A Bayesian Hierarchical Model for Risk Reduction in Nigerian Municipal Infrastructure Asset Management Systems

"background": "Municipal infrastructure asset management in developing nations faces significant challenges due to data scarcity, heterogeneous asset conditions, and uncertain environmental stressors. Current deterministic models often fail to adequately quantify and propagate these uncertainties, leading to suboptimal maintenance and investment decisions. ", "purpose and objectives": "This study develops and validates a novel probabilistic framework to quantify risk reduction within municipal infrastructure systems. The primary objective is to provide a robust, data-informed methodology for prioritising maintenance interventions under uncertainty. ", "methodology": "A Bayesian hierarchical model is constructed, integrating sparse inspection data with expert judgement. The core model is specified as y{ij \ (\ + \ Xij, \²), \ \ (\\, \²), where yij is the degradation state of asset i in municipality j, and X₈₉ are covariates. Posterior distributions for failure probabilities and risk reduction are estimated using Hamiltonian Monte Carlo sampling. ", "findings": "The model application to a network of drainage and road assets demonstrated that prioritisation based on posterior expected utility reduced projected systemic risk by an estimated 34% compared to conventional age-based schedules. Crucially, the 90% credible intervals for risk reduction estimates highlighted the value of incorporating epistemic uncertainty, with intervals narrowing by up to 60% as hierarchical data pooling increased. ", "conclusion": "The Bayesian hierarchical approach provides a statistically rigorous mechanism for infrastructure risk assessment, effectively synthesising fragmented data to inform management strategies under deep uncertainty. ", "recommendations": "Municipal authorities should adopt probabilistic asset management frameworks that explicitly model uncertainty. Initial implementation should focus on critical asset classes where inspection data can be systematically pooled across administrative boundaries. ", "key words": "Bayesian inference, infrastructure risk, asset management, probabilistic modelling, decision support", "contribution statement": "This paper presents a novel hierarchical Bayesian model that formally quantifies epistemic uncertainty in infrastructure