Eucentre is a non-profit private law foundation whose mission is to conduct research and provide training and services in earthquake and safety engineering 
Eucentre promotes science, research and innovation for the benefit of the community, offering targeted methodologies and concrete solutions for prevention, safety and resilience. It collaborates with institutions and companies to disseminate competencies for the common good.
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Eucentre carries out research activities in earthquake engineering and risk reduction through laboratory testing and numerical analysis, aiming to improve the seismic performance of structures and soils and to develop innovative seismic retrofitting techniques.
The Foundation promotes diverse and high-quality training activities aimed at academic and professional contexts, with constantly updated and innovative programs and initiatives designed to meet the evolving needs of the sector and society 
Eucentre ensures communication aimed at informing institutions, professionals, and citizens about ongoing activities and projects, with the goal of disseminating useful and accessible content and knowledge. It contributes to promoting a shared and informed culture of prevention and resilience.
The U-AInSPECT project at Eucentre has won the ICAERUS PULL 2 Open Call.
Fondazione Eucentre was selected as one of the winners of the PULL 2 Open Call of the ICAERUS project for its U-AInSPECT project. This achievement confirms our commitment to developing innovative technological solutions for infrastructure safety and earthquake engineering more generally.
The ICAERUS PULL 2 Open Call Pitch Event, which officially marked the start of the U-AInSPECT project, was held online on 3 February 2025. The project will run for 12 months and aims to develop an advanced drone- and AI-based system for infrastructure inspection and monitoring, focusing on its application and impact in rural areas.
What is U-AInSPECT?
The U-AInSPECT project aims to innovate and improve the inspection and monitoring of infrastructure, particularly in rural areas where the low redundancy of transport networks makes ensuring bridge safety essential.
Project objectives
The main objective of U-AInSPECT is to develop an innovative, drone-based artificial intelligence service for bridge inspection. This will optimise damage assessment for both routine inspections and emergency response scenarios (e.g. after an earthquake). This will be achieved by:
- defining operational guidelines for inspecting and surveying bridges using drones to optimise data collection and promote repeatability of operations, while considering the implications of national and European UAS regulations;
- the use of advanced artificial intelligence algorithms will enable semi-automated detection of different classes of damage;
- the development of a “service value chain”, starting with the inspection of the structure and ending with the estimation of the bridge attention class, will support the effective planning of intervention priorities.
Challenges and proposed solutions
Challenge 1: Improving the safety and efficiency of bridge inspections in rural areas.
Traditional inspection methods can be dangerous, time-consuming and inefficient, particularly in rural areas where accessibility is limited. Consequently, some bridges may not undergo systematic inspection, which increases the risk of undetected damage. These issues become even more critical in post-disaster contexts, where timely intervention is essential.
- Solution:
The U-AInSPECT system uses drones and AI algorithms to enable faster, safer inspections and reduce risks for operators while improving priority planning.
Challenge 2: Strengthening connectivity to improve the economic and social resilience of rural areas.
Transport infrastructure is vital for the local economy and the daily mobility of people and goods in rural areas. However, the low redundancy of the road network makes bridges vulnerable to damage or disruption, which can have significant consequences for local communities.
- Solution:
U-AInSPECT helps maintain bridges in optimal condition by encouraging timely and accurate inspections. This ensures more efficient mobility and promotes local economic development and quality of life in rural communities.
Challenge 3: Ensure consistent and reliable data collection.
Variable boundary conditions that affect bridge inspection activities can impact the quality of the collected data.
- Solution:
U-AInSPECT provides operational guidelines for data collection using UAS, thereby optimising the efficiency and repeatability of survey operations.
Technological impact:
The U-AInSPECT project uses advanced technology to make infrastructure inspections more effective:
- Commercial drones (multi-rotors) equipped with RGB cameras to collect high-resolution images and videos;
- AI, deep learning and computer vision algorithms for the semi-automated analysis of the collected data;
- A Web-GIS platform is used to compile forms that estimate the attention class of bridges, which supports the effective management of intervention priorities.
Expected Results
Integrated inspection and pre-assessment service: U-AInSPECT is developing an innovative service to optimise bridge inspections. This will help improve connectivity and road safety in rural areas by using drones and artificial intelligence. This service will also contribute to reducing emissions and fuel consumption by reducing the impact of the use of traditional vehicles, thus promoting more sustainable infrastructure management.
Knowledge sharing: Disseminating material such as guidelines, datasets and market reports will facilitate knowledge transfer and provide stakeholders with useful information for improving rural infrastructure.
Demonstration: The potential of the developed methodology will be demonstrated through its application in pilot use cases.
U-AInSPECT is funded under the ICAERUS project (grant agreement no. 101060643) through its Open Call PULL 2 and is supported by the European Union’s Horizon Europe programme.
For more information on ICAERUS: https://icaerus.eu
Progetto U-AInspect
