TrialsNet

This case will be implemented in two areas within the Greek Cluster: the Athens International Airport and the public infrastructure provided by DAEM in the Municipality of Athens. The solution will utilise data from various sources, such as municipal vehicles, weather information, security cameras, drones and robots, to assess the structural health of buildings, pavements, and roads. The data collected will allow for more efficient and effective proactive management of public infrastructure assets, leading to cost savings and improved operations and services.

Augmented Reality (AR) will allow construction workers to have an on-site view of buildings or other assets blueprints and receive live bidirectional communications with remote experts who can provide assistance and video instructions. Remotely controlled or unmanned vehicles will reduce risk and accelerate the building process. AI techniques, such as Neural Networks (NN) and Deep Learning (DL), will be used to assess the state of public infrastructure assets, produce alerts and suggestions for city authorities, improve workers’ safety, and schedule predictive maintenance. Digital Twins of public construction sites will validate complicated technical plans without wasting physical resources.

This use case focuses on an advanced solution for Proactive Public Infrastructure Asset Management. It integrates data from multiple sources, including security cameras, drones, municipal vehicles, and autonomous ground vehicles (AGVs), and applies Artificial Intelligence (AI) and Deep Learning to enable real-time assessment of the condition of critical infrastructures such as buildings, pavements, and road networks. The system supports early detection of damages, predictive maintenance, and continuous monitoring of public assets, ultimately improving safety, reducing operational costs, and increasing the effectiveness of interventions. AI algorithms automatically identify faults, estimate their severity, and generate alerts or predictions for potential failures. A complementary Digital Twin environment visualizes the infrastructure status, offering authorities an intuitive and up-to-date representation of current conditions. Through the wi.move platform, users can access dashboards, live detections, historical insights, and decision-support tools to better plan maintenance actions. The solution is supported by Beyond 5G (B5G) network infrastructure, ensuring high-speed, low-latency, and reliable communication for real-time data exchange and remote operation. Overall, the use case demonstrates how AI, robotics, and next-generation connectivity can transform infrastructure monitoring into a data-driven, efficient, and scalable process.