Pavement condition indices in Canada: survey of practices and a framework for harmonized measurement

A systematic survey of 14 Canadian transportation agencies reveals that while 86% rely on pavement condition indices for decision-making, satisfaction is low, with only 36% of agencies believing these indices accurately reflect on-ground realities. This disconnect stems from a fragmented assessment of disparate distress classifications and incompatible severity thresholds that hinder interoperability. This paper argues that while a single universal index is impractical due to diverse regional conditions, harmonization of the underlying measurement architecture is both achievable and necessary. To bridge this semantic gap, the paper proposes a sensor-grounded harmonization framework that decouples objective physical measurement from subjective policy interpretation. The framework utilizes high-resolution Light Detection and Ranging and imaging to generate standardized geometric primitives, specifically, topological cracking graphs and deformation models, which serve as the immutable building blocks for assessment. Through formalizing a three-stage architecture (sensor-derived measurement, agency-specific calibration, and index aggregation), the study provides a scalable pathway toward artificial intelligence-enabled, standardized pavement management that preserves local agency autonomy.