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 \ (\, \), where is the failure rate for asset i in group j, with group-level parameters \, \ 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