A Bayesian Hierarchical Model for the Reliability Assessment of Municipal Infrastructure Asset Systems in Rwanda

{ "background": "The reliability assessment of municipal infrastructure asset systems in developing nations is often hampered by sparse, heterogeneous, and uncertain data. Conventional reliability models struggle to integrate multi-source information and quantify epistemic uncertainty, limiting their utility for asset management decision-making.", "purpose and objectives": "This study develops and applies a novel Bayesian hierarchical model to evaluate the system reliability of municipal infrastructure assets, specifically water distribution networks and road segments. The objective is to provide a robust probabilistic framework that accounts for data limitations and supports infrastructure investment prioritisation.", "methodology": "A Bayesian hierarchical modelling framework is proposed, integrating condition data from municipal audits with expert judgement. The model structure is $\\lambda{ij} \\sim \\text{Gamma}(\\alphaj, \\betaj)$, where $\\lambda{ij}$ is the failure rate for asset $i$ in group $j$, with group-level parameters $\\alphaj, \\betaj$ drawn from community-wide hyperpriors. Posterior distributions were estimated using Markov chain Monte Carlo simulation.", "findings": "The model successfully quantified system reliability, revealing a posterior probability of 0.87 that the median time-to-failure for road assets falls below the target threshold. Analysis indicated that approximately 30% of the variability in failure rates was attributable to differences between municipal districts, highlighting systemic regional disparities.", "conclusion": "The Bayesian hierarchical model provides a statistically robust and operationally relevant tool for assessing infrastructure system reliability under data-scarce conditions. It formally incorporates uncertainty, yielding a more nuanced understanding of asset performance to inform management strategies.", "recommendations": "Municipal engineers should adopt probabilistic reliability assessments to guide maintenance planning. Future work should integrate the model with lifecycle cost analysis to optimise rehabilitation interventions across asset portfolios.", "key words": "Infrastructure reliability, Bayesian statistics, asset management, hierarchical model, probabilistic assessment", "contribution statement": "This paper presents a novel methodological framework for infrastructure reliability analysis that explicitly models data scarcity and regional heterogeneity, a significant advancement for asset