Using AI in Road Assets Operation and Maintenance

Details

This course introduces participants to advanced applications of AI in the operation and maintenance of road infrastructure. Participants will explore how AI-driven tools such as predictive analytics, computer vision, and IoT data integration can improve asset condition assessment, maintenance planning, and resource allocation. The course combines theoretical foundations with practical case studies and system demonstrations, with a focus on digital transformation strategies for road authorities and transport infrastructure entities.

Target Group

Target Group:

• Road maintenance engineers and project managers
• Public works officials and transport authority personnel
• Asset management professionals in infrastructure agencies
• Civil and transport engineers focused on innovation
• IT and digital transformation leads in infrastructure sectors
  

Goals

• Understand the role of AI in road asset lifecycle management.
• Identify AI technologies applicable to inspection, monitoring, and maintenance.
• Utilize predictive maintenance and fault detection models for roads and structures.
• Analyze data from sensors, drones, and GIS to enhance asset performance.
• Integrate AI into existing asset management systems and processes.
• Support digital transformation in road maintenance operations using AI.
  

Target Competencies

Target Competencies:

• AI literacy in infrastructure maintenance
• Data-driven decision-making and predictive modeling
• Integration of AI with asset management systems
• Digital strategy development for public assets
• Operational planning and maintenance optimization
  

Outlines

Foundations of AI in Infrastructure Asset Management

• Overview of AI technologies (ML, CV, NLP) in infrastructure
• AI and the digital transformation of asset operations
• Lifecycle concepts of road assets (pavements, signage, structures)
• AI in asset registration, valuation, and depreciation tracking
• Benefits and risks of AI in public infrastructure

Data Collection and Integration for AI Models

• Types of data: condition, usage, weather, traffic, and materials
• IoT, sensor networks, and edge computing for roads
• GIS, LIDAR, drone imaging, and real-time data streams
• Data cleaning, labeling, and integration into centralized systems
• Building data pipelines for predictive and prescriptive analytics