{ "background": "Municipal infrastructure asset management in developing contexts is challenged by data scarcity and heterogeneous asset conditions, complicating reliable risk assessment. Traditional deterministic models often fail to adequately quantify uncertainty, which is critical for prioritising maintenance investments.", "purpose and objectives": "This study evaluates a novel Bayesian hierarchical modelling methodology for quantifying risk reduction in municipal infrastructure systems. The objective is to provide a robust, probabilistic framework for asset managers to optimise intervention strategies under uncertainty.", "methodology": "A Bayesian hierarchical model was developed and applied to a dataset of municipal water and road assets. The core model structure is $y{ij} \\sim \\text{Beta}(\\mu{ij}\\phi, (1-\\mu{ij})\\phi)$, with $\\text{logit}(\\mu{ij}) = \\alpha{j[i]} + \\beta X{ij}$, where $\\alphaj \\sim \\text{Normal}(\\mu{\\alpha}, \\sigma_{\\alpha})$, capturing asset- and network-level variability. Posterior distributions were estimated using Hamiltonian Monte Carlo.", "findings": "The model successfully quantified the probabilistic reduction in failure risk from proposed interventions. For a representative subset of water pipelines, targeted rehabilitation was associated with a median reduction in annual failure probability of 42% (90% credible interval: 35% to 48%). The hierarchical structure effectively pooled information across asset classes, improving inference for data-sparse assets.", "conclusion": "The Bayesian hierarchical approach provides a statistically rigorous framework for infrastructure risk assessment, formally integrating uncertainty and variability across asset hierarchies. It represents a significant methodological advance over common point-estimate practices.", "recommendations": "Asset management authorities should adopt probabilistic risk models to inform capital planning. Future work should integrate the model with long-term financial planning tools and expand the asset condition database to enhance prior information.", "key words": "Bayesian statistics, infrastructure risk, asset management, probabilistic modelling, hierarchical model, maintenance planning", "contribution statement": "This