Highways Maintenance and Renewal Scheduling Exploiting New Data Sources and Digital Twins

In the UK, over £5 billion is spent annually on highways maintenance and renewal, currently managed through cyclical and reactive methods. Strict budget rules and the high cost of asset data collection hinder flexibility and efficiency. This research aims to create a decision-making framework for optimizing highways maintenance, leveraging emerging data sources like connected vehicles and drone sensors.The framework will integrate multi-objective factors-such as cost, safety, and sustainability-while addressing organizational constraints.

It will be tested theoretically and in practice with highways authorities to support more responsive, data-driven maintenance strategies.

This research delves into the evolving landscape of highways maintenance, focusing on how emerging technologies can be leveraged to transform current practices. At its core lies the question of how new data sources, such as connected vehicles and drone-mounted sensors, can be harnessed to continuously update Digital Twin models, creating a real-time, dynamic representation of highway assets.

Furthermore, it explores how Digital Twins can not only model individual assets but also manage entire networks, capturing the complex, interconnected nature of infrastructure systems.