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Technical University of Munich

AICC

Artificial Intelligence for smart design and testing of cement and concrete

Principal Investigators 

Prof. Dr. rer. nat. Daniel Cremers, 
Chair of Computer Vision and Artificial Intelligence, Departments of Informatics and Mathematics

Prof. Dr.-Ing. Prof. h.c. Christoph Gehlen, 
Chair of Materials Science and Testing, Department of Materials Engineering

Prof. Dr. Alisa Machner, 
Professorship for Mineral Construction Materials, Department of Materials Engineering

Project contributors

Viktor Kostic, M.Eng., 
Chair of Materials Science and Testing, Department of Materials Engineering

Schnürer, Luis, M.Sc., 
Professorship for Mineral Construction Materials, Department of Materials Engineering

Motivation  and Goals 

The aim of the AICC (Artificial Intelligence for the Smart Design and Testing of Cement and Concrete) project is to improve existing and develop new testing methods in materials science to ensure the long-term quality and durability of concrete structures by adapting artificial intelligence techniques. This includes the efficient characterization of the microstructure of concretes and mortars with regard to their freeze-thaw resistance by analyzing the proportion of air voids and their size distribution. In addition, the degree of reaction of cement additives (SCMs) in cementitious systems is investigated depending on their chemical composition in order to evaluate their reactivity.

Recent Results 

  • Increased reactivity for selected SCMs with increasing Al content
  • Detection of Al enriched areas in EDX maps
  • Development of an efficient labeling technique for air-voids and aggregates
  • Deployment of first segmentation models using deep learning and confocal microscopy
  • Extension of models to different magnification levels using ensembling techniques
  • Synthesized additional concrete images using Generative AI

 

Selected documentation

Submitted journal paperV Kostic, Q Khan, D Cremers, C Gehlen, J Timothy and T Kränkel, 2024: Efficient labelling of air-voids and aggregates in concrete and mortar using Confocal Laser Scanning Microscopy combined with Meta AI's Segment Anything Model. (Submission to Advanced Engineering Informatics planned in late 2024: Special Issue AI for the Built World)
Conference papers

Schnürer, Luis; Machner, Alisa (2022): Synthesis and Reactivity Testing of Artificial Supplementary Cementitious Materials. 4th International Conference on the Chemistry of Construction Materials, ICCCM 26.-28.09.2022. Karlsruhe, Germany (poster contribution)

L Schnürer, A Machner, 2023: Effects of the Chemical Composition of Synthetic Slags Compared to an Average Blast Furnace Slag. In: Proceedings in Civil Engineering, Volume 6, Issue 6. https://doi.org/10.1002/cepa.2933.

Q Khan, M Hassan, V Kostic, V Golkov, C Gehlen and D Cremers, 2024: Improving the Detection of Air-Voids and Aggregates in Images of Concrete Using Generative AI. In GNI Symposium on AI for the Built World, 2024

V Kostic, Q Khan, D Cremers, C Gehlen, J Timothy and T Kränkel, 2024:  Efficient labelling of air-voids and aggregates in concrete and mortar using Confocal Laser Scanning Microscopy combined with Meta AI's Segment Anything Model. In GNI Symposium on AI for the Built World, 2024

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