TY - CONF A1 - Munsch, Sarah Mandy A1 - Telong, Melissa A1 - Grobla, Lili A1 - Schumacher, Katrin A1 - Völker, Christoph A1 - Yared, Kaleb A1 - Kruschwitz, Sabine ED - Ferrara, Liberato ED - Muciaccia, Giovanni ED - di Summa, Davide T1 - Study on the Predictability of Carbonation Resistance of Cementitous Materials Based on NMR Features and the Use of SLAMD N2 - This study explores the acceleration of material design in the concrete industry, focusing on improving carbonation resistance, a key factor in the durability of concrete structures. Traditional tests for carbonation resistance are lengthy, but with the construction industry aiming for sustainable production, finding a balance between carbonation resistance and CO2 footprint is crucial. Our research employs two innovative methods: 1. Applying the Sequential Learning App for Materials Discovery (SLAMD), an AI materials design framework, to an extensive dataset of real-world concrete compositions to selectively test materials that meet market demands: maximum durability, optimal eco-durability, and the best cost-durability trade-off. 2. Investigating 1H Nuclear Magnetic Resonance (NMR) relaxometry as a quick alternative for characterizing carbonation behavior, as it saves time compared to traditional tests and assesses the complete material’s pore space. Specific NMR features are then integrated into the material design model, with the model’s performance compared against traditional approaches. The results of our study are compelling, demonstrating that materials can be precisely tailored to meet specific requirements with minimal data points. This marks a significant stride in the concrete industry, indicating thatNMR-based, lowfidelity surrogate characterizations, combined with a focused, data-driven design approach, can substantially accelerate the development of durable, sustainable concrete mixtures. T2 - 4 RILEM Spring Convention and Conference on advanced construction materials and processes for a carbon neutral society 2024 CY - Milano, Italy DA - 07.04.2024 KW - Nuclear magnetic resonance KW - carbonation resistance KW - cement and concrete KW - SLAMD app KW - predictability PY - 2024 DO - https://doi.org/10.1007/978-3-031-70281-5_49 VL - 2 SP - 435 EP - 442 PB - Springer CY - Cham, Switzerland AN - OPUS4-61692 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Völker, Christoph A1 - Firdous, R. A1 - Kruschwitz, Sabine A1 - Stephan, D. T1 - Sequential learning to accelerate discovery of alkali-activated binders N2 - Alkali-activated binders (AAB) can provide a clean alternative to conventional cement in terms of CO2 emissions. However, as yet there are no sufficiently accurate material models to effectively predict the AAB properties, thus making optimal mix design highly costly and reducing the attractiveness of such binders. This work adopts sequential learning (SL) in high-dimensional material spaces (consisting of composition and processing data) to find AABs that exhibit desired properties. The SL approach combines machine learning models and feedback from real experiments. For this purpose, 131 data points were collected from different publications. The data sources are described in detail, and the differences between the binders are discussed. The sought-after target property is the compressive strength of the binders after 28 days. The success is benchmarked in terms of the number of experiments required to find materials with the desired strength. The influence of some constraints was systematically analyzed, e.g., the possibility to parallelize the experiments, the influence of the chosen algorithm and the size of the training data set. The results show the advantage of SL, i.e., the amount of data required can potentially be reduced by at least one order of magnitude compared to traditional machine learning models, while at the same time exploiting highly complex information. This brings applications in laboratory practice within reach. KW - Alkali-activated binders KW - Machine learning KW - Sequential learning KW - Materials by design KW - Materials informatics PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-531376 DO - https://doi.org/10.1007/s10853-021-06324-z SN - 0022-2461 SN - 1573-4803 VL - 56 SP - 15859 EP - 15881 PB - Springer CY - Dordrecht AN - OPUS4-53137 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Kruschwitz, Sabine A1 - Völker, Christoph A1 - Zia, Ghezal Ahmad Jan A1 - Moreno Torres, Benjami A1 - Hartmann, Timo T1 - Reincarnate: Shaping a sustainable future in construction through digital innovation N2 - We introduce the REINCARNATE project, funded by the European Union's Horizon Europe program, to boost circularity by merging digital innovations with practical applications and a focus on material reuse. The heart of REINCARNATE is the Circular Potential Information Model (CP-IM), a digital platform designed to assess and enhance the recyclability of construction materials, construction products, and buildings. The CP-IM integrates advanced technologies such as digital twins, AI, and robotics to revolutionize the handling of construction waste, turning it into valuable resources and cutting the environmental footprint of the sector. Among its features are digital tracing, material durability predictions, and CO2 reduction materials design. These are showcased in eleven European demonstration projects, highlighting the practical benefits of these technologies in reducing construction waste and CO2 emissions by up to 80% and 70% respectively. REINCARNATE aims to marry innovation with real-world application, providing the construction industry with strategies for sustainable and circular practices. T2 - Rilem Spring Convention CY - Milan, Italy DA - 10.04.2024 KW - Construction sustainability KW - European project KW - Recycled materials KW - Life cycle KW - Digital construction PY - 2024 AN - OPUS4-60625 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Kruschwitz, Sabine A1 - Völker, Christoph A1 - Zia, Ghezal Ahmad Jan A1 - Moreno Torres, Benjami A1 - Hartmann, Timo ED - Ferrara, L. ED - Muciaccia, G. ED - di Summa, D. T1 - REINCARNATE: Shaping a Sustainable Future in Construction Through Digital Innovation N2 - We introduce the REINCARNATE project, funded by the European Union’s Horizon Europe program, to boost circularity by merging digital innovations with practical applications and a focus on material reuse. The heart of REINCARNATE is the Circular Potential Information Model (CP-IM), a digital platform designed to assess and enhance the recyclability of construction materials, construction products, and buildings. The CP-IM integrates advanced technologies such as digital twins, AI, and robotics to revolutionize the handling of construction waste, turning it into valuable resources and cutting the environmental footprint of the sector. Among its features are digital tracing, material durability predictions, and CO2 reduction materials design. These are showcased in eleven European demonstration projects, highlighting the practical benefits of these technologies in reducing construction waste and CO2 emissions by up to 80% and 70% respectively. REINCARNATE aims to marry innovation with real-world application, providing the construction industry with strategies for sustainable and circular practices. T2 - 4 RILEM Spring Convention and Conference on advanced construction materials and processes for a carbon neutral society 2024 CY - Milano, Italy DA - 07.04.2024 KW - Digital construction KW - Construction sustainability KW - European project KW - Llife cycle KW - Recycled materials PY - 2024 SN - 978-3-031-70280-8 SN - 978-3-03170281-5 SN - 978-3-031-70283-9 DO - https://doi.org/10.1007/978-3-031-70281-5_32 SN - 2211-0844 SN - 2211-0852 N1 - Serientitel: RILEM Bookseries – Series title: RILEM Bookseries VL - 56 IS - 2 SP - 283 EP - 291 PB - Springer CY - Cham AN - OPUS4-61661 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Völker, Christoph A1 - Moreno Torres, Benjami A1 - Zia, Ghezal Ahmad Jan A1 - Rug, Tehseen A1 - Firdous, Rafia A1 - Böhmer, Felix A1 - Stephan, Dietmar A1 - Kruschwitz, Sabine T1 - Presenting SLAMD – A Sequential Learning Based Software for the Inverse Design of Sustainable Cementitious Materials N2 - In recent decades, the number of components in concrete has grown, particularly in formulations aimed at reducing carbon footprints. Innovations include diverse binders, supplementary cementitious materials, activators, concrete admixtures, and recycled aggregates. These developments target not only the enhancement of material properties but also the mitigation of the ecological and economic impacts of concrete — the most extensively used material by humankind. However, these advancements also introduce a greater variability in the composition of raw materials. The material’s behavior is significantly influenced by its nanoscale properties, which can pose challenges in accurate characterization. Consequently, there’s an increasing need for experimental tuning of formulations. This is accompanied by a more inconsistent composition of raw materials, which makes an experimental tuning of formulations more and more necessary. However, the increased complexity in composition presents a challenge in finding the ideal formulation through trial and error. Inverse design (ID) techniques offer a solution to this challenge by allowing for a comprehensive search of the entire design space to create new and improved concrete formulations. In this publication, we introduce the concept of ID and demonstrate how our open-source app “SLAMD” provides all necessary steps of the workflow to adapt it in the laboratory, lowering the application barriers. The intelligent screening process, guided by a predictive model, leads to a more efficient and effective data-driven material design process resulting in reduced carbon footprint and improved material quality while considering socio-economic factors in the materials design. KW - Sustainable concrete KW - Machine learning optimization KW - Inverse design techniques KW - Scientific software KW - Data-driven material design PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-589993 DO - https://doi.org/10.17756/nwj.2023-s2-032 VL - 9 SP - 180 EP - 187 AN - OPUS4-58999 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Karafiludis, Stephanos A1 - Standl, Jacob A1 - Ryll, Tom W. A1 - Schwab, Alexander A1 - Prinz, Carsten A1 - Wolf, Jakob B. A1 - Kruschwitz, Sabine A1 - Emmerling, Franziska A1 - Völker, Christoph A1 - Stawski, Tomasz M. T1 - High-Entropy Phosphate Synthesis: Advancements through Automation and Sequential Learning Optimization N2 - Transition metal phosphates (TMPs) are extensively explored for electrochemical and catalytical applications due to their structural versatility and chemical stability. Within this material class, novel high-entropy metal phosphates (HEMPs)─containing multiple transition metals combined into a single-phase structure─are particularly promising, as their compositional complexity can significantly enhance functional properties. However, the discovery of suitable HEMP compositions is hindered by the vast compositional design space and complex or very specific synthesis conditions. Here, we present a data-driven strategy combining automated wet-chemical synthesis with a Sequential Learning App for Materials Discovery (SLAMD) framework (Random Forest regression model) to efficiently explore and optimize HEMP compositions. Using a limited set of initial experiments, we identified multimetal compositions in a single-phase crystalline solid. The model successfully predicted a novel Co0.3Ni0.3Fe0.2Cd0.1Mn0.1 phosphate octahydrate phase, validated experimentally, demonstrating the effectiveness of the machine learning approach. This work highlights the potential of integrating automated synthesis platforms with data-driven algorithms to accelerate the discovery of high-entropy materials, offering an efficient design pathway to advanced functional materials. KW - Metal phosphates KW - High entropy KW - Sequential learning KW - Automated synthesis KW - MAP KW - Random forest KW - Machine learning PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-641554 DO - https://doi.org/10.1021/acs.cgd.5c00549 SN - 1528-7483 VL - 25 IS - 19 SP - 7989 EP - 8001 PB - American Chemical Society (ACS) CY - Washington, DC AN - OPUS4-64155 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Stawski, Tomasz A1 - Karafiludis, Stephanos A1 - Standl, Jakob A1 - Ryll, Tom A1 - Schwab, Alexander A1 - Prinz, Carsten A1 - Wolf, Jakob A1 - Kruschwitz, Sabine A1 - Emmerling, Franziska A1 - Völker, Christoph T1 - High-Entropy Metal Phosphate Synthesis: Advancements through Automation and Sequential Learning Optimization N2 - To accelerate high-entropy metal phosphate (HEMP) discovery, we employed a Random Forest regression model within a SLAMD framework. Trained on limited initial data, the model efficiently explored the vast compositional space to predict a novel five-metal phosphate, which was then successfully synthesized and validated experimentally. T2 - AI4 Materials Science and Testing 2025 CY - Berlin, Germany DA - 06.11.2025 KW - Metal phosphates KW - High-entropy KW - Sequential learning PY - 2025 AN - OPUS4-64686 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Völker, Christoph A1 - Kruschwitz, Sabine A1 - Boller, C. A1 - Wiggenhauser, Herbert T1 - Feasibility study on adapting a machine learning based multi-sensor data fusion approach for honeycomb detection in concrete N2 - We present the results of a machine learning (ML)- inspired data fusion approach, applied to multi-sensory nondestructive testing (NDT) data. Our dataset consists of Impact-Echo (IE), Ultrasonic Pulse Echo (US) and Ground Penetrating Radar (GPR) measurements collected on large-scale concrete specimens with built–in simulated honeycombing defects. In a previous study we were able to improve the detectability of honeycombs by fusing the information from the three different sensors with the density based clustering algorithm DBSCAN. We demonstrated the advantage of data fusion in reducing the false positives up to 10% compared to the best single sensor, thus, improving the detectability of the defects. The main objective of this contribution is to investigate the generality, i.e. whether the conclusions from one specimen can be adapted to the other. The effectiveness of the proposed approach on a separate full-scale concrete specimen was evaluated. T2 - NDE/NDT for Highway and Bridges: Structural Materials Technology 2016 CY - Portland, Oregon, USA DA - 29.08.2016 KW - Data fusion KW - Concrete evaluation KW - Honeycombing KW - Machine learning KW - Clustering PY - 2016 SN - 978-1-57117-392-8 SP - 144 EP - 148 PB - The American Society for Nondestructive Testing, Inc CY - Portland, Oregon, USA AN - OPUS4-38288 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - VIDEO A1 - Kruschwitz, Sabine A1 - Völker, Christoph T1 - EU-Project: Reincarnate Reducing Waste and CO2 Footprint of construction sector N2 - In our current research project „Reincarnate“ we aim to anchor the idea of the circular economy in the European construction industry and significantly extend the life cycle of buildings, construction products and materials through innovative solutions. On the long term, this is an approach reduce construction waste by 80 percent and the CO2 footprint of the construction sector by 70 percent." This project has received funding from the European Union’s Horizon Europe research and innovation programme and will take you on a tour on what are the drivers, what is the goal, who are the partners and how we want to make the world a better place! KW - circular economy KW - CO2 footprint KW - Building industry KW - Information modelling PY - 2022 PB - Bundesanstalt für Materialforschung und -prüfung (BAM) CY - Berlin AN - OPUS4-56646 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Völker, Christoph A1 - Moreno Torres, Benjami A1 - Rug, Tehseen A1 - Firdous, Rafia A1 - Zia, Ghezal Ahmad Jan A1 - Lüders, Stefan A1 - Lisdero Scaffino, Horacio A1 - Höpler, Michael A1 - Böhmer, Felix A1 - Pfaff, Matthias A1 - Stephan, Dietmar A1 - Kruschwitz, Sabine T1 - Data driven design of alkali-activated concrete using sequential learning N2 - This paper presents a novel approach for developing sustainable building materials through Sequential Learning. Data sets with a total of 1367 formulations of different types of alkali-activated building materials, including fly ash and blast furnace slag-based concrete and their respective compressive strength and CO2-footprint, were compiled from the literature to develop and evaluate this approach. Utilizing this data, a comprehensive computational study was undertaken to evaluate the efficacy of the proposed material design methodologies, simulating laboratory conditions reflective of real-world scenarios. The results indicate a significant reduction in development time and lower research costs enabled through predictions with machine learning. This work challenges common practices in data-driven materials development for building materials. Our results show, training data required for data-driven design may be much less than commonly suggested. Further, it is more important to establish a practical design framework than to choose more accurate models. This approach can be immediately implemented into practical applications and can be translated into significant advances in sustainable building materials development. KW - Sustainable building materials KW - Sequential learning KW - Data-driven materials design KW - Alkali-activated building materials PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-584376 UR - https://www.sciencedirect.com/science/article/pii/S095965262302379X DO - https://doi.org/10.1016/j.jclepro.2023.138221 SN - 0959-6526 SN - 1879-1786 VL - 418 SP - 1 EP - 13 PB - Elsevier CY - Amsterdam AN - OPUS4-58437 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -