White Papers

Artificial Intelligence (AI) Derived Pavement Condition Indices: RoadBotics Ratings and HD-PCI

Over the past decade, artificial intelligence (AI)-based technologies have revolutionized the design and maintenance of infrastructure assets. Although AI is not equipped to address many aspects of construction, it has proven successful in optimizing design, managing risk, monitoring safety, and automating inspections and assessments.

These technologies are beneficial for increasing productivity, improving overall efficiency, and conserving resources. However, as is the case with inspections and assessments, they are currently not widely adopted.

Pavement Management Pilot: UPWP Task 5.32

Public works departments in North Florida and throughout the nation have significant backlogs in pavement management needs. Identifying and predicting pavement failures early can result in significant savings over rehabilitation, resurfacing or reconstruction which is required if adequate maintenance cannot be performed.

This pilot project compares traditional pavement rating systems used by Florida Department of Transportation (FDOT), municipalities such as Clay County and machine-learning video-based technologies to develop low-cost pavement management systems for small and rural communities.

UKPMS and RoadBotics: A Comparison

RoadBotics offers a universal 1-5 pavement surface condition rating based on the visual assessment of high quality roadway image data. To help road managers in the United Kingdom better understand this 1-5 RoadBotics rating system, the following analysis compares the United Kingdom Pavement Management System (UKPMS) rating system with RoadBotics. The specific case that is detailed in this study compares RoadBotics pavement assessment data and UKPMS data in Westminster, London.