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

{ "background": "The reliability assessment of municipal infrastructure asset systems in developing nations is hindered by sparse, heterogeneous, and uncertain data. Traditional deterministic models often fail to capture the systemic uncertainties and hierarchical dependencies inherent in such complex networks.", "purpose and objectives": "This study aims to develop and validate a novel Bayesian hierarchical modelling framework for the reliability assessment of integrated municipal infrastructure systems, specifically addressing data scarcity and integrating expert judgement with sparse field observations.", "methodology": "A Bayesian hierarchical model was constructed, formalised as $y{ij} \\sim \\text{Bernoulli}(\\theta{ij}),\\; \\text{logit}(\\theta{ij}) = \\alpha{j[i]} + \\beta X{ij},\\; \\alphaj \\sim \\mathcal{N}(\\mu{\\alpha}, \\sigma{\\alpha}^2)$. Parameter inference was performed via Hamiltonian Monte Carlo sampling. The model was applied to a dataset comprising condition states and failure records for water, road, and drainage assets across multiple Ugandan municipalities.", "findings": "The model quantified substantial variability in system reliability between different asset types and regions, with posterior distributions revealing a 95% credible interval for the baseline log-odds of failure ranging from -2.1 to 0.8. Drainage subsystems were identified as the least reliable component, with a median probability of being in a failed state approximately 1.7 times higher than water distribution assets.", "conclusion": "The proposed Bayesian hierarchical framework provides a robust, probabilistic tool for infrastructure reliability assessment under data-scarce conditions, effectively characterising uncertainty and enabling asset-level and system-level inferences.", "recommendations": "Municipal engineers and asset managers should adopt probabilistic reliability models to inform maintenance prioritisation. Future work should integrate temporal components for degradation forecasting and expand the model to include socio-economic covariates.", "key words": "Bayesian inference, infrastructure reliability, hierarchical modelling, asset management, probabilistic methods", "contribution statement": "This paper presents a novel methodological framework