AI in cement production: The open-source web app SLAMD uses sequential learning to focus laboratory trials, thereby enabling CO₂ reduction and faster, more cost-effective, climate-friendly cement formulations.
Cement is one of the world’s most important building materials and is also responsible for 8 per cent of global CO2 emissions. If this industry were a country, it would rank among the top five nations in terms of CO2 emissions. To radically reduce climate-damaging emissions, the Federal Institute for Materials Research and Testing is working to accelerate the development of sustainable cement formulations. Thanks to a web application developed by iteratec, which combines software engineering with artificial intelligence methods, research into climate-friendly cement formulations has now been simplified many times over.
Due to the high CO2 emissions associated with cement production, research into new formulations is essential. However, this is highly challenging, as variations in the formulation can have a massive impact on key factors such as the compressive strength of the concrete. Furthermore, laboratory tests are very time-consuming. AI methods were used to analyse existing data so that only the most promising formulations, possessing all the necessary material properties, would be tested in the laboratory. For the further development of a prototype created by BAM, iteratec’s technical expertise was called upon, because:
The software, developed in collaboration with iteratec, represents a technical innovation and holds great potential for the cement industry, which aims to become climate-neutral in Germany by 2050.
The existing prototype was further developed and validated by iteratec using state-of-the-art AI methods. As traditional machine learning approaches are not suitable due to the large number of parameters involved in formula development – and the limited data set – sequential learning came into play here. The advantage is that it is possible to work with a very small amount of data, which is gradually expanded through new laboratory tests. In this way, the algorithm is enriched with more data. Step by step, the quality of the predictions can thus be optimised through the interplay between machine learning on the one hand and laboratory experiments on the other.
To make this solution available to laboratory staff for their work, the AI core was integrated into a completely newly developed and openly accessible web application called SLAMD. This application includes a wide range of technically complex functions to test and visualise formulations based on different material compositions, ensuring optimal compressive strength and low CO2 emissions.
The result: Formulation research is significantly more efficient. Laboratory staff can develop alternative, climate-friendly cements more quickly and therefore more cost-effectively.
A free, licence-free open-source tool for research and industry
A first step towards an autonomous, fully automated laboratory
Universal applicability of the app for research into other building materials
As part of our project at the interface between applied AI research and materials science, our collaboration with iteratec has taken our research to a new level. The highly qualified team familiarised themselves remarkably quickly with the complex scientific interrelationships and translated these into a practical application in a targeted manner. This enabled us to develop a bespoke AI-supported software architecture right from the start, which has exceeded our expectations in every respect. The jointly developed solution has the potential to fundamentally transform research into sustainable building materials.
Federal Institute for Materials Research and Testing
The BAM is a scientific and technical federal authority operating under the remit of the Federal Ministry for Economic Affairs and Climate Action. It conducts testing, research and provides advice on the protection of people, the environment and property.
