Smarter Asset Management Starts with Better Decisions: Register to IRF’s Road Asset Management Course

Jul 19 / IRF Academy
Road agencies today are expected to deliver safer, more resilient and reliable road networks while managing ageing infrastructure, increasing climate risks and tightening budgets. Meeting these expectations demands smarter, data-driven approaches to managing infrastructure throughout its lifecycle.

To help professionals respond to these needs, IRF is offering the Next Generation Road Asset Management: Smart Strategies for Resilient and High-Performing Road Networks Course, an online course taking place from 27 October to 5 November 2026.

Across four live sessions, participants will explore the latest practices in performance-based asset management, predictive maintenance and digital infrastructure management to support better investment decisions and long-term network performance.

From Reactive Maintenance to Strategic Asset Management

Modern asset management is increasingly driven by performance, risk and evidence rather than fixed maintenance schedules. The course examines how leading road agencies are using condition data, predictive analysis and digital technologies to improve decision-making, extend asset life and maximise the value of infrastructure investments.

Participants will gain practical frameworks that can be adapted to different institutional settings, whether managing national highways or local road networks.

During the programme, participants will learn how to:

  • Define appropriate levels of service for different road networks.
  • Apply lifecycle thinking to maintenance planning.
  • Prioritise interventions using condition assessments and risk analysis.
  • Support investment decisions through performance indicators and multi-criteria analysis.
  • Incorporate predictive analytics, monitoring systems and digital tools into asset management practices.
  • Integrate safety, resilience and operational performance into long-term infrastructure planning.

What the Course Covers

The programme combines strategic concepts with practical implementation through four interactive online sessions.

Participants begin by exploring the principles of modern road asset management, including governance, service levels and lifecycle management. They then examine how agencies collect and interpret condition data to prioritise maintenance and investment decisions.

The course continues with predictive asset management, demonstrating how digital technologies, monitoring systems and forecasting techniques support more proactive maintenance strategies. The final session focuses on implementation, covering governance, funding models, performance-based delivery and organisational practices that enable agencies to embed asset management into day-to-day operations.

Designed for Infrastructure Professionals

The course is intended for professionals responsible for planning, maintaining and managing road infrastructure assets, including road agency managers, maintenance engineers, asset management specialists, infrastructure planners, consultants, multilateral development bank teams and professionals working in resilience, road safety and infrastructure digitalisation.

Learn from an Industry Leader

The programme is led by Scott Bloxsom, CEO and Co-owner of Essency and a Chartered Professional Engineer with more than three decades of international experience. Having advised road agencies across Australia, Europe, Africa, the Middle East and North America, Scott brings extensive expertise in asset management systems, Performance-Based Contracts and the application of ISO 55000 standards to improve infrastructure performance and long-term value.

Register Now

As road networks become more complex and expectations for performance continue to grow, agencies need professionals who can make informed, evidence-based infrastructure decisions.

This course provides the practical knowledge, tools and international perspectives needed to modernise road asset management and build more resilient, high-performing networks.
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