Open-Campus-Week 2025

25. – 28. August 2025

Together with its European research partners, the Center Construction Robotics (CCR) in the Construction Cluster on RWTH Aachen Campus is organising from 25 – 28 August 2025 the Open-Campus-Week on Europe’s first digital Reference Construction Site as part of the 5G Industry Campus Europe.

During these days, the CCR will present research highlights from the collaboration between the interdisciplinary science team at RWTH Aachen University, industry partners and students.

Participation for research partners is free of charge.

Price overview:
Contribution fee for external participants – EUR 45
Contribution fee for external participants with project presentation – EUR 1,000

On Wednesday, 27. August 2025, the Center Construction Robotics will be offering exciting and varied workshops.

Detailed information on the individual workshops and registration can be found below.

Join us and register to expand your knowledge in machine learning, robotics or in the interactive and automated point cloud processing and get in touch with RWTH researchers.

Note that some courses are offered in English. Other courses are offered in German. More information can be found on the German page.

AI-Querying for Knowlegdegraphs in AEC

The practice-oriented workshop teaches and demonstrates how Large Language Models can be used to retrieve information from the semantic web in the construction value chain. In the workshop, you will learn the basics of knowledge modelling with the help of ontologies and the practical use of graph databases as the single source of truth.

OCW_LD_workshop_2025_v2-1170x555 Open-Campus-Week 2025

IOT for Construction Automation

In the workshop you will learn the basics of publisher-subscriber based communication protocol as well as JSON payload structure to be able to interface control of the manufacturing IoT devices and visualize incoming state of them through varying dashboard tools.

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Machine learning for construction machines

In this workshop, the basics of machine learning will be discussed and explained using various examples. The examples range from simple regression models to complex neural networks approaches for the realization and implementation of filters, controllers, etc.