TY - JOUR A1 - Ukrainczyk, Neven A1 - Bernard, Thomas A1 - Babaahmadi, Arezou A1 - Huang, Liming A1 - Zausinger, Christoph A1 - Soive, Anthony A1 - Bonnet, Stéphanie A1 - Georget, Fabien A1 - Mrak, Maruša A1 - Dolenec, Sabina A1 - Völker, Tobias A1 - Suraneni, Prannoy A1 - Wilson, William T1 - Test methods for chloride diffusivity of blended cement pastes: a review by RILEM TC 298-EBD N2 - The use of supplementary cementitious materials (SCM) is an important part of the roadmap for reducing CO2 emissions and extending the service life of reinforced concrete structures. To accelerate the adoption of SCMs, the RILEM Technical Committee 298-EBD evaluates scaled-down cement paste test methods to assess the effect of SCM on resistance to chloride and sulfate ingress and reactivity, which are critical to concrete durability. This review focuses on methods for measuring chloride diffusivity and is divided into four sections: diffusivity models and parameters, diffusion test methods (including NMR and chloride measurements), migration test methods and implications for future research. Key insights highlight the complexities of multi-species ionic and molecular diffusion/migration, including various binding interactions, and compares the different measurement methodologies. The review also addresses the test scale and aggregate effects, noting the pros and cons of testing at the paste, mortar, and concrete scales. The review underscores the need for further investigation into testing protocols and the influence of SCM on chloride diffusion, emphasizing that comprehensive testing across different scales provides complementary information for assessing durability performance. KW - Chloride ingress KW - Diffusion tests KW - Migration test KW - Cement paste KW - Concrete KW - Supplementary cementitious materials (SCM) PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-645889 DO - https://doi.org/10.1617/s11527-025-02809-4 SN - 1359-5997 VL - 58 IS - 10 SP - 1 EP - 35 PB - Springer Science and Business Media LLC AN - OPUS4-64588 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Völker, Christoph T1 - WEBSLAMD N2 - The objective of SLAMD is to accelerate materials research in the wet lab through AI. Currently, the focus is on sustainable concrete and binder formulations, but it can be extended to other material classes in the future. 1. Summary Leverage the Digital Lab and AI optimization to discover exciting new materials Represent resources and processes and their socio-economic impact. Calculate complex compositions and enrich them with detailed material knowledge. Integrate laboratory data and apply it to novel formulations. Tailor materials to the purpose to achieve the best solution. Workflow Digital Lab Specify resources: From base materials to manufacturing processes – "Base" enables a detailed and consistent description of existing resources Combine resources: The combination of base materials and processes offers an almost infinite optimization potential. "Blend" makes it easier to design complex configurations. Digital Formulations: With "Formulations" you can effortlessly convert your resources into the entire spectrum of possible concrete formulations. This automatically generates a detailed set of data for AI optimization. AI-Optimization Materials Discovery: Integrate data from the "Digital Lab" or upload your own material data. Enrich the data with lab results and adopt the knowledge to new recipes via artificial intelligence. Leverage socio-economic metrics to identify recipes tailored to your requirements. KW - Materials informatics KW - Scientific software KW - Sequential learning PY - 2022 UR - https://github.com/BAMresearch/WEBSLAMD DO - https://doi.org/10.26272/opus4-56640 PB - GitHub CY - San Francisco, CA, USA AN - OPUS4-56640 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Kruschwitz, Sabine A1 - Völker, Christoph A1 - Munsch, Sarah Mandy A1 - Klewe, Tim A1 - Zia, Ghezal Ahmad Jan A1 - Schumacher, Katrin A1 - Yared, Kaleb A1 - Schmidt, Wolfram T1 - KI und Robotik im Dienst der Nachhaltigkeit: Beschleunigung innovativer Lösungen im Bausektor N2 - Der Vortrag beschäftigt sich mit der Implementierung fortschrittlicher Technologien in neue Wertschöpfungsketten im Bausektor, insbesondere im Bereich Recycling, zirkuläres Produktdesign und Lebenszustandsanalyse. Im Zentrum stehen Industrie- und Grundlagenforschungsprojekte an der Schnittstelle zwischen Wissenschaft und praktischer Anwendung. T2 - DigiCon 2024 CY - Munich, Germany DA - 21.11.2024 KW - KI KW - Materialdesign KW - Recycling KW - Baumaterial PY - 2024 AN - OPUS4-61800 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Völker, Christoph A1 - Kruschwitz, Sabine A1 - Ebell, Gino T1 - A machine learning‑based data fusion approach for improved corrosion testing N2 - This work presents machine learning-inspired data fusion approaches to improve the non-destructive testing of reinforced concrete. The principal effects that are used for data fusion are shown theoretically. Their effectiveness is tested in case studies carried out on largescale concrete specimens with built-in chloride-induced rebar corrosion. The dataset consists of half-cell potential mapping, Wenner resistivity, microwave moisture and ground penetrating radar measurements. Data fusion is based on the logistic Regression algorithm. It learns an optimal linear decision boundary from multivariate labeled training data, to separate intact and defect areas. The training data are generated in an experiment that simulates the entire life cycle of chloride-exposed concrete building parts. The unique possibility to monitor the deterioration, and targeted corrosion initiation, allows data labeling. The results exhibit an improved sensitivity of the data fusion with logistic regression compared to the best individual method half-cell potential. KW - Corrosion KW - Potential mapping KW - Machine learning PY - 2019 DO - https://doi.org/10.1007/s10712-019-09558-4 SN - 1573-0956 SN - 0169-3298 VL - 41 IS - 3 SP - 531 EP - 548 PB - Springer Nature AN - OPUS4-48799 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Völker, Christoph T1 - Trainingsworkshop Datenanalyse N2 - Wir laden zum Trainingsworkshop Datenanalyse ein. Angetrieben durch die Digitalisierung und die sogenannte Industrie 4.0 steigt die Erwartungshaltung gegenüber der Nutzung von Daten. Die Unsicherheit, Widersprüchlichkeit und Redundanz separat verarbeiteter einzelner Quellen soll durch die synergetische Zusammenführung heterogener Datensätze überwunden werden. Dabei steht der zunehmenden Aufgabenkomplexität eine ebenso zunehmende Verfügbarkeit an Daten und Analyseverfahren gegenüber. Der Workshop vermittelt ein konzeptionelles Verständnis für moderne Datenanalyseverfahren (Machine Learning (ML), Multivariate Statistik) und soll durch ein anschließendes Hands-On Training mit Python (https://www.python.org) einen einfachen Einstieg in die Thematik ermöglichen. Der Kurs richtet sich an den wissenschaftlichen Nachwuchs. T2 - Trainingsworkshop KDA: Schwerpunkt Machine Learning und Python CY - Berlin, Germany DA - 03.06.2019 KW - KDA Training KW - Data Analysis KW - Machine Learning PY - 2019 AN - OPUS4-49860 LA - mul AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Völker, Christoph T1 - Integration von Forschungsdaten im Bereich zerstörungsfreien Prüfung (ZfP) im Bauwesen N2 - Der Vortrag gibt einen Überblick zu Fragestellungen, Herausforderungen und Nutzen von semantischer Datenintegration im Forschungsbereich der zerstörungsfreien Prüfung im Bauwesen. T2 - 1. KDA Kolloquium der BAM CY - Berlin, Germany DA - 29.03.2019 KW - Digitalisierung KW - Ontology KW - ZfP KW - Beton KW - Datenanalyse PY - 2019 AN - OPUS4-50026 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Zia, Ghezal Ahmad Jan A1 - Hanke, Thomas A1 - Skrotzki, Birgit A1 - Völker, Christoph A1 - Bayerlein, Bernd T1 - Enhancing Reproducibility in Precipitate Analysis: A FAIR Approach with Automated Dark-Field Transmission Electron Microscope Image Processing N2 - AbstractHigh-strength aluminum alloys used in aerospace and automotive applications obtain their strength through precipitation hardening. Achieving the desired mechanical properties requires precise control over the nanometer-sized precipitates. However, the microstructure of these alloys changes over time due to aging, leading to a deterioration in strength. Typically, the size, number, and distribution of precipitates for a quantitative assessment of microstructural changes are determined by manual analysis, which is subjective and time-consuming. In our work, we introduce a progressive and automatable approach that enables a more efficient, objective, and reproducible analysis of precipitates. The method involves several sequential steps using an image repository containing dark-field transmission electron microscopy (DF-TEM) images depicting various aging states of an aluminum alloy. During the process, precipitation contours are generated and quantitatively evaluated, and the results are comprehensibly transferred into semantic data structures. The use and deployment of Jupyter Notebooks, along with the beneficial implementation of Semantic Web technologies, significantly enhances the reproducibility and comparability of the findings. This work serves as an exemplar of FAIR image and research data management. KW - Industrial and Manufacturing Engineering KW - General Materials Science KW - Automated image analysis KW - FAIR research data management KW - Reproducibility KW - microstructural changes PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-593905 DO - https://doi.org/10.1007/s40192-023-00331-5 SN - 2193-9772 SP - 1 EP - 15 PB - Springer Science and Business Media LLC CY - Heidelberg AN - OPUS4-59390 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bayerlein, Bernd A1 - Hanke, T. A1 - Muth, Thilo A1 - Riedel, Jens A1 - Schilling, Markus A1 - Schweizer, C. A1 - Skrotzki, Birgit A1 - Todor, A. A1 - Moreno Torres, Benjami A1 - Unger, Jörg F. A1 - Völker, Christoph A1 - Olbricht, Jürgen T1 - A Perspective on Digital Knowledge Representation in Materials Science and Engineering N2 - The amount of data generated worldwide is constantly increasing. These data come from a wide variety of sources and systems, are processed differently, have a multitude of formats, and are stored in an untraceable and unstructured manner, predominantly in natural language in data silos. This problem can be equally applied to the heterogeneous research data from materials science and engineering. In this domain, ways and solutions are increasingly being generated to smartly link material data together with their contextual information in a uniform and well-structured manner on platforms, thus making them discoverable, retrievable, and reusable for research and industry. Ontologies play a key role in this context. They enable the sustainable representation of expert knowledge and the semantically structured filling of databases with computer-processable data triples. In this perspective article, we present the project initiative Materials-open-Laboratory (Mat-o-Lab) that aims to provide a collaborative environment for domain experts to digitize their research results and processes and make them fit for data-driven materials research and development. The overarching challenge is to generate connection points to further link data from other domains to harness the promised potential of big materials data and harvest new knowledge. KW - Data infrastructures KW - Digital representations KW - Digital workflows KW - Knowledge graphs KW - Materials informatics KW - Ontologies KW - Vocabulary providers PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-546729 DO - https://doi.org/10.1002/adem.202101176 SN - 1438-1656 SP - 1 EP - 14 PB - Wiley-VCH GmbH CY - Weinheim AN - OPUS4-54672 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Völker, Christoph A1 - Shokouhi, P. T1 - Data aggregation for improved honeycomb detection in concrete using machine learning-based algorithms N2 - We present the results of several machine learning (ML)- inspired data fusion algorithms applied to multi-sensory nondestructive testing (NDT) data. Our dataset consists of Impact-Echo (IE), Ultrasonic Pulse Echo (US) and Ground Penetrating Radar (GPR) data collected on large-scale concrete specimens with built–in simulated honeycombing defects. The main objective is to improve the detectability of honeycombs by fusing the information from the three different sensors. We describe normalization, feature detection and optimal feature selection. We have used unsupervised and supervised ML, i.e., classification and clustering, for data fusion. We demonstrate 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 methods were evaluated on a concrete specimen. The effectiveness of the proposed approach was demonstrated on a separate full-scale concrete specimen. The results indicate the transportability of the conclusions from one specimen to the other. T2 - NDT-CE 2015 - International symposium non-destructive testing in civil engineering CY - Berlin, Germany DA - 15.09.2015 KW - Data fusion KW - Concrete evaluation KW - Honeycombing KW - Machine learning KW - Clustering PY - 2015 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-350968 UR - https://www.ndt.net/?id=18364 SN - 1435-4934 VL - 20 IS - 11 SP - 1 EP - 8 PB - NDT.net CY - Kirchwald AN - OPUS4-35096 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Krause, Martin A1 - Mauke, R. A1 - Effner, Ute A1 - Milmann, Boris A1 - Völker, Christoph A1 - Wiggenhauser, Herbert T1 - Ultrasonic testing of a sealing construction made of salt concrete in an underground disposal facility for radioactive waste N2 - For the closure of radioactive waste disposal facilities engineered barriers- so called “drift seals” are used. The purpose of these barriers is to constrain the possible infiltration of brine and to prevent the migration of radionuclides into the biosphere. In a rock salt mine a large scale in-situ experiment of a sealing construction made of salt concrete was set up to prove the technical feasibility and operability of such barriers. In order to investigate the integrity of this structure, non-destructive ultrasonic measurements were carried out. Therefore two different methods were applied at the front side of the test-barrier: 1 Reflection measurements from boreholes 2 Ultrasonic imaging by means of scanning ultrasonic echo methods This extended abstract is a short version of an article to be published in a special edition of ASCE Journal that will briefly describe the sealing construction, the application of the non-destructive ultrasonic measurement methods and their adaptation to the onsite conditions -as well as parts of the obtained results. From this a concept for the systematic investigation of possible contribution of ultrasonic methods for quality assurance of sealing structures may be deduced. T2 - NDT-CE 2015 - International symposium non-destructive testing in civil engineering CY - Berlin, Germany DA - 2015-09-15 KW - Ultrasonic reflection measurement KW - Dry contact transducers in boreholes KW - Interface salt-concrete / rock salt KW - Ultrasonic imaging of internal reflectors PY - 2015 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-346203 UR - http://www.ndt.net/?id=18304 SN - 1435-4934 VL - 20 IS - 11 SP - 1 EP - 4 PB - NDT.net CY - Kirchwald AN - OPUS4-34620 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 - Recknagel, Christoph A1 - Geburtig, Anja A1 - Wachtendorf, Volker T1 - Die Kunst der Fuge im Glasfassadenbau N2 - Ingenieurbauwerke wie Brücken, hoch beanspruchte Verkehrsflächen oder auch Hochhäuser und deren Bauwerksteile wie z.B. Hochhausfassaden werden aus technischen und ästhetischen Gründen durch Fugen in Einzelabschnitte unterteilt. Zur Sicherstellung der Gebrauchsfähigkeit und zum Schutz des Gesamtbauwerks, aber in zunehmendem Maße auch zur statisch-konstruktiven Anbindung der Bauwerksteile, werden diese Fugenspalte in aller Regel durch spezielle Fugenfüllsysteme verschlossen. Aufgrund der hohen Sicherheitsrelevanz bei Fugenfüllungen im Glasfassadenbau fordert das Baurecht auch bei diesen Bauprodukten als Voraussetzung für die baupraktische Verwendbarkeit neben dem Nachweis der Funktionsfähigkeit auch den Nachweis der Dauerhaftigkeit. Da die hierfür bekannten Nachweismethoden zur Dauerhaftigkeit keine allgemeine Zulassungsakzeptanz finden, kann das ästhetische, bauphysikalische und ökonomische Potential von modernen geklebten Ganzglasfassaden (sogenannte SSG-Fassaden) in der Baupraxis der Bundesrepublik Deutschland derzeit nicht ausgenutzt werden. Grund dafür sind die ungenügend erfassten und in den Bewertungsverfahren simulierten Wechselwirkungen derartiger Baukonstruktionen mit der Umwelt. In diesem Beitrag soll am Beispiel moderner Fugen im Glasfassadenbau (SSG-Fassaden) eine ganzheitliche gebrauchsbezogene Versuchsmethodik zur kontrollierten Ansprache der Funktionsfähigkeit und Dauerhaftigkeit von Fugensystemen unter realitätsnah und reproduzierbar simulierten Umwelteinwirkungen vorgestellt werden. Dazu werden die maßgebenden Umwelteinflüsse und die Quantifizierung der daraus folgenden Beanspruchungen auf derartige Fugensysteme dargestellt. Basierend darauf werden eine repräsentative Beanspruchungsfunktion zur Nachstellung der maßgebenden Umwelteinflüsse und Beanspruchungen auf das System Tragrahmen - Fugenfüllung- Glasscheibe sowie eine geeignete Probendimensionierung abgeleitet. In der Konsequenz werden die Erkenntnisse in den Aufbau einer neuartigen komplexen Versuchseinrichtung überführt. Funktionsprinzip und Leistungsparameter dieser Anlage zur Umweltsimulation werden vorgestellt. Die Möglichkeiten der Systemkennzeichnung werden vorgestellt. Erste Versuchsergebnisse zeigen das Potential der neuartigen Bewertungsmethodik auf, die Kunst der Fuge im Bauwesen gebrauchsorientiert weiter zu entwickeln. T2 - 44. Jahrestagung der GUS 2015 CY - Stutensee-Blankenloch, Germany DA - 25.03.2015 KW - Umweltsimulation KW - Dauerhaftigkeit KW - Funktionsverhalten KW - Neue Untersuchungsmethodik PY - 2015 SN - 978-3-9816286-4-7 SP - 125 EP - 139 AN - OPUS4-33040 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -