The adoption of systematic maintenance practices in transport infrastructure is critical for asset longevity, yet robust methodological frameworks for evaluating the implementation of such systems in developing contexts are scarce. This paper develops and applies a quasi-experimental methodological framework to evaluate the adoption rate of a new centralised maintenance depot system within a national transport agency. A difference-in-differences design was employed, comparing depot performance metrics before and after system implementation against a control group of depots using legacy practices. The core statistical model is $Y_{it} = \beta_0 + \beta_1 \text{Treat}_i + \beta_2 \text{Post}_t + \delta (\text{Treat}_i \times \text{Post}_t) + \epsilon_{it}$, where inference relies on cluster-robust standard errors at the depot level. The framework's application indicates a positive but heterogeneous treatment effect. Preliminary analysis suggests a significant increase in planned maintenance compliance, with an estimated average treatment effect of 18 percentage points (95% CI: 12 to 24). Adoption was more pronounced in urban depots with higher initial technical capacity. The quasi-experimental framework provides a rigorous, evidence-based method for isolating the causal effect of systematic maintenance interventions, moving beyond descriptive case studies. Future evaluations of engineering systems in similar contexts should incorporate quasi-experimental designs to strengthen causal claims. Policy rollout should consider phased implementation targeting higher-capacity units first to demonstrate proof of concept. quasi-experimental design, infrastructure maintenance, difference-in-differences, adoption rate, evaluation methodology This paper provides a novel methodological framework for the causal evaluation of engineering system adoption in resource-constrained settings, demonstrating its application to a national transport maintenance programme.