": "This paper presents a novel Bayesian hierarchical model that uniquely integrates multi-plant operational data with expert elicitation to reduce uncertainty in process-control system
Copyright (c) 2003 https://creativecommons.org/licenses/by/4.0/
A Bayesian Hierarchical Model for Reliability Diagnostics and Maintenance Policy in Rwandan Water Treatment Systems
{ "background": "The reliability of water treatment infrastructure is a critical determinant of public health and economic development. In many developing nations, maintenance policies are often reactive, leading to inefficient resource allocation and service disruption. A systematic, data-driven approach to reliability assessment is therefore essential for effective infrastructure management.", "purpose and objectives": "This policy analysis article develops and evaluates a novel Bayesian hierarchical model to diagnose reliability and inform maintenance strategies for water treatment systems. The primary objective is to provide a robust methodological framework for quantifying system failure risks and optimising intervention schedules.", "methodology": "We formulate a Bayesian hierarchical model where the failure rate $\lambda{ij}$ for component $i$ in plant $j$ is modelled as $\lambda{ij} \\sim \\text{Gamma}(\\alphaj, \\betaj)$, with hyperpriors on $\\alphaj$ and $\\betaj$ to pool information across facilities. Inference is performed using Hamiltonian Monte Carlo, with posterior distributions quantifying uncertainty in reliability metrics. The model is applied to operational and maintenance data from a network of treatment facilities.", "findings": "The analysis reveals substantial heterogeneity in subsystem reliability, with filtration units exhibiting a posterior probability of 0.85 of having a higher failure rate than coagulation units. The 95% credible interval for the mean time between failures for pumping systems was estimated to be [42, 58] days. The hierarchical structure effectively identified outlier plants requiring prioritised intervention.", "conclusion": "The proposed model provides a statistically rigorous tool for moving from reactive to condition-based maintenance policies. It successfully integrates sparse, heterogeneous data to produce actionable reliability diagnostics at both system and component levels.", "recommendations": "Policy should mandate the standardised collection of failure and maintenance data to feed such models. Infrastructure investment plans should be informed by probabilistic reliability assessments, and maintenance budgets should be allocated based on quantified posterior risk, not just historical expenditure.", "key words": "Infrastructure reliability, Bayesian statistics, maintenance optimisation, water treatment, hierarchical modelling, policy analysis
Copyright (c) 2014 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation and Panel-Data Estimation for Power-Distribution Risk Reduction in Senegal
{ "background": "Power-distribution infrastructure in many developing nations faces significant reliability and safety challenges due to ageing assets and operational stresses. A systematic, data-driven methodology for evaluating equipment risk and forecasting the impact of interventions is required to optimise capital expenditure and enhance grid resilience.", "purpose and objectives": "This case study aims to develop and apply a novel methodological framework for the evaluation of power-distribution equipment. Its core objective is to estimate the causal effect of systematic equipment replacement and maintenance programmes on technical risk reduction within a national utility.", "methodology": "A panel-data econometric model is constructed using utility operational and asset data. The model, $Risk{it} = \\beta0 + \\beta1 Intervention{it} + \\beta2 X{it} + \\alphai + \\epsilon{it}$, estimates risk reduction, where $\\alpha_i$ denotes transformer-unit fixed effects and robust standard errors are clustered at the feeder level. The analysis integrates technical failure data, environmental variables, and maintenance records.", "findings": "The panel-data estimation indicates a statistically significant reduction in aggregate technical risk following targeted interventions. A one-standard-deviation increase in the intervention index is associated with a 22% decrease in the predicted probability of a major fault (95% CI: 18% to 26%). The analysis identifies transformer overloading and environmental corrosion as the dominant residual risk factors.", "conclusion": "The methodological framework provides a robust, evidence-based tool for prioritising infrastructure investment. The results confirm that structured intervention programmes can substantially mitigate distribution network risk, though persistent environmental and load-related challenges require ongoing management.", "recommendations": "Utilities should adopt panel-data estimation for long-term investment planning and performance tracking. It is recommended to increase sensor deployment for critical environmental and load data to refine the model. Future programmes should target regions with high corrosion exposure as a priority.", "key words": "power distribution, risk assessment, panel data, infrastructure management, econometric modelling, grid resilience", "cont
Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation and Cost-Effectiveness Analysis of Senegal's Power-Distribution Equipment Using a Difference-in-Differences Model
{ "background": "The evaluation of power-distribution infrastructure in developing economies often lacks robust, quantitative frameworks for assessing the cost-effectiveness of equipment upgrades and interventions. This creates challenges for evidence-based investment planning and maintenance strategies within the utility sector.", "purpose and objectives": "This Data Descriptor presents a methodological framework and a novel, curated dataset designed to enable the rigorous evaluation of power-distribution equipment in Senegal. The primary objective is to provide a replicable model for measuring the cost-effectiveness of technical interventions, supporting data-driven decision-making in electrical engineering and utility management.", "methodology": "The core analytical approach is a difference-in-differences (DiD) model, specified as $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\delta (\\text{Treat}i \\times \\text{Post}t) + \\epsilon{it}$, where $Y{it}$ is the cost-effectiveness metric for unit $i$ at time $t$. The dataset comprises panel data on equipment performance, failure rates, maintenance, and capital costs, aggregated from utility records and field surveys. Inference is based on robust standard errors clustered at the feeder level.", "findings": "The application of the DiD model to the provided dataset reveals a statistically significant positive treatment effect, indicating that targeted equipment upgrades improved cost-effectiveness. A key concrete result is an estimated average treatment effect on the treated (ATT) of 18.5% reduction in total cost per unit of reliable energy delivered, with a 95% confidence interval of [14.2%, 22.8%].", "conclusion": "The developed methodology and accompanying dataset establish a valid and practical framework for the econometric evaluation of power-distribution equipment investments. The DiD model effectively isolates the causal impact of interventions from underlying trends.", "recommendations": "Utility engineers and planners should adopt similar panel data structures and quasi-experimental evaluation techniques for capital project appraisal
Methodological Evaluation and Panel-Data Estimation of Cost-Effectiveness in South African Manufacturing Plant Systems
{ "background": "The persistent challenge of low productivity and high operational costs within the nation's manufacturing sector necessitates robust, data-driven methodologies for evaluating plant system efficiency. Existing cost-effectiveness analyses often rely on cross-sectional data, which fails to account for unobserved heterogeneity and dynamic performance changes over time.", "purpose and objectives": "This case study aims to methodologically evaluate the application of panel-data econometric techniques for measuring cost-effectiveness in manufacturing plant systems. Its objective is to demonstrate a replicable framework that isolates true efficiency gains from transient shocks and firm-specific effects.", "methodology": "A longitudinal dataset from multiple manufacturing plants was analysed using a fixed-effects panel regression model. The core specification was $\\ln(\\text{Unit Cost}){it} = \\alphai + \\beta1 \\ln(\\text{Output}){it} + \\beta2 \\text{TechIndex}{it} + \\epsilon{it}$, where $\\alphai$ captures plant-specific fixed effects. Inference was based on robust standard errors clustered at the plant level.", "findings": "The panel estimation revealed that a one-unit increase in the technology adoption index was associated with a 4.7% reduction in unit cost, a relationship obscured in pooled OLS estimates. The fixed effects were statistically significant, confirming substantial unobserved heterogeneity across plants.", "conclusion": "Panel-data methods provide a superior methodological framework for cost-effectiveness evaluation in this context, controlling for biases inherent in simpler analytical approaches. This leads to more accurate identification of the drivers of engineering and operational efficiency.", "recommendations": "Industry practitioners and engineering managers should adopt panel-data frameworks for internal performance benchmarking. Future research should integrate engineering system parameters directly into the econometric specification.", "key words": "panel data, fixed effects, cost-effectiveness, manufacturing systems, operational efficiency, econometric modelling", "contribution statement": "This study provides a novel applied framework for integrating panel-data econometrics into engineering management practice, demonstrating that accounting for unobserved plant heterogeneity reverses the
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation and Reliability Assessment of Process-Control Systems in Uganda
{ "background": "Process-control systems are critical for infrastructure and industrial operations, yet their methodological evaluation in developing contexts lacks rigorous, comparative frameworks. In Uganda, ad hoc assessments have predominated, creating a gap for robust, quantitative reliability analysis.", "purpose and objectives": "This study aims to methodologically evaluate process-control systems by developing and applying a comparative difference-in-differences (DiD) model to assess system reliability. The objective is to quantify the causal impact of system upgrades on reliability metrics.", "methodology": "A quasi-experimental comparative study was conducted, analysing performance data from treatment and control groups of systems before and after interventions. The core statistical model is a two-way fixed effects DiD specification: $Y{it} = \\alpha + \\beta (Treati \\times Postt) + \\gammai + \\deltat + \\epsilon{it}$, where robust standard errors were clustered at the system level. Reliability was measured via mean time between failures (MTBF).", "findings": "The DiD estimator revealed that upgraded systems exhibited a statistically significant 23.4% increase in MTBF compared to the control group (β = 0.211, 95% CI: 0.187, 0.235). The parallel trends assumption was validated using pre-intervention data.", "conclusion": "The application of a DiD model provides a rigorous methodological framework for evaluating process-control systems, demonstrating a significant causal improvement in reliability following targeted upgrades.", "recommendations": "Adopt the DiD methodology for longitudinal performance evaluation of engineering systems. Prioritise investments in control-system upgrades that mirror the intervention characteristics linked to the highest reliability gains identified in this study.", "key words": "process control, reliability engineering, difference-in-differences, quasi-experimental design, infrastructure management", "contribution statement": "This paper provides a novel application of econometric causal inference methodology, specifically the difference-in-differences model, to the field of engineering system reliability assessment, offering a
Copyright (c) 2021 https://creativecommons.org/licenses/by/4.0/
Replication and Panel-Data Analysis of Water Treatment System Reliability in South Africa, 2000–2024
The reliability of water treatment systems is a critical infrastructure challenge. Previous national assessments have often relied on cross-sectional data, limiting the ability to analyse temporal trends and control for unobserved heterogeneity across facilities. This study replicates and extends a prior national assessment using a longitudinal framework. Its primary objective is to apply panel-data methods to estimate system reliability, isolating time-invariant facility-specific effects from operational performance trends. We construct a facility-level panel dataset from operational performance records. Reliability is modelled as a function of maintenance expenditure, operator training, and asset age using a two-way fixed effects estimator: $Reliability_{it} = \alpha_i + \lambda_t + \beta X_{it} + \epsilon_{it}$, where $\alpha_i$ and $\lambda_t$ are facility and year fixed effects. Inference is based on robust standard errors clustered at the municipal level. A 10% increase in preventative maintenance expenditure is associated with a 2.3 percentage point increase in system reliability (95% CI: 1.7 to 2.9). The facility fixed effects account for over 40% of the variation in the outcome, underscoring the importance of unobserved, time-invariant factors like design and location. Panel-data estimation provides a more rigorous methodological foundation for evaluating water treatment system performance by controlling for persistent, unobserved differences between facilities. National monitoring programmes should adopt longitudinal data collection protocols. Infrastructure investment decisions must account for significant baseline differences between facilities, which are revealed by the panel approach. infrastructure reliability, panel data, fixed effects, water treatment, maintenance, South Africa This study provides the first application of a two-way fixed effects panel model to national water treatment reliability data, demonstrating that a substantial portion of performance variation is attributable to time-invariant facility characteristics.
Copyright (c) 2002 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation and Efficiency Gains of Process-Control Systems in Tanzania
{ "background": "The adoption of automated process-control systems in industrial and infrastructure projects is increasing across sub-Saharan Africa. However, rigorous empirical evidence quantifying their operational efficiency gains within local contexts remains scarce.", "purpose and objectives": "This study aims to methodologically evaluate the impact of implementing modern process-control systems on project efficiency. The primary objective is to estimate the causal effect on key performance metrics, isolating the technology's contribution from other confounding factors.", "methodology": "A quasi-experimental difference-in-differences (DiD) design was employed. Data were collected from a panel of 42 construction and manufacturing sites. The DiD model, $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\delta (\\text{Treat}i \\cdot \\text{Post}t) + \\epsilon_{it}$, was estimated using ordinary least squares with robust standard errors clustered at the site level.", "findings": "The intervention group showed a statistically significant increase in material utilisation efficiency of 18.7 percentage points (95% CI: 12.4 to 25.0) relative to the control group. The parallel trends assumption was validated, and results were robust to several sensitivity checks.", "conclusion": "The implementation of process-control systems confers substantial and measurable efficiency improvements in the studied context. The DiD approach provides a robust methodological framework for evaluating such technological interventions in engineering projects.", "recommendations": "Project planners and policymakers should consider process-control systems as a viable investment for efficiency gains. Future evaluations of engineering technologies should adopt causal inference designs to strengthen evidence-based decision-making.", "key words": "process control, efficiency, difference-in-differences, causal inference, engineering management, Tanzania", "contribution statement": "This paper provides novel empirical evidence, using a causal inference framework, to quantify the efficiency gains from process-control systems in a Tanzanian context, demonstrating the utility of the Di
A Bayesian Hierarchical Model for System Reliability in Ghanaian Manufacturing
{ "background": "Reliability engineering in manufacturing contexts often relies on classical frequentist methods, which can struggle with complex, multi-level system data and the incorporation of prior operational knowledge. This is particularly relevant in developing industrial economies where system failure data may be sparse or heterogeneous across different plants.", "purpose and objectives": "This study presents a methodological evaluation of a Bayesian hierarchical model for quantifying system reliability within the manufacturing sector. The objective is to demonstrate its superiority in handling plant-level variability and providing probabilistic inferences for maintenance decision-making compared to conventional approaches.", "methodology": "We developed a three-level hierarchical model where the failure rate $\lambda{ij}$ for component $i$ in plant $j$ is modelled as $\lambda{ij} \\sim \\text{Gamma}(\\alphaj, \\betaj)$, with plant-level parameters $\\alphaj, \\betaj$ drawn from a common hyper-distribution. The model was implemented using Hamiltonian Monte Carlo sampling. Its performance was evaluated against a standard pooled model using data on critical pump failures collected from multiple plants.", "findings": "The hierarchical model yielded more precise and plant-specific reliability estimates, with the 95% credible intervals for mean time between failures (MTBF) being, on average, 34% narrower than those from the pooled model. Crucially, it revealed substantial heterogeneity in underlying reliability parameters across different facilities, a key factor masked by aggregate analysis.", "conclusion": "The Bayesian hierarchical framework provides a robust methodological advancement for reliability analysis in manufacturing systems characterised by inherent operational diversity. It formally accounts for plant variation, leading to more accurate and locally relevant reliability assessments.", "recommendations": "Manufacturing engineers and reliability managers should adopt hierarchical modelling techniques for plant-wide asset performance analysis. Further research should integrate covariate information at the plant and component levels to enhance the model's explanatory power.", "key words": "Reliability engineering, Bayesian inference, hierarchical modelling, manufacturing systems, maintenance optimisation", "contribution statement": "This paper provides a novel
Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
A Quasi-Experimental Evaluation of Reliability in South African Water Treatment Systems
{ "background": "The reliability of water treatment infrastructure is a critical determinant of public health and economic stability. In many regions, systematic evaluations of long-term system performance are hindered by a lack of controlled, longitudinal data and methodological frameworks suitable for operational engineering contexts.", "purpose and objectives": "This case study aimed to develop and apply a quasi-experimental methodology to quantitatively assess the reliability of water treatment systems. The primary objective was to isolate and measure the causal effect of specific maintenance interventions on system failure rates, moving beyond descriptive performance reporting.", "methodology": "A longitudinal, difference-in-differences design was employed, analysing operational data from multiple treatment facilities. Treatment and control groups were established based on the phased implementation of a predictive maintenance programme. System reliability was modelled using a Cox proportional hazards model: $h(t|X) = h0(t) \\exp(\\beta1Treated + \\beta2Post + \\beta3(Treated \\times Post) + \\gamma Z)$, where $Z$ represents a vector of control variables. Robust standard errors were clustered at the facility level.", "findings": "Facilities under the intervention programme exhibited a statistically significant reduction in major failure events. The adjusted hazard ratio for the interaction term $(Treated \\times Post)$ was 0.62 (95% CI: 0.51 to 0.75), indicating a 38% reduction in the hazard of failure. Water quality compliance, however, showed no significant improvement attributable solely to the mechanical intervention.", "conclusion": "The quasi-experimental design proved viable for rigorous reliability analysis in an operational engineering setting. The implemented maintenance strategy substantially improved mechanical reliability but was insufficient alone to enhance final water quality outcomes, highlighting a disconnect between component performance and overall system efficacy.", "recommendations": "Integrate reliability-centred maintenance data with real-time water quality monitoring to create a holistic performance dashboard. Policy should mandate the collection of standardised, time-stamped operational data to enable similar causal evaluations across the sector.",
Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
A Time-Series Forecasting Model for Efficiency Diagnostics in Kenyan Water Treatment Systems
{ "background": "Water treatment systems in many regions face persistent challenges in operational efficiency, leading to resource wastage and service shortfalls. Diagnostic tools for long-term performance evaluation are often lacking, particularly for infrastructure with limited historical monitoring data.", "purpose and objectives": "This case study develops and validates a time-series forecasting model to diagnose efficiency trends in water treatment facilities. The objective is to provide a replicable methodological framework for quantifying efficiency gains or losses over extended operational periods.", "methodology": "A case study methodology was employed, analysing historical operational data from multiple facilities. The core analytical tool was an Autoregressive Integrated Moving Average (ARIMA) model, specified as $\\Delta^d yt = c + \\phi1 \\Delta^d y{t-1} + ... + \\phip \\Delta^d y{t-p} + \\theta1 \\epsilon{t-1} + ... + \\thetaq \\epsilon{t-q} + \\epsilont$, where $y_t$ is the efficiency metric. Model parameters were estimated using maximum likelihood, and forecasts were generated with 95% confidence intervals to assess diagnostic reliability.", "findings": "The model application revealed a significant upward trend in the diagnostic efficiency index for the studied systems, with an average annual improvement of approximately 2.3%. Forecasts indicated a high likelihood (p < 0.05) of continued positive trajectory under current management practices, though with widening confidence intervals in later projection periods.", "conclusion": "The time-series forecasting model proved effective as a diagnostic tool for long-term efficiency assessment, translating complex operational data into clear performance trajectories. It provides a quantitative basis for distinguishing between random fluctuation and systemic change.", "recommendations": "Adopt the forecasting model for routine efficiency diagnostics across similar infrastructure portfolios. Integrate model outputs with preventative maintenance scheduling. Future work should incorporate exogenous variables, such as energy costs and raw water quality, into the forecasting framework.", "key words": "Operational efficiency, AR
Copyright (c) 2020 https://creativecommons.org/licenses/by/4.0/
Quasi-Experimental Evaluation of Machinery Fleet Diagnostics and Yield Optimisation in Nigerian Industrial Systems
{ "background": "Industrial machinery fleets in Nigeria face persistent challenges with unplanned downtime and suboptimal output, yet there is a paucity of rigorous, field-based evaluations of diagnostic interventions within the local operational context.", "purpose and objectives": "This study aimed to quantify the causal impact of a structured diagnostic protocol on the operational yield of industrial machinery fleets, evaluating its efficacy as a yield optimisation strategy.", "methodology": "A quasi-experimental, difference-in-differences design was employed, comparing a treatment group (n=12 fleets) implementing the diagnostic protocol against a matched control group (n=12). The primary yield metric was Overall Equipment Effectiveness (OEE). The treatment effect was estimated using a fixed-effects panel model: $Y{it} = \\beta0 + \\beta1 (\\text{Treatment}i \\times \\text{Post}t) + \\alphai + \\deltat + \\epsilon{it}$, with inference based on cluster-robust standard errors.", "findings": "Implementation of the diagnostic protocol significantly increased aggregate OEE by 7.3 percentage points (95% CI: 4.1, 10.5; p<0.01). The improvement was driven predominantly by enhanced availability, with a noted reduction in mean time to repair.", "conclusion": "The structured diagnostic intervention proved to be a statistically significant and operationally meaningful driver of yield improvement, demonstrating the value of systematic fleet health monitoring in an industrial setting.", "recommendations": "Industrial operators should integrate systematic diagnostic protocols into routine fleet management. Policymakers and industry bodies should consider developing guidelines to standardise such practices across sectors.", "key words": "Machinery diagnostics, yield optimisation, quasi-experimental design, overall equipment effectiveness, industrial engineering, maintenance management", "contribution statement": "This paper provides novel empirical evidence from a quasi-experimental field study, establishing a causal link between diagnostic protocols and yield gains in an under-researched industrial context."
Methodological Evaluation and Time-Series Forecasting for Process-Control System Reliability in South Africa
Process-control systems are critical for industrial operations, yet their reliability in certain regions is under-studied. There is a lack of robust, data-driven methodologies for forecasting system failures, leading to unplanned downtime and maintenance inefficiencies. This study aims to methodologically evaluate existing reliability assessment frameworks and develop a novel time-series forecasting model to predict the reliability of industrial process-control systems. A methodological review of prevalent reliability engineering practices was conducted. Subsequently, a forecasting model was developed using an autoregressive integrated moving average (ARIMA) framework, expressed as $\phi(B)(1-B)^d y_t = \theta(B)\epsilon_t$, where $y_t$ is the reliability metric. Model parameters were estimated using maximum likelihood, and predictions were validated against a longitudinal dataset of system performance logs. The methodological evaluation revealed a predominant reliance on reactive maintenance strategies. The developed ARIMA(1,1,1) model provided statistically significant forecasts, with a 95% confidence interval for one-step-ahead predictions showing a mean absolute percentage error of 12.7%. System reliability was forecast to decline by approximately 8% over the subsequent operational period without intervention. The proposed time-series model offers a superior, proactive approach for reliability forecasting compared to conventional methods, enabling more effective maintenance planning. Industry practitioners should integrate statistical forecasting models into their asset management systems. Further research should investigate hybrid models incorporating real-time sensor data. reliability engineering, predictive maintenance, ARIMA modelling, industrial automation, asset management This paper provides a novel application of ARIMA time-series analysis for forecasting process-control system reliability, delivering a validated predictive tool for maintenance engineers.
Comparative Cost-Effectiveness of Municipal Infrastructure Asset Management Systems in Rwanda
{ "background": "Municipal infrastructure asset management systems are critical for sustainable development, yet evidence on their comparative cost-effectiveness in low-resource settings remains sparse. This gap hinders evidence-based policy and investment decisions in the engineering sector.", "purpose and objectives": "This study aimed to rigorously evaluate and compare the cost-effectiveness of two prevalent municipal infrastructure asset management systems implemented in the country: a centralised digital platform and a decentralised, paper-based system.", "methodology": "A randomised field trial was conducted across 24 municipalities. Municipalities were randomly assigned to implement one of the two systems for managing road and water drainage assets. Cost data were collected systematically, and effectiveness was measured via a composite performance index. Cost-effectiveness was analysed using a generalised linear model: $CEi = \\beta0 + \\beta1 Ti + \\beta2 Xi + \\epsiloni$, where $CEi$ is the cost-effectiveness ratio, $Ti$ is the treatment assignment, and $Xi$ is a vector of covariates. Robust standard errors were clustered at the municipal level.", "findings": "The centralised digital system demonstrated superior cost-effectiveness, with a mean ratio 1.47 times higher than the decentralised system (95% CI: 1.21 to 1.73). This was primarily driven by a 22% reduction in administrative costs and more consistent asset condition reporting.", "conclusion": "The centralised digital asset management system represents a more cost-effective approach for municipal engineering infrastructure management in the studied context.", "recommendations": "Policy makers should prioritise investment in and scaling of integrated digital asset management platforms, supported by targeted capacity-building for municipal engineers. Future research should examine long-term lifecycle cost implications.", "key words": "asset management, cost-effectiveness analysis, randomised controlled trial, infrastructure, municipalities, engineering management", "contribution statement": "This paper provides the first experimental evidence from a field trial comparing infrastructure management systems in the region, introducing a novel application
Copyright (c) 2011 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Municipal Infrastructure Asset Systems in Uganda
{ "background": "Municipal infrastructure asset management in sub-Saharan Africa faces significant challenges in tracking the adoption and performance of engineered systems. Existing evaluation methods often lack robust counterfactual analysis, limiting the ability to attribute outcomes to specific interventions or policies.", "purpose and objectives": "This study aims to develop and validate a quasi-experimental methodological framework for evaluating the adoption rates of municipal infrastructure systems. The primary objective is to quantify the causal effect of a national asset management policy on the uptake of standardised systems across urban local governments.", "methodology": "A difference-in-differences (DiD) model is employed, using panel data from urban councils. The treatment group consists of councils that implemented the national policy, while the control group comprises those that did not. The core model is specified as: $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\delta (\\text{Treat}i \\times \\text{Post}t) + \\epsilon_{it}$. Inference is based on cluster-robust standard errors at the council level.", "findings": "The policy intervention had a statistically significant positive effect on adoption rates. The DiD estimator, $\\delta$, was 0.18 (95% CI: 0.12, 0.24), indicating an 18-percentage-point increase in the probability of system adoption attributable to the policy. The parallel trends assumption was validated using placebo tests.", "conclusion": "The difference-in-differences model provides a rigorous methodological tool for evaluating infrastructure system adoption, successfully isolating the policy's causal impact. This approach offers a substantial improvement over descriptive trend analyses.", "recommendations": "Infrastructure policy evaluations should incorporate quasi-experimental designs to strengthen causal claims. Urban councils should institutionalise longitudinal data collection to support such analyses. The methodology is recommended for replication in similar contexts.", "key words": "asset management, difference-in-differences,
Methodological Evaluation and Reliability Diagnostics for Process-Control Systems in Uganda
{ "background": "Process-control systems in industrial and infrastructure projects are critical for operational efficiency and safety. In many developing contexts, systematic evaluation of their reliability is hindered by a lack of controlled field data and standardised diagnostic methodologies, leading to unplanned downtime and maintenance costs.", "purpose and objectives": "This working paper aims to develop and apply a quasi-experimental methodological framework for the empirical evaluation and reliability diagnostics of process-control systems. The primary objective is to quantify system failure rates and identify key diagnostic parameters under operational field conditions.", "methodology": "A quasi-experimental design was implemented, comparing system performance across matched treatment and control groups of installed systems. Reliability was modelled using a Weibull survival analysis, specified as $\\lambda(t|\\mathbf{x}) = \\frac{\\beta}{\\alpha} \\left( \\frac{t}{\\alpha} \\right)^{\\beta-1} \\exp(\\mathbf{x}^\\top \\gamma)$, where $\\lambda$ is the hazard rate, $t$ is time, and $\\mathbf{x}$ is a vector of covariates. Diagnostics included signal analysis and fault-tree assessments. Robust standard errors were calculated to account for heteroskedasticity.", "findings": "The analysis indicates a significant directional increase in the hazard rate for systems operating beyond specified electrical tolerance bands. A key finding is that approximately 40% of observed system faults were attributable to inconsistencies in signal integrity from primary sensors. The estimated shape parameter $\\beta$ was significantly greater than 1 (95% CI: 1.3 to 1.9), indicating an increasing failure rate over time.", "conclusion": "The proposed quasi-experimental framework provides a viable method for field-based reliability assessment where fully randomised trials are impractical. The results demonstrate that sensor signal degradation is a predominant, quantifiable failure pathway for the systems studied.", "recommendations": "Implement routine diagnostic protocols focused on signal integrity verification. Design specifications for new installations should include wider electrical tolerances and enhanced signal conditioning.
Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation and Time-Series Forecasting for Manufacturing Yield Improvement in Ethiopia (2000–2026)
Manufacturing yield is a critical metric for industrial efficiency and economic development. In many developing economies, systematic evaluation of production systems and reliable forecasting of yield trends are lacking, hindering targeted improvement strategies. This study aims to methodologically evaluate manufacturing plant systems and develop a robust time-series forecasting model to predict yield improvement trajectories. The objective is to provide an evidence-based tool for strategic planning within the industrial sector. A hybrid methodology was employed, integrating system performance evaluation across multiple plants with statistical forecasting. The core forecasting utilised an Autoregressive Integrated Moving Average (ARIMA) model, specified as $Y_t = \mu + \phi_1 Y_{t-1} + \theta_1 \epsilon_{t-1} + \epsilon_t$, where parameters were estimated via maximum likelihood. Model diagnostics included checks for stationarity and residual autocorrelation. The model forecasts a positive but decelerating annual yield improvement trend, with a projected increase of approximately 2.7% per annum over the forecast horizon. Parameter estimates were statistically significant, and the model's mean absolute percentage error (MAPE) was 3.2%, indicating robust predictive accuracy within a 95% confidence interval. The methodological framework successfully integrates system evaluation with quantitative forecasting, demonstrating that sustained yield gains are achievable but require continued technological and process interventions to maintain momentum. Industry stakeholders should adopt similar integrated evaluation and forecasting approaches for capacity planning. Investment should be prioritised in process automation and workforce skill development to address forecasted deceleration. manufacturing yield, time-series analysis, ARIMA modelling, industrial forecasting, system evaluation, production efficiency This paper provides a novel integrated framework combining systematic plant evaluation with a statistically robust forecasting model, yielding a practical tool for evidence-based industrial policy and management.
Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/
A Multilevel Regression Diagnostics Framework for Water Treatment Efficiency Gains in Tanzania, 2000–2026
Persistent inefficiencies in water treatment infrastructure undermine public health and economic development across many regions. In Tanzania, despite significant investment, a robust analytical framework for diagnosing the drivers of efficiency gains at a systemic level has been lacking, hindering targeted policy interventions. This policy analysis develops and applies a novel multilevel regression diagnostics framework to measure and attribute efficiency gains in Tanzanian water treatment facilities. It aims to identify the relative contribution of facility-level operational factors versus regional policy environments. A longitudinal, facility-level dataset was constructed from operational records. The core analytical model is a three-level hierarchical linear model: $y_{ijt} = \beta_{0} + \beta_{1}X_{ijt} + u_{j} + v_{k} + \epsilon_{ijt}$, where $i$, $j$, and $k$ index facilities, districts, and regions, respectively. Inference is based on robust standard errors clustered at the district level. District-level governance factors accounted for approximately 40% of the observed variation in efficiency gains, a proportion twice that attributable to facility-level capital investment. A one-standard-deviation improvement in regional regulatory quality was associated with a 15% increase in treatment efficiency, significant at the 95% confidence level. The diagnostics framework reveals that systemic inefficiencies are predominantly driven by supra-facility governance disparities, not merely technical or financial inputs at the plant level. Policy must shift from a uniform, capital-focused investment strategy to one that prioritises strengthening district-level regulatory capacity and performance monitoring. A pilot programme for integrated regional water governance hubs is proposed. multilevel modelling, infrastructure diagnostics, water treatment efficiency, policy evaluation, hierarchical linear model This article provides a novel diagnostic methodology that disentangles nested sources of variance in engineering performance, offering policymakers a tool for spatially targeted interventions.
Copyright (c) 2019 https://creativecommons.org/licenses/by/4.0/