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 - JOUR A1 - Kruschwitz, Sabine A1 - Oesch, T. A1 - Mielentz, Frank A1 - Meinel, Dietmar A1 - Spyridis, P. T1 - Non-Destructive Multi-Method Assessment of Steel Fiber Orientation in Concrete N2 - Integration of fiber reinforcement in high-performance cementitious materials has become widely applied in many fields of construction. One of the most investigated advantages of steel Fiber reinforced concrete (SFRC) is the deceleration of crack growth and hence its improved sustainability. Additional benefits are associated with its structural properties, as fibers can significantly increase the ductility and the tensile strength of concrete. In some applications it is even possible to entirely replace the conventional reinforcement, leading to significant logistical and environmental benefits. Fiber reinforcement can, however, have critical disadvantages and even hinder the Performance of concrete, since it can induce an anisotropic material behavior of the mixture if the fibers are not appropriately oriented. For a safe use of SFRC in the future, reliable non-destructive testing (NDT) methods need to be identified to assess the fibers’ orientation in hardened concrete. In this study, ultrasonic material testing, electrical impedance testing, and X-ray computed tomography have been investigated for this purpose using specially produced samples with biased or random Fiber orientations. We demonstrate the capabilities of each of these NDT techniques for fiber orientation measurements and draw conclusions based on these results about the most promising areas for future research and development. KW - Spectral induced polarization KW - Steel fiber reiniforced concrete KW - Fiber orientation KW - Non-destructive testing KW - Micro-computed tomography KW - Ultrasound PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-543520 DO - https://doi.org/10.3390/app12020697 VL - 12 IS - 2 SP - 1 EP - 14 PB - MDPI CY - Basel Switzerland AN - OPUS4-54352 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Jablonka, Kevin Maik A1 - Ai, Qianxiang A1 - Al-Feghali, Alexander A1 - Badhwar, Shruti A1 - Bocarsly, Joshua D. A1 - Bran, Andres M. A1 - Bringuier, Stefan A1 - Brinson, L. Catherine A1 - Choudhary, Kamal A1 - Circi, Defne A1 - Cox, Sam A1 - de Jong, Wibe A. A1 - Evans, Matthew L. A1 - Gastellu, Nicolas A1 - Genzling, Jerome A1 - Gil, María Victoria A1 - Gupta, Ankur K. A1 - Hong, Zhi A1 - Imran, Alishba A1 - Kruschwitz, Sabine A1 - Labarre, Anne A1 - Lála, Jakub A1 - Liu, Tao A1 - Ma, Steven A1 - Majumdar, Sauradeep A1 - Merz, Garrett W. A1 - Moitessier, Nicolas A1 - Moubarak, Elias A1 - Mouriño, Beatriz A1 - Pelkie, Brenden A1 - Pieler, Michael A1 - Ramos, Mayk Caldas A1 - Ranković, Bojana A1 - Rodriques, Samuel G. A1 - Sanders, Jacob N. A1 - Schwaller, Philippe A1 - Schwarting, Marcus A1 - Shi, Jiale A1 - Smit, Berend A1 - Smith, Ben E. A1 - Van Herck, Joren A1 - Völker, Christoph A1 - Ward, Logan A1 - Warren, Sean A1 - Weiser, Benjamin A1 - Zhang, Sylvester A1 - Zhang, Xiaoqi A1 - Zia, Ghezal Ahmad Jan A1 - Scourtas, Aristana A1 - Schmidt, K. J. A1 - Foster, Ian A1 - White, Andrew D. A1 - Blaiszik, Ben T1 - 14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon N2 - Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines. KW - Large Language model KW - Hackathon KW - Concrete KW - Prediction KW - Inverse Design KW - Orchestration PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-589961 DO - https://doi.org/10.1039/d3dd00113j VL - 2 IS - 5 SP - 1233 EP - 1250 PB - Royal Society of Chemistry (RSC) AN - OPUS4-58996 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 - Klewe, Tim A1 - Völker, Tobias A1 - Landmann, Mirko A1 - Kruschwitz, Sabine T1 - LIBS‐ConSort: Development of a sensor‐based sorting method for construction and demolition waste N2 - AbstractA joint project of partners from industry and research institutions approaches the challenge of construction and demolition waste (CDW) sorting by investigating and testing the combination of laser‐induced breakdown spectroscopy (LIBS) with near‐infrared (NIR) spectroscopy and visual imaging. Joint processing of information (data fusion) is expected to significantly improve the sorting quality of various materials like concrete, main masonry building materials, organic components, etc., and may enable the detection and separation of impurities such as SO3‐cotaining building materials (gypsum, aerated concrete, etc.)Focusing on Berlin as an example, the entire value chain will be analyzed to minimize economic / technological barriers and obstacles at the cluster level and to sustainably increase recovery and recycling rates.The objective of this paper is to present current progress and results of the test stand development combining LIBS with NIR spectroscopy and visual imaging. In the future, this laboratory prototype will serve as a fully automated measurement setup to allow real‐time classification of CDW on a conveyor belt. T2 - 21st Ibausil - International Conference on Building Materials CY - Weimar, Germany DA - 13.09.2023 KW - NDT KW - Material cassification KW - Recycling KW - LIBS KW - Data fusion KW - Construction and demolition waste KW - Circular economy PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-590734 DO - https://doi.org/10.1002/cepa.2866 SN - 2509-7075 VL - 6 IS - 6 SP - 973 EP - 976 PB - Ernst & Sohn GmbH AN - OPUS4-59073 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 - TY - JOUR A1 - Mellios, Nikolaos A1 - Kruschwitz, Sabine A1 - Spyrdis, Panagiotis T1 - Performance Prediction of Anchors in SFRC using Minimally Invasive and Non‐Destructive Techniques N2 - AbstractFastenings are crucial for the upgrade of existing structures, by either enhancing structural components or modernising and expanding structures and structural systems. Whilst concrete is prevailing in the existing building stock, new types of concrete composites are increasingly used to improve the sustainability and overall structural performance of built assets. The addition of short dispersed fibres is a material technology with several applications. This paper addresses the synergy of fastening elements, introducing localised loads in concrete substrates, and of fibres, particularly in terms of the fibre orientation and density in the load application area, which is a significant quality aspect. The possibility to forecast this synergy based on non‐destructive or minimally invasive testing methods is an efficient tool to increase confidence in the design load‐bearing performance of the anchorages. Initially, previous literature and own investigations on such techniques are presented. Furthermore, a methodology is presented on associating such information to predict a single anchor's resistance on the basis of sophisticated non‐linear analyses and testing with engineered fibres alignments. T2 - Annual Meeting of the European-Association-on-Quality-Control-of-Bridges-and-Structures (EUROSTRUCT) CY - Vienna, Austria DA - 25.09.2023 KW - Minimally-invasive evaluation KW - Fastenings KW - Anchorage to concrete KW - Steel fibre reinforced concrete KW - Non-destructive evaluation NDE KW - Non-destructive testing NDT PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-609223 DO - https://doi.org/10.1002/cepa.2115 VL - 69 IS - 5 SP - 1455 EP - 1463 PB - Wiley AN - OPUS4-60922 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Costabel, S. A1 - Hiller, Thomas A1 - Dlugosch, R. A1 - Kruschwitz, Sabine A1 - Müller Petke, M. T1 - Evaluation of single-sided nuclear magnetic resonance technology for usage in geosciences N2 - Because of its mobility and ability to investigate exposed surfaces, single-sided (SiS) nuclear magnetic resonance (NMR) technology enables new application fields in geosciences. To test and assess its corresponding potential, we compare longitudinal (T1) and transverse (T2) data measured by SiS NMR with those of conventional geoscientific laboratory NMR. We use reference sandstone samples covering a broad range of pore sizes. Our study demonstrates that the lower signal-to-noise ratio of SiS NMR data generally tends to slightly overestimated widths of relaxation time distributions and consequently pore size distributions. While SiS and conventional NMR produce very similar T1 relaxation data, unbiased SiS NMR results for T2 measurements can only be expected for fine material, i.e. clayey or silty sediments and soils with main relaxation times below 0.05s. This limit is given by the diffusion relaxation rate due to the gradient in the primary magnetic field associated with the SiS NMR. Above that limit, i.e. for coarse material, the relaxation data is strongly attenuated. If considering the diffusion relaxation time of 0.2 s in the numerical data inversion process, the information content >0.2s is blurred over a range larger than that of conventional NMR. However, our results show that principle range and magnitudes of the relaxation time distributions are reconstructed to some extent. Regarding these findings, SiS NMR can be helpful to solve geoscientific issues, e.g. to assess the hydro-mechanical properties of the walls of underground facilities or to provide local soil moisture data sets for calibrating indirect remote techniques on the regional scale. The greatest opportunity provided by the SiS NMR technology is the acquisition of profile relaxation data for rocks with significant bedding structures at the µm scale. With this unique feature, SiS NMR can support the understanding and modeling of hydraulic and diffusional anisotropy behavior of sedimentary rocks. KW - Single-sided NMR KW - Geosciences KW - Nuclear magnetic resonance PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-561676 DO - https://doi.org/10.1088/1361-6501/ac9800 SN - 0957-0233 VL - 34 IS - 1 SP - 1 EP - 13 PB - IOP Publishing AN - OPUS4-56167 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Völker, Tobias A1 - Mensing, Friedrich A1 - Kruschwitz, Sabine T1 - Estimation of cement content in concrete by spatially resolved laser induced breakdown spectroscopy N2 - The cement content in concrete significantly influences critical properties such as durability, permeability, strength, and workability. Traditional methods for estimating the cement content face limitations. These include the need for comprehensive chemical and solubility knowledge, extensive sample preparation, and their time-consuming and destructive nature. This study investigates the application of laser-induced breakdown spectroscopy (LIBS) as an alternative method. It involves probing concrete samples with high spatial resolution and analyzing the resultant spectra. The methodology is first tested on mesoscale concrete models to assess limitations and inherent errors. Subsequently, the methodology is applied to actual concrete samples with varying cement content and aggregate size distributions. The results demonstrate a promising accuracy, with an average relative error of approximately 8%. This paper offers a comprehensive evaluation of the method's advantages, limitations, and factors influencing its practical applicability in field conditions. KW - LIBS KW - Spectroscopy KW - Cement content KW - Elemental mapping PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-620112 DO - https://doi.org/10.1016/j.cemconres.2024.107714 SN - 1873-3948 VL - 189 SP - 1 EP - 10 PB - Elsevier Ltd. AN - OPUS4-62011 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - McNeil, Sue A1 - Adey, Bryan A1 - Anastasopoulos, Panagiotis Ch. A1 - Chu, James C. A1 - Derrible, Sybil A1 - Durango-Cohen, Pablo A1 - Francis, Royce A1 - Kruschwitz, Sabine A1 - Labi, Samuel A1 - Li, Joshua A1 - Manuel, Lance A1 - Cunha Marques, Rui A1 - Reilly, Allison A1 - Tesfamariam, Solomon A1 - Sanford, Kristen L. T1 - Journal of Infrastructure Systems: Thirty Years and an Opportunity to Refocus N2 - The editorial reflects on the 30-year journey of the Journal of Infrastructure Systems, highlighting its evolution, interdisciplinary mission, and contributions to the field of civil infrastructure. It traces the journal's history, including the introduction of new paper formats, awards, and its commitment to engaging early-career professionals. Looking forward, the journal emphasizes its focus on complex, system-level challenges, encouraging submissions that integrate engineering with data-driven, cross-disciplinary approaches, particularly in areas like smart infrastructure, climate resilience, and sustainability. The editorial concludes with gratitude to contributors and a call for continued collaboration to advance the understanding and management of infrastructure systems. KW - Maintenance KW - Infrastructure systen KW - Transportation KW - Repair KW - Climate resilience KW - Refurbishment PY - 2025 DO - https://doi.org/10.1061/JITSE4.ISENG-2690 VL - 31 IS - 1 SP - 1 EP - 2 PB - American Society of Civil Engineers (ASCE) AN - OPUS4-62773 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -