Probabilistic infrastructure performance models: An iterative-methods approach

摘要

y High fidelity infrastructure performance models are critical for transportation planning agencies to develop cost-effective and sustainable resource allocation policies. This paper presents a new, iterative-methods approach to estimate infrastructure performance models based on sampling theory. The model addresses the issue around measurement uncertainty underlying infrastructure condition assessments for continuous distress indicators and its effect on the parametric models underlying decision-support tools. Through a case study of pavement roughness data collected as part of FHWA's long-term pavement performance program, the new approach reduces the unexplained variance that would typically enter decision-support tools by 14%. It also addresses concerns around heteroscedasticity surrounding conventional methods, allowing modelers to recover efficiency in their statistical estimates. The proposed methodology is of particular significance for decision-makers and stakeholders evaluating infrastructure distress data subject to considerable uncertainty. The contributions of this research will allow transportation agencies to integrate improved performance models within their asset management frameworks.