Randomised Field Trial for Reliability Diagnostics of Municipal Infrastructure Asset Systems in Rwanda

Municipal infrastructure asset systems in many developing nations face significant reliability challenges, yet diagnostic methodologies often rely on non-randomised, convenience-based sampling, which can introduce substantial bias into system performance estimates. This study aimed to evaluate the methodological efficacy of a randomised field trial for generating unbiased reliability diagnostics of municipal water distribution networks, with the objective of establishing a robust framework for infrastructure system assessment. A stratified randomised sampling design was implemented across a network of municipal water systems. System reliability was modelled using a proportional hazards framework, $h(t|X) = h_0(t) \exp(\beta_1 X_1 + \beta_2 X_2)$, where covariates included pipe material and soil corrosivity. Inference was based on robust standard errors to account for cluster-level heterogeneity. The randomised trial design reduced spatial sampling bias by an estimated 40% compared to prior non-randomised audits. A key technical finding was that asbestos cement pipes exhibited a hazard ratio of 2.3 (95% CI: 1.7 to 3.1) for failure relative to ductile iron, controlling for soil conditions. The randomised field trial provides a methodologically superior approach for diagnosing infrastructure reliability, yielding less biased and more generalisable estimates of system performance crucial for asset management planning. Municipal engineers and asset managers should adopt randomised sampling strategies for baseline condition assessments. Future research should integrate this diagnostic method with predictive maintenance algorithms. infrastructure reliability, asset management, randomised trial, proportional hazards model, water distribution networks This paper provides the first application of a formally randomised field trial methodology for municipal infrastructure diagnostics in a sub-Saharan context, demonstrating its utility in reducing spatial sampling bias.