African Structural Engineering is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Fatigue Life Assessment of Welded Steel Bridge Connections Under Variable Amplitude Loading
Welded steel connections are the most fatigue-critical components in highway bridge structures, and their reliable assessment under realistic variable amplitude loading remains a fundamental challenge in structural engineering practice. This paper presents a comprehensive fatigue life assessment of welded steel bridge connections subjected to variable amplitude traffic loading, integrating three complementary methodologies: the nominal stress method per EN 1993-1-9 (Eurocode 3), the hot-spot stress method per the International Institute of Welding (IIW) guidelines, and a fracture mechanics approach using the Paris-Erdogan crack propagation law. Rainflow cycle-counting was applied to measured and simulated stress histories from weigh-in-motion (WIM) traffic surveys on two South Sudanese highway corridors. The Palmgren-Miner linear damage accumulation rule was used to predict fatigue life under the derived variable amplitude spectra. A probabilistic reliability analysis using First-Order Reliability Method (FORM) and Monte Carlo simulation quantified the uncertainty in fatigue life prediction arising from material scatter, geometric imperfections, and traffic randomness. Results demonstrate that the nominal stress method provides conservative predictions (15-23% underestimation of fatigue life relative to hot-spot stress method) but remains the recommended approach for routine design practice in Sub-Saharan Africa owing to its simplicity. The probabilistic analysis reveals that the reliability index beta falls below the Eurocode target of 3.8 after approximately 52-68 years for the most heavily trafficked corridors, providing a quantitative basis for inspection and rehabilitation planning. The effect of weld quality class (as-welded vs. post-weld treated) on fatigue life
Copyright (c) 2026 African Structural Engineering
Finite Element Analysis of Above-Ground Storage TankFoundations on Soft Swamp Soils in Jonglei StateSouth Sudan
—Above-ground crude oil storage tanks (ASTs) in Jonglei State, South Sudan, are founded on soft Sudd wetland clays exhibiting undrained shear strengths as low as 4–8 kPa and consolidation settlements exceeding 300 mm under standard loading. This paper presents a comprehensive finite element analysis (FEA) study of AST foundation performance on these challenging soils, evaluating eight foundation configurations across 36 parametric cases using the Mohr-Coulomb constitutive model implemented in PLAXIS 2D (axisymmetric and plane-strain formulations). Geotechnical characterisation from five boreholes and 120 in-situ vane shear tests established a five-layer soil profile for the Pibor–Bor corridor, with primary consolidation coefficients cv of 0.40–2.50 × 10⁻³ m²/day. FEA results demonstrate that unreinforced shallow foundations and ring-rafts shallower than 1.0 m consistently fail the API 650 bearing capacity criterion (FoS ≥ 2.5) for tank diameters of 30 m and above under design contact pressures of 120 kPa. A 1.2-m-thick reinforced concrete ring-raft achieves FoS = 2.61 and maximum differential settlement of 31 mm, satisfying API 650 shell distortion limits. For tank diameters exceeding 40 m or sites with Su < 10 kPa, piled raft foundations (FoS = 4.20) or stone column ground improvement (FoS = 3.05) are mandated. A parametric Factor of Safety surface as a function of undrained shear strength and raft thickness provides a design nomograph for direct engineering application. Seasonal flood inundation reduces effective Su by 22–35% and lowers FoS by 0.4–0.8 units, necessitating flood protection measures as a prerequisite to any foundation design on the Jonglei wetland plain.
A Quasi-Experimental Evaluation of Reliability in Tanzanian Water Treatment Systems
{ "background": "The reliability of water treatment infrastructure in sub-Saharan Africa is a critical determinant of public health and economic development. Current performance assessments often lack robust, diagnostic methodologies capable of isolating causal factors of system failure.", "purpose and objectives": "This study aimed to develop and apply a novel quasi-experimental methodological framework for the performance diagnostics of water treatment systems, with the objective of quantifying reliability and identifying specific failure mechanisms.", "methodology": "A longitudinal, interrupted time-series design was employed, monitoring key performance indicators (e.g., turbidity, chlorine residual) at multiple treatment facilities. System reliability was modelled using a generalised linear mixed model: $\\logit(P(Y{it}=1)) = \\beta0 + \\beta1 Tt + \\beta2 X{it} + ui + \\epsilon{it}$, where $Y{it}$ is operational status, $Tt$ is a post-intervention period indicator, $X{it}$ are time-varying covariates, and $ui$ are facility random effects. Robust standard errors were used for inference.", "findings": "The framework successfully diagnosed distinct failure modes. A key finding was that mechanical filtration failures accounted for a significant proportion (approximately 40%) of total system downtime, a relationship confirmed with 95% confidence. Operational performance showed no statistically significant improvement following routine maintenance interventions alone.", "conclusion": "The proposed quasi-experimental framework provides a rigorous, transferable method for engineering diagnostics, moving beyond descriptive reporting to causal analysis of infrastructure performance.", "recommendations": "Infrastructure assessments should integrate causal diagnostic methods. Maintenance protocols must be revised to prioritise mechanical filtration components and incorporate predictive, condition-based strategies.", "key words": "Infrastructure reliability, quasi-experimental design, performance diagnostics, water treatment, sub-Saharan Africa, causal inference", "contribution statement": "This paper presents a novel methodological framework that applies causal inference techniques from econometrics to the field performance evaluation of civil engineering infrastructure, generating actionable diagnostic
Copyright (c) 2000 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation and Panel-Data Estimation for Yield Improvement in Nigerian Power-Distribution Systems
{ "background": "Chronic inefficiencies in power-distribution networks, characterised by high technical and commercial losses, severely constrain electricity supply and economic development. A persistent gap exists in the rigorous, longitudinal analysis of operational performance across diverse infrastructural assets within these systems.", "purpose and objectives": "This study aims to develop and apply a panel-data econometric framework to methodologically evaluate the performance of distribution equipment and quantify the drivers of yield improvement within a national network.", "methodology": "A balanced panel dataset was constructed from operational records of 2,347 distribution transformers across multiple service regions. The core relationship was estimated using a two-way fixed effects model: $Y{it} = \\beta0 + \\beta1 X{it} + \\mui + \\lambdat + \\epsilon{it}$, where $Y{it}$ is the technical yield. Robust standard errors were clustered at the feeder level.", "findings": "Proactive maintenance interventions demonstrated a statistically significant positive association with technical yield. A one-standard-deviation increase in the maintenance index was associated with a 4.7% improvement in yield (95% CI: 3.1% to 6.3%), holding other factors constant. Load diversity and transformer age were also significant determinants.", "conclusion": "The methodological approach confirms that systematic, data-driven asset management is a critical lever for enhancing distribution efficiency. The panel model effectively isolates the impact of operational practices from time-invariant unobserved heterogeneity.", "recommendations": "Distribution network operators should institutionalise panel-data analytics for performance benchmarking and prioritise predictive maintenance schedules based on the identified key drivers. Regulatory frameworks should incentivise yield improvement linked to these evidence-based operational metrics.", "key words": "power distribution, technical losses, fixed effects model, asset management, infrastructure efficiency", "contribution statement": "This paper provides a novel application of panel-data econometrics to power-distribution asset performance, introducing a robust methodological framework for isolating causal drivers of yield improvement in a developing economy context
Copyright (c) 2020 https://creativecommons.org/licenses/by/4.0/
Replication and Methodological Diagnostics of Tanzanian Water Treatment Systems
{ "background": "Previous engineering-economic analyses of centralised water treatment infrastructure in sub-Saharan Africa have produced inconsistent conclusions regarding long-term cost-effectiveness. These discrepancies hinder evidence-based policy and investment, suggesting potential methodological limitations in prior modelling approaches.", "purpose and objectives": "This replication study conducts a methodological diagnostic of prior cost-effectiveness models. Its primary objective is to verify and extend the original multilevel regression analysis using an expanded dataset and robust statistical diagnostics to assess the reliability of earlier findings.", "methodology": "We executed a direct replication followed by a measurement-error-in-variables analysis. The core multilevel model is specified as $Cost{ij} = \\beta{0j} + \\beta{1}Volume{ij} + \\beta{2}Age{ij} + u{0j} + e{ij}$, where $j$ denotes the facility. We employed cluster-robust standard errors and conducted a full suite of sensitivity analyses, including tests for heteroscedasticity and influential outliers.", "findings": "The replication revealed a significant upward bias in earlier cost-effectiveness estimates. Specifically, the original coefficient for treatment volume was overestimated by approximately 22%. Our diagnostic identified that failure to account for non-linear depreciation of mechanical components was a key source of this bias, materially affecting the projected net present value of systems.", "conclusion": "The original study's conclusions on cost-effectiveness are not robust to methodological refinement. The significant bias underscores the critical importance of incorporating engineering degradation parameters into economic models of infrastructure.", "recommendations": "Future engineering-economic analyses of public infrastructure should integrate physical asset degradation models into their regression frameworks. Policymakers should treat cost projections from models lacking such diagnostics with caution.", "key words": "replication study, infrastructure economics, multilevel modelling, water treatment, cost-benefit analysis, asset management", "contribution statement": "This study provides a novel diagnostic framework that integrates engineering degradation rates into regression-based cost-effectiveness models, yielding a more robust tool for infrastructure investment appraisal
Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
A Bayesian Hierarchical Model for Power-Distribution System Reliability
{ "background": "Ghana's power-distribution network has faced persistent reliability challenges, with frequent outages impacting economic development. Existing policy evaluations often rely on aggregate failure rates, lacking the granularity to identify specific equipment vulnerabilities and regional disparities that inform targeted infrastructure investment.", "purpose and objectives": "This policy analysis aims to develop and apply a novel Bayesian hierarchical model to quantify the reliability of distribution system components. The objective is to provide evidence-based insights for prioritising maintenance and capital renewal within the national grid.", "methodology": "A Bayesian hierarchical Weibull survival model is employed, treating failure times for transformers, circuit breakers, and lines as right-censored data. The model structure is $\\lambda{ij} \\sim \\text{Weibull}(\\alphaj, \\exp(\\beta^T X{ij}))$, where $\\alphaj$ are component-specific shape parameters and $X_{ij}$ are regional covariates. Posterior distributions are estimated using Markov chain Monte Carlo sampling.", "findings": "The analysis reveals a pronounced spatial heterogeneity in reliability, with the posterior probability that component failure rates in the northern regions exceed the national median being 0.92 (95% credible interval: 0.87, 0.96). Distribution transformers were identified as the most critical component, accounting for an estimated 42% of system unreliability.", "conclusion": "The hierarchical model successfully disentangles component-specific failure patterns from regional effects, offering a superior evidence base for policy compared to traditional reliability metrics. The findings underscore the necessity of geographically and technically differentiated investment strategies.", "recommendations": "Policy should mandate the adoption of probabilistic reliability modelling for grid planning. Investment should be prioritised towards transformer replacement programmes in high-failure regions and the establishment of a centralised, component-level failure database to continuously update model parameters.", "key words": "infrastructure reliability, Bayesian inference, survival analysis, power distribution, grid investment, maintenance policy", "contribution statement": "This paper introduces a
Copyright (c) 2017 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation and Risk Reduction Forecasting for Ethiopia's Power-Distribution Infrastructure, 2000–2026
{ "background": "The reliability of power-distribution infrastructure is a critical determinant of economic development and social welfare. In Ethiopia, ageing assets and operational challenges have historically led to significant service interruptions and safety risks, necessitating a robust analytical framework for policy intervention.", "purpose and objectives": "This policy analysis aims to methodologically evaluate the state of the nation's power-distribution equipment systems and to develop a forecasting model for quantifying the risk reduction achievable through targeted infrastructure investment and maintenance policies.", "methodology": "A time-series forecasting model was developed using historical failure-rate data and asset-condition indices. The core model is a generalised linear model with a logit link: $\\log(\\frac{p}{1-p}) = \\beta0 + \\beta1 X1 + \\beta2 X_2 + \\epsilon$, where $p$ is the probability of a major fault. Model parameters were estimated using maximum likelihood, with robust standard errors to account for heteroscedasticity.", "findings": "The analysis forecasts that a sustained annual investment increase of 15% in targeted asset replacement would reduce the system-wide probability of catastrophic failure by an estimated 42% (95% CI: 38% to 46%) over the forecast period. The model indicates diminishing returns beyond this investment threshold.", "conclusion": "Strategic, data-driven investment in distribution infrastructure can substantially mitigate systemic risk. The forecasting model provides a quantifiable basis for prioritising policy decisions and capital expenditure.", "recommendations": "Policymakers should adopt the presented forecasting framework for long-term infrastructure planning. Investment should be prioritised towards geographically identified high-risk clusters and the replacement of specific, ageing equipment classes identified by the model.", "key words": "infrastructure risk, forecasting model, power distribution, policy analysis, asset management", "contribution statement": "This paper provides a novel, empirically grounded forecasting methodology that directly links infrastructure investment levels to quantifiable reductions in system-wide failure risk, offering a new tool for
Copyright (c) 2003 https://creativecommons.org/licenses/by/4.0/
Replication and Validation of a Time-Series Forecasting Model for Municipal Infrastructure Asset Efficiency in South Africa (2000–2026)
{ "background": "Municipal infrastructure asset management in South Africa requires robust predictive tools to plan for maintenance and capital expenditure. A previously proposed time-series forecasting model for infrastructure efficiency gains has been cited in policy discussions but lacks independent validation of its methodological rigour and predictive performance.", "purpose and objectives": "This study aimed to replicate and critically evaluate the methodological framework of the specified forecasting model. The primary objective was to assess its computational reproducibility, statistical validity, and sensitivity to input assumptions.", "methodology": "The original model specification, $yt = \\beta0 + \\beta1 \\text{log}(A{t-1}) + \\beta2 Tt + \\epsilont$, where $A$ represents asset condition and $T$ is a time trend, was re-implemented using open-source software. The replication process tested the model's stability by applying bootstrapping techniques to derive robust standard errors and conducting sensitivity analyses on key parameters.", "findings": "The replication confirmed the model's core computational output but revealed a significant sensitivity to the initial asset condition parameter; a 10% variation in this input altered long-term efficiency forecasts by approximately 15%. The bootstrapped confidence intervals for the trend coefficient $\\beta2$ were substantially wider than originally reported, indicating greater forecasting uncertainty.", "conclusion": "While the model is computationally reproducible, its utility for long-term strategic planning is limited by its sensitivity to initial conditions and the under-reported uncertainty in its trend estimates.", "recommendations": "Future applications of this model should incorporate probabilistic forecasting methods and more comprehensive uncertainty quantification. Municipal engineers should treat its point forecasts with caution, using them as one input within a broader scenario-based planning framework.", "key words": "asset management, forecasting, infrastructure, replication, time-series, uncertainty quantification", "contribution statement": "This study provides the first independent, methodological validation of a cited forecasting model, offering a critical technical assessment of its limitations for infrastructure planning and establishing a revised protocol for
Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation and Panel-Data Estimation of Municipal Infrastructure System Reliability in Senegal, 2000–2026
{ "background": "Municipal infrastructure systems in many developing nations face chronic reliability challenges, yet longitudinal, system-level performance data and robust analytical frameworks for their assessment are scarce. This impedes evidence-based asset management and investment planning.", "purpose and objectives": "This study aims to develop and evaluate a methodological framework for quantifying the reliability of integrated municipal infrastructure systems. Its objective is to estimate system-wide reliability trends and identify key determinants using a longitudinal dataset.", "methodology": "A panel-data econometric model is employed, analysing a novel, author-compiled dataset covering water supply, sanitation, road networks, and public lighting across multiple municipalities. The core reliability metric is modelled using a fixed-effects estimator: $R{it} = \\alphai + \\beta X{it} + \\deltat + \\epsilon{it}$, where $R{it}$ is the composite reliability index for municipality $i$ in period $t$, $\\alphai$ denotes municipality fixed effects, $X{it}$ is a vector of time-varying covariates, and $\\delta_t$ represents period effects. Inference is based on robust standard errors clustered at the municipal level.", "findings": "System reliability exhibited a statistically significant negative trend over the study period, with an average annual decline of 1.7 percentage points (95% CI: -2.3 to -1.1). The analysis identified capital expenditure volatility and institutional fragmentation as the most substantial negative predictors of reliability, whereas targeted operation and maintenance investments showed a positive, albeit diminishing, marginal return.", "conclusion": "The methodological framework provides a replicable tool for systemic infrastructure assessment. The results indicate a concerning degradation in the performance of municipal infrastructure systems, driven more by institutional and financial management factors than by asset age alone.", "recommendations": "Municipal authorities should prioritise stabilising capital budgets and strengthening integrated, cross-asset management protocols. National policy should incentivise data-driven performance monitoring and ring-fence funding for routine maintenance.", "key words": "infrastructure
Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
A Quasi-Experimental Evaluation of Reliability in Nigerian Transport Maintenance Depot Systems
{ "background": "Maintenance depot systems are critical for transport infrastructure reliability, yet their operational performance in developing economies is under-studied. Existing assessments often lack rigorous, quantitative frameworks for evaluating systemic reliability, leading to reactive rather than proactive management.", "purpose and objectives": "This study aimed to develop and apply a quasi-experimental methodology to quantitatively evaluate the reliability of transport maintenance depot systems. The primary objective was to measure the causal effect of depot system configuration on reliability metrics.", "methodology": "A quasi-experimental, pre-test/post-test design with non-equivalent control groups was implemented across multiple depots. System reliability was modelled using a Weibull failure process. The core statistical model was $R(t) = \\exp^{-(t/\\eta)^\\beta}$, where $\\eta$ is the scale parameter and $\\beta$ the shape parameter. Data were collected via direct observation and maintenance logs, with inference based on robust standard errors to account for heteroskedasticity.", "findings": "Depots implementing a standardised preventive maintenance protocol showed a statistically significant 34% improvement in mean time between unscheduled repairs (95% CI: 22% to 46%). The shape parameter $\\beta$ shifted from 0.8 (indicating infant mortality) to 1.1 (indicating random failures), suggesting a fundamental change in the failure regime.", "conclusion": "The quasi-experimental design successfully isolated the impact of systematic interventions on depot reliability. The results demonstrate that structured preventive maintenance protocols can substantially enhance the reliability of depot systems in this context.", "recommendations": "Transport authorities should adopt standardised, data-driven preventive maintenance schedules. Future depot designs should integrate reliability-centred maintenance principles from the planning phase.", "key words": "maintenance engineering, system reliability, quasi-experimental design, transport infrastructure, preventive maintenance", "contribution statement": "This paper provides a novel methodological framework for the causal evaluation of engineering system reliability in real-world operational settings, moving beyond descriptive
Risk Reduction in Kenyan Water Treatment Systems
The performance of water treatment infrastructure in Kenya is critical for public health and economic development, yet systematic, longitudinal analyses of its operational risks are scarce. Existing policy evaluations often lack the technical rigour to isolate the effects of specific interventions from other confounding factors. This policy analysis aims to quantify the impact of targeted infrastructure investments and regulatory changes on the operational risk profile of water treatment systems. It seeks to identify which policy levers most effectively reduce failure rates and improve reliability. A balanced panel dataset of facility-level performance indicators was constructed. The core analysis employs a fixed-effects model: $FailureRate_{it} = \alpha_i + \beta_1Investment_{it} + \beta_2RegulatoryScore_{it} + \gamma X_{it} + \epsilon_{it}$, where $\alpha_i$ denotes facility-specific effects. Inference is based on robust standard errors clustered at the county level. A one-standard-deviation increase in the regulatory compliance score was associated with a 17.5% reduction in the probability of a major treatment failure. The effect of capital investment was positive but statistically insignificant after controlling for facility heterogeneity and maintenance expenditure. Enhanced regulatory oversight and performance monitoring are more strongly correlated with reduced operational risk than capital expenditure alone. This suggests that policy must prioritise governance and operational management alongside physical asset renewal. Policy should mandate and fund continuous performance monitoring to inform targeted maintenance. Regulatory bodies require strengthened enforcement capabilities. Future investment programmes must integrate explicit operational risk benchmarks. infrastructure resilience, panel data, fixed effects, regulatory compliance, operational risk, water treatment This study provides the first longitudinal, facility-level econometric assessment of water treatment policy effectiveness in the region, introducing a novel risk-reduction metric tailored for engineering asset management.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation and Risk Reduction in Senegalese Manufacturing Systems
{ "background": "Manufacturing systems in developing economies face significant operational risks, yet rigorous methodological frameworks for evaluating systemic interventions are scarce. The Senegalese industrial sector, a key regional hub, lacks empirical studies quantifying the efficacy of engineering-led risk reduction programmes.", "purpose and objectives": "This study aims to develop and apply a quasi-experimental econometric model to methodologically evaluate the causal impact of a structured engineering safety and maintenance protocol on operational risk within manufacturing plants.", "methodology": "A difference-in-differences (DiD) model was employed, leveraging panel data from treatment and control groups of plants. The core specification is $Y{it} = \\alpha + \\beta (Treati \\times Postt) + \\gammai + \\deltat + \\epsilon{it}$, where $Y_{it}$ is the plant-level risk index. Inference is based on cluster-robust standard errors at the plant level.", "findings": "Implementation of the protocol caused a statistically significant reduction in the composite risk index. The average treatment effect on the treated (ATT) was an 18.5% reduction (95% CI: 12.1% to 24.9%). The parallel trends assumption was validated using pre-intervention data.", "conclusion": The methodological application of DiD provides robust, causal evidence that the engineered system intervention substantially mitigated operational risks. This confirms the value of quasi-experimental designs for post-hoc evaluation in structural engineering management.", "recommendations": "Manufacturing plant managers should adopt similar evidence-based protocols. Policymakers and engineering practitioners are encouraged to integrate quasi-experimental evaluation frameworks into the planning stages of systemic interventions to rigorously measure impact.", "key words": "difference-in-differences, operational risk, quasi-experimental design, manufacturing systems, safety protocol, causal inference", "contribution statement": "This paper provides the first application of a difference-in-differences model to isolate the causal effect of an engineering system intervention on operational risk in a West African manufacturing
A Randomised Field Trial Methodology for Evaluating Distribution Transformer Efficiency and Loss Reduction in Uganda
{ "background": "Distribution transformer losses constitute a significant portion of technical losses in power networks, yet robust field-based methodologies for evaluating efficiency improvements in real-world settings are lacking, particularly in sub-Saharan Africa.", "purpose and objectives": "This article presents a novel randomised field trial (RFT) methodology designed to empirically quantify the efficiency gains and loss reduction achieved by deploying high-efficiency distribution transformers within an operational network.", "methodology": "The RFT clusters substation feeders into matched pairs based on load profile and technical characteristics, then randomly assigns a high-efficiency transformer to one feeder within each pair. Core efficiency is measured using a precision three-wattmeter method under actual load conditions. The treatment effect on aggregate technical and commercial losses is estimated using a difference-in-differences model: $\\Delta Loss{it} = \\beta0 + \\beta1 Treatmenti + \\beta2 Postt + \\beta3 (Treatmenti \\times Postt) + \\epsilon{it}$, with inference based on cluster-robust standard errors.", "findings": "As this is a methodology article, no empirical results from the trial are presented. The developed protocol successfully enrolled and randomised over 150 distribution transformers across multiple network regions. The primary anticipated outcome is a quantifiable reduction in median load losses, with the analysis powered to detect a relative reduction of 15% or greater at 80% power.", "conclusion": "The proposed RFT framework provides a rigorous, evidence-based approach for evaluating grid efficiency interventions, moving beyond laboratory estimates to deliver actionable, field-verified performance data.", "recommendations": "Utilities and regulators should adopt randomised field trial designs for capital-intensive grid equipment deployments to validate efficiency claims, inform procurement standards, and accurately forecast return on investment.", "key words": "randomised field trial, distribution transformer, technical losses, difference-in-differences, energy efficiency, sub-Saharan Africa", "contribution statement": "This paper provides the first formalised RFT methodology for evaluating
Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
A Bayesian Hierarchical Model for Yield Improvement in Rwandan Transport Maintenance Depot Systems
{ "background": "Transport maintenance depot systems in developing economies are critical infrastructure assets, yet their operational yield is often suboptimal due to complex, interacting factors. Current evaluation methods frequently lack the statistical rigour to quantify uncertainty and integrate multi-level data effectively.", "purpose and objectives": "This study presents a methodological evaluation of a novel Bayesian hierarchical model designed to measure and diagnose yield improvement within depot systems. The objective is to provide a robust framework for identifying key performance drivers and quantifying their effects with explicit uncertainty.", "methodology": "A Bayesian hierarchical model was developed and applied to operational data from a network of depots. The core model structure is $y{ij} \\sim \\text{Normal}(\\alphaj + \\beta X{ij}, \\sigma^2)$, with $\\alphaj \\sim \\text{Normal}(\\mu{\\alpha}, \\tau^2)$, where $y{ij}$ is the yield for observation $i$ in depot $j$, $X{ij}$ are covariates, and $\\alphaj$ are depot-level random effects. Inference was performed using Hamiltonian Monte Carlo.", "findings": "The model identified depot management practice as the most significant driver of yield variation, with a posterior probability exceeding 0.95 that its effect was positive. The estimated depot variability ($\\tau$) had a 90% credible interval of [0.15, 0.32], indicating substantial heterogeneity not explained by observed covariates alone.", "conclusion": "The Bayesian hierarchical model provides a statistically robust methodological framework for yield analysis in maintenance depot systems, successfully quantifying uncertainty and isolating multi-level performance drivers.", "recommendations": "Depot system managers should adopt hierarchical modelling approaches for performance diagnostics. Future research should integrate real-time sensor data into the model framework to enable predictive maintenance scheduling.", "key words": "Bayesian inference, hierarchical modelling, infrastructure management, maintenance engineering, operational yield, probabilistic analysis", "contribution statement": "This paper contributes a novel, probabilistically rigorous
Methodological Evaluation and Time-Series Forecasting for Yield Improvement in Rwandan Process-Control Systems
{ "background": "Process-control systems in industrial settings are critical for operational efficiency and product yield. In many developing economies, systematic evaluation of these systems and predictive modelling for yield optimisation are underdeveloped, leading to suboptimal performance and resource utilisation.", "purpose and objectives": "This study aims to methodologically evaluate existing process-control frameworks and to develop a robust time-series forecasting model specifically for predicting and improving production yield in an industrial context.", "methodology": "A hybrid methodology was employed, integrating a diagnostic evaluation of control system architectures with the development of an Autoregressive Integrated Moving Average with exogenous variables (ARIMAX) model, specified as $Yt = \\mu + \\sum{i=1}^{p}\\phii Y{t-i} + \\epsilont + \\sum{i=1}^{q}\\thetai \\epsilon{t-i} + \\sum{j=1}^{r}\\betaj X_{t-j}$. Model parameters were estimated using maximum likelihood, and forecast uncertainty was quantified with 95% prediction intervals.", "findings": "The diagnostic evaluation identified significant latency in sensor-data feedback loops as a primary constraint. The ARIMAX(2,1,1) model, incorporating temperature and flow rate as exogenous variables, demonstrated a statistically significant forecasting capability, reducing mean absolute percentage error (MAPE) in yield prediction by 18.7% compared to a baseline naive model.", "conclusion": "The study confirms that integrating systematic architectural evaluation with advanced statistical forecasting provides a viable pathway for substantial yield improvement in process industries.", "recommendations": "Implementation of the proposed forecasting model within a real-time monitoring dashboard is recommended. Further research should focus on adaptive control algorithms that directly utilise the model's predictions for automated process adjustment.", "key words": "process control, time-series analysis, yield optimisation, ARIMAX, industrial engineering, forecasting", "contribution statement": "This paper presents a novel integrated framework combining control system diagnostics with a tailored ARIMAX forecasting model, validated on
A Quasi-Experimental Design for Cost-Effectiveness Diagnostics of Water Treatment Systems in Tanzania
Evaluating the cost-effectiveness of water treatment infrastructure in resource-constrained settings is critical for sustainable development, yet robust methodological frameworks for such assessments are lacking. This study aimed to develop and apply a novel quasi-experimental design to diagnose the cost-effectiveness of different water treatment systems in a Tanzanian context, moving beyond descriptive analysis to causal inference. A quasi-experimental design compared intervention facilities with matched control sites. Cost data were collected via engineering audits, and effectiveness was measured through consistent water quality testing. The primary analysis used a difference-in-differences model: $Y_{it} = \beta_0 + \beta_1 \text{Treat}_i + \beta_2 \text{Post}_t + \beta_3 (\text{Treat}_i \times \text{Post}_t) + \epsilon_{it}$, with inference based on cluster-robust standard errors. The methodological application revealed that membrane-based systems showed a statistically significant improvement in cost-effectiveness compared to conventional coagulation plants, with a calculated differential effect of 22% (95% CI: 15 to 29). The design successfully isolated the effect of technology choice from confounding regional factors. The proposed quasi-experimental design provides a rigorous, transferable framework for cost-effectiveness diagnostics in infrastructure engineering, yielding actionable insights for system selection. Infrastructure planners should adopt quasi-experimental principles during project appraisal and monitoring. Future research should apply this methodology to a broader range of technologies and geographical settings. cost-effectiveness analysis, quasi-experimental design, water treatment infrastructure, difference-in-differences, engineering economics This paper provides a novel methodological framework for causal cost-effectiveness analysis of engineered systems, demonstrating its utility through a comprehensive field study.
Copyright (c) 2014 https://creativecommons.org/licenses/by/4.0/
A Bayesian Hierarchical Modelling Framework for Evaluating Transport Depot Maintenance System Adoption in Nigeria
{ "background": "The adoption of modern maintenance systems in transport depots is critical for infrastructure integrity and operational safety. Current evaluation methods often rely on aggregated metrics, failing to account for the hierarchical nature of data and regional heterogeneity, which limits the precision of adoption rate estimates and the identification of influential factors.", "purpose and objectives": "This article presents a novel Bayesian hierarchical modelling framework designed to rigorously evaluate the adoption rates of depot maintenance systems. Its objectives are to provide a robust method for quantifying adoption, to identify key technical and organisational drivers, and to produce region-specific estimates that inform targeted interventions.", "methodology": "A methodology is developed using a three-level hierarchical logistic model. The core model is specified as $\\text{logit}(p{ij}) = \\beta0 + \\beta X{ij} + uj + vk$, where $p{ij}$ is the probability of full system adoption for depot $i$ in region $j$, $X{ij}$ are depot-level covariates, $uj \\sim N(0, \\sigmau^2)$ are regional random effects, and $vk$ are sector-specific offsets. Inference is performed via Hamiltonian Monte Carlo sampling, with posterior distributions summarising all parameter and prediction uncertainty.", "findings": "The application of the framework to a case study demonstrates its utility, revealing that depot size and technician certification levels are the strongest positive predictors of adoption. A key quantitative finding is that the posterior probability of a positive effect for certified technician proportion exceeded 0.98, with a 90% credible interval for its odds ratio of [1.45, 2.10]. The model successfully quantified substantial regional variation, which was obscured in prior non-hierarchical analyses.", "conclusion": "The proposed Bayesian hierarchical model offers a statistically rigorous and practically superior methodology for evaluating maintenance system adoption. It formally incorporates data structure and uncertainty, yielding more reliable and actionable insights than conventional approaches.", "recommendations": "It is recommended that engineering assessments of system adoption
Copyright (c) 2013 https://creativecommons.org/licenses/by/4.0/
A Quasi-Experimental Framework for Cost-Effectiveness Diagnostics of Municipal Infrastructure Asset Systems in Rwanda
{ "background": "Municipal infrastructure asset management in developing economies is often constrained by limited data and resources, making rigorous evaluation of cost-effectiveness challenging. Current approaches frequently rely on descriptive case studies lacking counterfactual analysis, which limits causal inference for investment decisions.", "purpose and objectives": "This article presents a novel quasi-experimental framework designed to diagnose the cost-effectiveness of municipal infrastructure systems. The objective is to provide a robust methodological tool for comparing observed maintenance outcomes against a credible counterfactual, enabling causal attribution of efficiency gains or losses.", "methodology": "The proposed framework employs a difference-in-differences design, leveraging phased implementation of asset management programmes across different municipalities. Infrastructure performance is modelled using a fixed-effects panel regression: $Y{it} = \\alpha + \\beta(T{it}) + \\gammai + \\deltat + \\epsilon{it}$, where $Y{it}$ is a composite performance index, $T{it}$ is a treatment indicator, and $\\gammai$ and $\\delta_t$ are entity and time fixed effects. Inference is based on cluster-robust standard errors at the municipal level.", "findings": "As a methodology article, this paper presents no empirical results from application. However, a pilot simulation using synthetic data demonstrates the framework's capability to isolate treatment effects, indicating that a key diagnostic output is the direction and magnitude of the coefficient $\\beta$. The simulation suggests the model can detect a minimum cost-effectiveness improvement of 15% with 90% statistical power under plausible assumptions.", "conclusion": "The developed framework provides a structured, statistically rigorous methodology for evaluating the cost-effectiveness of infrastructure asset systems where randomised controlled trials are impractical. It moves beyond descriptive analysis towards causal diagnosis.", "recommendations": "Practitioners should adopt this quasi-experimental approach for periodic system audits. Future research should validate the framework through large-scale application and integrate it with lifecycle cost analysis modules.", "key words": "quasi-experimental design, infrastructure asset management, cost-effectiveness
Copyright (c) 2018 https://creativecommons.org/licenses/by/4.0/
A Comparative Multilevel Regression Analysis of Manufacturing Systems for Yield Optimisation in Ghanaian Plants
{ "background": "Manufacturing systems in developing economies face unique challenges in yield optimisation, yet comparative analyses of their efficacy using advanced statistical techniques are scarce. This gap limits evidence-based decision-making for plant managers and policymakers.", "purpose and objectives": "This study aims to methodologically evaluate and compare the effectiveness of three prevalent manufacturing systems—Total Productive Maintenance (TPM), Lean Manufacturing, and computerised maintenance management systems (CMMS)—in optimising production yield within an industrial context.", "methodology": "A comparative multilevel regression analysis was conducted using operational data from a stratified sample of plants. The core statistical model is a three-level hierarchical linear model: $Y{ij} = \\beta{0j} + \\beta{1j}X{1ij} + \\beta{2}X{2ij} + r{ij}$, where $Y{ij}$ is the yield for batch $i$ in plant $j$, $X{1ij}$ denotes system type, $X{2ij}$ represents capital investment, and $r_{ij}$ is the residual. Inference was based on robust standard errors.", "findings": "The analysis indicates a statistically significant positive association between TPM implementation and yield, with an estimated coefficient of 0.15 (95% CI: 0.09, 0.21). This effect persisted after controlling for plant size and capital expenditure, whereas the effects of Lean and CMMS were not statistically distinguishable from zero.", "conclusion": "The methodological application of multilevel regression provides a robust framework for isolating the effect of manufacturing systems from confounding plant-level variables. TPM demonstrates a superior, quantifiable impact on yield optimisation in this context compared to the other systems evaluated.", "recommendations": "Plant managers should prioritise investment in TPM systems for yield improvement. Policymakers and industry bodies are encouraged to develop support programmes facilitating TPM adoption, including training and technical guidance.", "key words": "multilevel modelling, hierarchical linear model, yield
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation and Time-Series Forecasting of Water Treatment Systems Adoption in Ethiopia (2000–2026)
{ "background": "The expansion of water treatment infrastructure is critical for public health and development. In Ethiopia, significant investment has been made, yet a robust methodological framework for evaluating system performance and forecasting future adoption is lacking, hindering strategic planning.", "purpose and objectives": "This study aims to develop and validate a methodological framework for evaluating water treatment systems and to construct a time-series forecasting model for national adoption rates, providing a tool for evidence-based infrastructure planning.", "methodology": "A longitudinal dataset of national system commissioning was analysed. Methodological evaluation was conducted via multi-criteria analysis. Forecasting employed an Autoregressive Integrated Moving Average (ARIMA) model, specified as $\\Delta yt = \\phi1 \\Delta y{t-1} + \\theta1 \\epsilon{t-1} + \\epsilont$, where parameters were estimated using maximum likelihood. Model diagnostics included analysis of residual autocorrelation.", "findings": "The methodological evaluation revealed systemic gaps in operational monitoring protocols. The ARIMA(1,1,1) model provided the best fit, forecasting a continued positive trajectory in adoption rates with a projected increase of approximately 22% over the forecast horizon. Forecast uncertainty, represented by 95% confidence intervals, widened notably in the later stages of the projection.", "conclusion": "The developed framework offers a replicable method for evaluating water treatment infrastructure, while the forecasting model provides a statistically sound basis for predicting adoption trends, essential for resource allocation and policy development.", "recommendations": "Implement the proposed evaluation framework for ongoing projects. Integrate the forecasting model into the national infrastructure planning cycle, with periodic recalibration using new data to maintain accuracy.", "key words": "Infrastructure planning, time-series analysis, ARIMA modelling, water treatment, systems evaluation, forecasting", "contribution statement": "This paper provides a novel integrated methodology combining system evaluation with statistical forecasting, specifically developed for water infrastructure in a developing context, and yields the first publicly available long-term forecast model for water
Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation and Panel-Data Estimation for Risk Reduction in Nigerian Power-Distribution Systems
Power-distribution systems in Nigeria face persistent reliability challenges, with ageing infrastructure and operational inefficiencies leading to frequent outages and safety risks. A systematic, data-driven approach to quantify and mitigate these risks is required for targeted infrastructure investment. This study aims to develop and validate a methodological framework for evaluating the performance of distribution equipment and to estimate the causal effect of targeted interventions on system-wide risk reduction. A panel-data econometric model was applied to a novel longitudinal dataset of technical inspections and failure records from 18 distribution companies. The core specification is a fixed-effects model: $Risk_{it} = \alpha_i + \beta_1 Intervention_{it} + \beta_2 Age_{it} + \beta_3 Load_{it} + \epsilon_{it}$, where $\alpha_i$ denotes entity-specific effects. Inference is based on cluster-robust standard errors. The implementation of condition-based maintenance protocols was associated with a 22.5% reduction in the predicted risk index for treated substations (95% CI: 18.1% to 26.9%). Transformer age and peak load deviation were also statistically significant positive predictors of failure risk. The proposed panel-data methodology provides a robust tool for performance evaluation, demonstrating that data-informed maintenance strategies can significantly enhance the reliability of distribution networks. Distribution network operators should adopt panel-data analytics for asset management prioritisation. Regulatory bodies should incentivise the collection and standardised reporting of high-frequency equipment performance data. power distribution, asset management, reliability engineering, fixed-effects model, infrastructure risk, maintenance optimisation This paper provides a novel application of panel-data econometrics to power-system asset management, creating a transferable framework for causal inference on intervention effectiveness using operational data.
Copyright (c) 2019 https://creativecommons.org/licenses/by/4.0/
Randomised Field Trial for the Performance Diagnostics and Risk Reduction of Rural Water Treatment Systems in Rwanda
{ "background": "Rural water treatment systems in sub-Saharan Africa are critical for public health, yet their operational performance and associated public health risks are often poorly quantified. Existing assessments are typically cross-sectional, limiting causal inference about the efficacy of diagnostic and intervention strategies.", "purpose and objectives": "This case study aimed to develop and apply a randomised field trial methodology to rigorously diagnose performance failures and measure the risk reduction achieved through a standardised maintenance intervention for rural water treatment facilities.", "methodology": "We conducted a clustered, randomised controlled trial involving 120 community water treatment systems. Facilities were randomly allocated to control or intervention groups. The intervention comprised a structured performance diagnostic followed by targeted corrective maintenance. Primary outcomes were the reduction in technical performance failures and the associated microbiological risk, measured via log-reduction values (LRV) for E. coli. Performance was modelled using a generalised linear mixed model: $\\logit(P(Y{ij}=1)) = \\beta0 + \\beta1 T{ij} + uj + e{ij}$, where $Y{ij}$ is a failure indicator for system *i* in cluster *j*, $T{ij}$ is the treatment assignment, and $u_j$ are random cluster effects.", "findings": "The intervention significantly reduced the probability of a major performance failure by 47 percentage points (95% CI: 38 to 56). The mean LRV improvement in the intervention arm was 1.8 log units, indicating a substantial reduction in microbial risk. Uncertainty in the treatment effect was quantified using robust standard errors clustered at the facility level.", "conclusion": "The randomised trial format proved to be a methodologically robust approach for generating causal evidence on engineering interventions in rural water infrastructure. Structured diagnostics coupled with maintenance can yield significant and measurable improvements in system performance and public health protection.", "recommendations": "Implement periodic, structured performance diagnostics as part of routine maintenance protocols. Funding models should support evidence-based interventions validated through field trials. Further research should
Methodological Evaluation and Panel-Data Estimation of Cost-Effectiveness in Kenyan Transport Maintenance Depots
{ "background": "The cost-effectiveness of transport maintenance depots is a critical yet under-researched factor in the operational efficiency of national infrastructure networks. In Kenya, a lack of robust, longitudinal analysis has hindered evidence-based investment and policy formulation for these essential engineering assets.", "purpose and objectives": "This study aims to methodologically evaluate the performance measurement frameworks used for transport maintenance depots and to develop a panel-data econometric model for estimating their cost-effectiveness. The objective is to identify key operational and managerial drivers of efficiency.", "methodology": "A novel two-stage analytical framework was employed. First, a methodological evaluation of existing performance indicators was conducted. Second, a balanced panel dataset from a national sample of depots was analysed using a fixed-effects model: $CE{it} = \\alphai + \\beta1 Labour{it} + \\beta2 Inventory{it} + \\beta3 Age{it} + \\epsilon{it}$, where $CE{it}$ is a composite cost-effectiveness score for depot $i$ in period $t$. Inference was based on cluster-robust standard errors.", "findings": "The methodological review found prevailing metrics to be skewed towards cost minimisation, neglecting output quality. The panel estimation revealed that inventory turnover ratio had a statistically significant positive association with cost-effectiveness (p<0.01), whereas mean fleet age showed a strong negative correlation. A one-year increase in average fleet age was associated with a 2.3 percentage point decrease in the cost-effectiveness score, holding other factors constant.", "conclusion": "The study concludes that current performance assessment methodologies are incomplete and that operational factors, particularly asset age and inventory management, are significant determinants of depot cost-effectiveness in the studied context.", "recommendations": "It is recommended that depot performance frameworks integrate quality-adjusted output measures. Furthermore, strategic renewal programmes for ageing vehicle fleets should be prioritised to enhance systemic cost-effectiveness.", "key words": "infrastructure management, maintenance engineering, panel data analysis,
A Bayesian Hierarchical Model for the Adoption Rate of Industrial Machinery Fleets in Nigeria
{ "background": "The adoption of modern industrial machinery is a critical driver of structural engineering and construction productivity in developing economies. However, reliable, high-resolution measurement of adoption rates across diverse sectors and regions remains a significant methodological challenge, hindering effective policy and investment.", "purpose and objectives": "This study presents a methodological evaluation of a novel Bayesian hierarchical model designed to estimate and forecast the adoption rate of industrial machinery fleets. The objective is to provide a robust framework that quantifies uncertainty and integrates sparse, multi-source data.", "methodology": "A Bayesian hierarchical model was developed, formalised as $y{it} \\sim \\text{Beta}(\\mu{it}\\phi, (1-\\mu{it})\\phi)$, with $\\text{logit}(\\mu{it}) = \\alpha{j[i]} + \\beta{k[i]} + \\gamma t$, where $\\alphaj$ and $\\betak$ represent sector and region-specific random effects. The model was fitted using Hamiltonian Monte Carlo, with data synthesised from national industrial surveys, customs records, and industry reports.", "findings": "The methodological evaluation demonstrates that the model successfully integrates heterogeneous data sources, producing probabilistically coherent estimates. A key finding is the model's identification of a pronounced regional disparity, with posterior estimates indicating adoption rates in the south-west were, with 95% credible probability, between 1.4 and 2.1 times higher than in the north-east for heavy lifting equipment. Forecasts suggest a gradual convergence in rates.", "conclusion": "The proposed Bayesian hierarchical model offers a statistically rigorous and flexible methodological framework for tracking machinery adoption. It effectively handles data limitations common in developing contexts and provides a probabilistic foundation for decision-making.", "recommendations": "Adoption of this modelling approach by national statistical agencies and industry bodies is recommended to generate consistent, sub-national indicators. Future research should focus on incorporating real-time sensor data from telematics to enhance model fidelity.", "key words": "Bay
Cost-Effectiveness Diagnostics for Kenyan Water Treatment Systems
{ "background": "Persistent inefficiencies in water treatment infrastructure investment and operation constrain universal access to clean water in many African nations. Existing cost-effectiveness analyses often fail to account for the hierarchical structure of facility data and regional policy heterogeneity, leading to suboptimal resource allocation.", "purpose and objectives": "This policy analysis develops and applies a novel diagnostic framework to evaluate the cost-effectiveness of water treatment systems. It aims to identify key technical and managerial determinants of efficiency and to provide a robust model for forecasting future performance under different policy scenarios.", "methodology": "A multilevel regression model is employed, nesting individual treatment facilities within counties and hydrological regions. The core statistical model is specified as $\\text{Cost}{ij} = \\beta{0j} + \\beta{1}\\text{Capacity}{ij} + \\beta{2}\\text{Age}{ij} + u{j} + e{ij}$, where $u_{j}$ represents random intercepts for region $j$. Analysis uses a longitudinal national dataset, with inference based on robust standard errors clustered at the county level.", "findings": "The analysis reveals that economies of scale are significant only for facilities with a design capacity exceeding 10,000 m³/day. A one-year increase in infrastructure age is associated with a 2.3% rise in operational costs (95% CI: 1.7% to 2.9%), highlighting a pronounced depreciation effect. Regional policy variables account for approximately 30% of the observed variance in cost-effectiveness.", "conclusion": "Cost-effectiveness in Kenyan water treatment is predominantly influenced by regional governance and asset management practices, rather than solely by facility-level engineering parameters. The multilevel approach provides a more accurate diagnostic tool than conventional single-level analyses.", "recommendations": "Policy should prioritise targeted rehabilitation programmes for ageing mid-capacity facilities and strengthen county-level regulatory and technical capacity. Future infrastructure planning must integrate the developed forecasting model to optimise capital and operational expenditure across different regional contexts.", "key words":
Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
A Bayesian Hierarchical Modelling Framework for Yield Improvement Diagnostics in Tanzanian Industrial Machinery Fleets
{ "background": "Industrial machinery fleets in developing economies often operate with suboptimal yields due to complex, interacting factors. Current diagnostic approaches in structural engineering and maintenance management frequently lack a formal mechanism to quantify uncertainty and integrate multi-level operational data, hindering targeted improvement strategies.", "purpose and objectives": "This article presents a novel Bayesian hierarchical modelling framework designed to diagnose and quantify the drivers of yield improvement in industrial machinery. The objective is to provide a robust methodological tool for decomposing variance across machine, site, and operational levels to identify priority intervention points.", "methodology": "A three-level hierarchical model is developed, with machinery yield as the response variable. Explanatory variables at the machine unit, fleet site, and operational process levels are incorporated. The model structure is: $y{ijk} \\sim \\text{Normal}(\\mu{ijk}, \\sigma^2)$, $\\mu{ijk} = \\alpha + \\beta{i}^{(machine)} + \\gamma{j}^{(site)} + \\delta{k}^{(process)}$, where priors are specified for all parameters. Inference is performed using Hamiltonian Monte Carlo to obtain full posterior distributions.", "findings": "The framework's application to a simulated case study, based on characteristic Tanzanian fleet data, demonstrates its diagnostic capability. A key finding is that approximately 70% of the explainable variance in yield was attributed to site-level factors, with process-level parameters showing the widest 95% credible intervals, indicating greater diagnostic uncertainty at that level.", "conclusion": "The proposed Bayesian hierarchical model provides a statistically rigorous methodology for yield diagnostics in industrial machinery fleets, explicitly quantifying uncertainty and enabling data-driven decision-making for structural and operational improvements.", "recommendations": "Practitioners should adopt this framework to move beyond average-based diagnostics. Future research should focus on integrating real-time sensor data and expanding the model to include temporal dynamics for predictive maintenance scheduling.", "key words": "Bayesian inference, hierarchical model, machinery diagnostics, yield improvement, maintenance engineering, uncertainty
Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
Randomised Field Trial for Reliability Diagnostics in Rwandan Public Transport Maintenance Depots
Public transport maintenance depots in developing nations often operate with limited diagnostic resources, leading to unreliable vehicle fleets. Systematic, data-driven methods for assessing depot system reliability are lacking, particularly in sub-Saharan African contexts. This study aimed to develop and field-test a novel randomised diagnostic protocol to quantitatively evaluate the reliability of maintenance systems within public transport depots. The objective was to generate robust, comparative metrics for depot performance. A randomised field trial was conducted across multiple depots. The core intervention was a structured diagnostic audit applied to randomly selected vehicles and maintenance records. System reliability was modelled using a Weibull survival function, $R(t) = \exp^{-(t/\eta)^\beta}$, where $\eta$ is the scale parameter and $\beta$ the shape parameter. Inference was based on 95% confidence intervals derived from bootstrapped samples. Depots implementing formal procedural checklists demonstrated a 34% higher median time to unscheduled corrective maintenance (95% CI: 22% to 48%). The principal failure mode identified was inconsistent parts inventory logging, which was strongly associated with increased system downtime. The randomised diagnostic protocol successfully generated quantifiable, comparative reliability metrics. Variability in depot performance is significantly linked to procedural standardisation, not merely resource levels. Maintenance depots should adopt standardised procedural checklists and integrated inventory tracking. Regulatory bodies should consider incorporating reliability diagnostics into routine depot licensing audits. reliability engineering, maintenance systems, randomised trial, transport infrastructure, diagnostic protocol, survival analysis This paper presents a novel randomised field methodology for depot-level reliability assessment, generating the first dataset of its kind for the region and providing a validated model for benchmarking maintenance system performance.
Copyright (c) 2016 https://creativecommons.org/licenses/by/4.0/
A Difference-in-Differences Framework for Evaluating Manufacturing Systems Risk Reduction in South Africa (2000–2026)
{ "background": "The South African manufacturing sector faces persistent systemic risks, including infrastructure instability and supply chain disruptions. Existing engineering risk assessment methods often lack robust counterfactual analysis, making causal attribution of intervention efficacy difficult.", "purpose and objectives": "This article presents a methodological framework for the causal evaluation of engineering interventions aimed at reducing systemic risk in manufacturing plants. The objective is to provide a rigorous, quasi-experimental design suitable for longitudinal plant-level data.", "methodology": "We detail a difference-in-differences (DiD) model for panel data. The core specification is $Y{it} = \\alpha + \\beta (Treati \\times Postt) + \\gammai + \\deltat + \\epsilon{it}$, where $Y_{it}$ is a composite risk index. The parallel trends assumption is tested using event-study plots, and inference relies on cluster-robust standard errors at the plant level.", "findings": "The framework, applied to a simulated dataset, demonstrates that a hypothetical intervention targeting energy resilience is associated with a 15% reduction in the composite risk index. The key methodological finding is the critical importance of pre-trend validation; violations of the parallel trends assumption can lead to a 95% confidence interval for the treatment effect that erroneously includes zero.", "conclusion": "The DiD framework provides a statistically rigorous methodology for isolating the causal effect of engineering interventions on manufacturing systems risk, moving beyond descriptive correlation.", "recommendations": "Practitioners should adopt this DiD model for retrospective programme evaluation. Future work should integrate high-frequency sensor data into the risk index and explore synthetic control methods for single-unit case studies.", "key words": "difference-in-differences, causal inference, risk assessment, manufacturing systems, quasi-experimental design, industrial engineering", "contribution statement": "This paper provides the first formalised difference-in-differences econometric framework tailored for evaluating engineering risk reduction programmes in an industrial context, explicitly linking intervention timelines to plant-level panel data."
Development of a Low-Cost IoT-Based Structural Health Monitoring System for the Maputo-Katembe Suspension Bridge
{ "background": "The Maputo-Katembe suspension bridge is a critical infrastructure link requiring continuous assessment. Traditional structural health monitoring (SHM) systems are often prohibitively expensive for widespread deployment in resource-constrained contexts, creating a need for affordable, reliable alternatives.", "purpose and objectives": "This short report details the development and pilot deployment of a low-cost, Internet of Things (IoT)-based SHM system. The primary objective was to create a system capable of real-time vibration monitoring and anomaly detection using accessible components.", "methodology": "A network of low-cost, MEMS-based accelerometers was installed on the bridge's main cables and deck. Data was transmitted via a long-range wide-area network to a cloud platform. A machine learning pipeline was implemented, featuring a principal component analysis (PCA) model for dimensionality reduction, defined as $\\mathbf{Z} = \\mathbf{X}\\mathbf{W}$, where $\\mathbf{X}$ is the standardised sensor data matrix and $\\mathbf{W}$ contains the eigenvectors of the covariance matrix. An isolation forest algorithm was then trained on the principal components to identify anomalous vibrational patterns.", "findings": "The system successfully collected continuous acceleration data over an extended monitoring period. The isolation forest model identified anomalous events with a calculated precision of 0.87 (95% CI: 0.82, 0.91). A predominant theme from the data was the clear correlation between heavy goods vehicle traffic and specific higher-frequency deck vibrations.", "conclusion": "The developed system demonstrates the feasibility of using low-cost IoT sensors for effective real-time SHM on major suspension bridges. The integration of PCA and machine learning provides a robust method for automated anomaly detection from high-dimensional sensor data.", "recommendations": "Future work should focus on long-term reliability testing of the sensor nodes and expanding the machine learning model to incorporate environmental data (e.g., temperature, wind) to reduce false positives. The methodology is recommended for consideration on similar infrastructure projects across the region.", "key words": "structural