Improving ML & AI Investment: Austroads Good Practice Recommendations

Demand for Machine Learning (ML) and Artificial Intelligence (AI) is increasing across the engineering sector. In particular, road agencies and their customers, who are demanding smarter decisions about infrastructure investments. This presents both a major opportunity and a major challenge:

How can we ensure the safe and reliable use of these technologies while achieving better results than traditional manual asset investment processes?

Our WSP team recently undertook a project for Austroads facilitating the use of ML/AI tools to support improved road asset investment – with potential to deliver significant efficiencies to road authorities through the use of decision-making support tools for pavement asset managers. The Quick Reference guide produced as part of the project outlines how all road agencies can participate and reap the benefits; a resource primarily designed for the road sector with applicability across the Engineering sector for better ML investment practices.

This business-friendly webinar will be of benefit to all engineers with an interest in Machine Learning and Artificial Intelligence; road agencies, infrastructure asset investors, asset managers, project managers, data scientists, data and systems specialists.

Presenters:
Ross Guppy, Transport Infrastructure Program Manager, Austroads
Dave Rawlinson, Principal Data Scientist, WSP in Australia
Tim Cross, Business Intelligence Advisory Manager, Technical Principal, WSP in New Zealand

Access Austroads reference material: https://austroads.com.au/publications...