Development of Machine Learning Decision Support Tools for Pavement Asset Management

This is the second webinar in a two-webinar series. The session describes two case studies in the use of machine learning (ML) and artificial intelligence (AI) to create decision-support tools for pavement asset management.

In the first study, we explore whether ML can learn to reproduce expert treatment decisions and automatically identify candidate projects, using historic condition, treatment and inventory data.

In the second study, we explore extensions to conventional Pavement Management System (PMS) optimisation tools which provide insight into the network-wide implications of various multi-criteria funding allocation scenarios, and the levels of service that can potentially be realised.

The webinar is presented by Tim Cross and David Rawlinson.