TY - CONF A1 - Völker, Christoph 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 AN - OPUS4-38289 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Völker, Christoph T1 - Visualization of automated multi sensor NDT assessment of concrete structures N2 - This talk demonstrates the results of the IGSTC-project entitled "NDT-Data Fusion". Project approach: Nondestructive testing (NDT) of concrete buildings allows to coordinate efficient repair measures. Multi-sensor platforms collect large data sets. Nevertheless, data analysis is typically performed manually. Data Fusion uses the full potential of a multi-sensory data set in order to: improve information quality (reliability, robustness, accuracy, clarity, completeness) and enables automated algorithm based data analysis. We present the project achievements, namely: - Development of building scanner system for multisensory NDT - Laboratory multi sensor investigations - Development of data fusion concept for honeycombing and pitting corrosion - Field testing T2 - IGSTC-Partners Meeting 2017 CY - Jodhpur, India DA - 22.10.2017 KW - Machine learning KW - Data fusion KW - NDT KW - Concrete KW - Corrosion KW - Honeycombs PY - 2017 AN - OPUS4-44097 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Völker, Christoph T1 - Data fusion for non-destructive testing N2 - This talk introduces the "mechanisms" behind data fusion and demonstrates how to effectively use them for NDT-data. Common fusion strategies are introduced to explain what is required to enter (publishing-) practice. T2 - NDE2017 Pre-Conference Workshop CY - Chennai, India DA - 12.12.2017 KW - Data fusion KW - NDT KW - Concrete KW - Validation KW - Machine learning PY - 2017 AN - OPUS4-44100 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Völker, Christoph T1 - Visualization of automated multi‐sensor NDT assessment of concrete structures (NDT Data Fusion) N2 - Nondestructive testing(NDT) of concrete buildings allows efficient repair measures. Multi-sensor platforms collect large data sets but the data analysis is typically performed manually. Data Fusion uses the full potential of a multi-sensory data set in order to: - improve information quality (reliability, robustness, accuracy, clarity, completeness) - enable automated algorithm based data analysis. This poster summarizes results of the IGSTC-project entitled "NDT-Data Fusion" (short title) and demonstrates a significant improvement in the testing performance for the example of corrosion detection. T2 - IGSTC-Partners Meeting 2017 CY - Jodhpur, India DA - 22.10.2017 KW - NDT KW - Data Fusion KW - Concrete KW - Corrosion KW - Data analytics PY - 2017 AN - OPUS4-42716 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Völker, Christoph T1 - Datenfusion –Informationsgewinn aus Multisensordaten N2 - Angetrieben durch Digitalisierung und die sogenannte Industrie 4.0 steigt die Erwartungshaltung gegenüber der Nutzung von Sensorik. 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 Sensoren, Daten und Rechenleistung gegenüber. Der Begriff Datenfusion fasst Ansätze zusammen, die Daten zu abstrakteren, aber besser verständlichen Informationen verarbeiten. Die Fusion ist Kernbestandteil effektiver Assistenzsysteme und beweist in vielzähligen Aufgaben - von militärischen Anwendungen über Flug- und Fahrassistenzsysteme, bis in den Heimbereich – ihr großes Potenzial. Durch die wachsende Automatisierung bei der Messdatenerfassung wird Datenfusion auch in der industriellen Qualitätsprüfung und –sicherung zunehmend attraktiver. Der Vortrag gibt einen Überblick über den breiten Themenkomplex und widmet sich dabei im Theorieteil speziell der Fragen, welche Informationen in multivariaten Datensätzen stecken und wie sie extrahiert werden können. Anschließend wird ein Beispiel für die erfolgreiche Anwendung zur zerstörungsfreien Prüfung von Betonbauteilen vorgestellt. Der dargestellte Datensatz ist klein, heterogen, hochdimensional und unausgeglichen. Anhand von Algorithmen mit unterschiedlicher Leistungsfähigkeiten hinsichtlich Anpassungsfähigkeit und Invarianz gegenüber Höherdimensionalität wird erläutert welche Prozesse zur Verbesserung der Informationsqualität nötig sind. T2 - Vortragsreihe des Kompetenzzentrums Datenanalyse CY - BAM UE, Berlin, Germany DA - 25.07.2018 KW - Maschinelles Lernen KW - Datenfusion KW - ZfP KW - Beton KW - Korrosion PY - 2018 AN - OPUS4-45602 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Völker, Christoph T1 - Vortragsreihe des Kompetenzzentrums Datenanalyse Datenfusion –Informationsgewinn aus Multisensordaten N2 - Angetrieben durch Digitalisierung und die sogenannte Industrie 4.0 steigt die Erwartungshaltung gegenüber der Nutzung von Sensorik. 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 Sensoren, Daten und Rechenleistung gegenüber. Der Begriff Datenfusion fasst Ansätze zusammen, die Daten zu abstrakteren, aber besser verständlichen Informationen verarbeiten. Die Fusion ist Kernbestandteil effektiver Assistenzsysteme und beweist in vielzähligen Aufgaben - von militärischen Anwendungen über Flug- und Fahrassistenzsysteme, bis in den Heimbereich – ihr großes Potenzial. Durch die wachsende Automatisierung bei der Messdatenerfassung wird Datenfusion auch in der industriellen Qualitätsprüfung und –sicherung zunehmend attraktiver. Der Vortrag gibt einen Überblick über den breiten Themenkomplex und widmet sich dabei speziell der Fragen, welche Informationen in multivariaten Datensätzen stecken und wie sie extrahiert werden können. Abschließend wird ein Beispiel für die erfolgreiche Anwendung zur zerstörungsfreien Prüfung von Betonbauteilen vorgestellt. Der systematische Vergleich von Algorithmen, die Charakteristika des Labordatensatzes in unterschiedlicher Weise adressieren erlaubt überraschende Schlussfolgerungen. T2 - Vortrag CY - BAM Berlin, Germany DA - 25.07.2018 KW - Datenanalyse PY - 2018 AN - OPUS4-46260 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Völker, Christoph A1 - John, Elisabeth A1 - Firdous, Rafia A1 - Hirsch, Tamino A1 - Kaczmarek, Daria A1 - Ziesack, Kevin A1 - Buchwald, Anja A1 - Stephan, Dietmar A1 - Kruschwitz, Sabine ED - Ferrara, Liberato ED - Muciaccia, Giovanni ED - di Summa, Davide T1 - Beyond Theory: Pioneering AI-Driven Materials Design in the Sustainable Building Material Lab N2 - This work focuses on Artificial Intelligence (AI)-driven materials design, addressing the challenge of improving the sustainability of building materials amid complex formulations. These formulations involve various components, such as binders, additives, and recycled aggregates, necessitating a balance between environmental impact and performance. Traditional experimental methods often fall short in managing the complexity of material composition, hindering fast enough development of optimal solutions. Our research explores complex composition materials design through a comprehensive, comparative lab study between Data-Driven Design, using SLAMD - an open-source AI materials design tool, and traditional Design of Experiments (DOE). We aimed to develop a high-performance, alkali-activated material using secondary precursors, aiming for a compressive strength exceeding 100 MPa after 7-days. The findings reveal that AI-driven design outperforms DOE in development speed and material quality, successfully identif. 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 - Secondary Raw Materials KW - Data-Driven Design KW - Sequential Learning KW - Design of Experiments KW - Alkali-Activated Binder PY - 2024 SN - 978-3-03170281-5 DO - https://doi.org/10.1007/978-3-031-70281-5_31 SN - 2211-0852 VL - 2 SP - 274 EP - 282 PB - Springer AN - OPUS4-61662 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Zia, Ghezal Ahmad Jan A1 - Völker, Christoph A1 - Moreno Torres, Benjami A1 - Kruschwitz, Sabine T1 - An Adaptive Upscaling Approach for Assessing Materials’ Circularity Potential with Non-destructive Testing (NDT) N2 - Advancing towards a circular economy necessitates the efficient reuse and maintenance of structural materials, which relies on accurate, non-damaging condition assessments. This paper introduces an innovative AI-driven adaptive sampling (AS) technique integrated with Non-Destructive Testing (NDT) to optimize this process. AS focuses on critical data points, reducing the amount of data needed for precise assessments—evidenced by our method requiring on average only 7 samples for Logistic Regression and 8 for Random Forest, contrasted with 29 for traditional sampling. By reducing the necessity for extensive data collection, our method not only streamlines the assessment process but also significantly contributes to the sustainability goals of the circular economy. These goals include resource efficiency, waste reduction, and material reuse. Efficient condition assessments promote infrastructure longevity, reducing the need for new materials and the associated environmental impact. The circular economy aims to create a sustainable system where resources are reused, and waste is minimized. This is achieved by extending the lifecycle of materials, reducing the environmental footprint, and promoting recycling and reuse. Longevity directly contributes to the circular economy by maximizing the utility and lifespan of existing materials and structures. Longer-lasting infrastructure means fewer resources are needed for repairs or replacements, leading to reduced material consumption and waste generation. This aligns with the circular economy's principles of sustainability and resource efficiency. This research not only advances the field of structural health monitoring but also aligns with the broader objective of enhancing sustainable construction practices within the circular economy framework. T2 - Rilem Spring Convention CY - Milano, Italy DA - 09.04.2024 KW - Adaptive Sampling KW - Random Sampling KW - Machine Learning KW - Non-Destructive Testing KW - Condition Assessment KW - Circular Economy PY - 2024 SN - 978-3-031-70277-8 DO - https://doi.org/10.1007/978-3-031-70277-8_38 SN - 2211-0844 VL - 55 SP - 330 EP - 338 PB - Springer Nature Switzerland CY - Switzerland AN - OPUS4-62458 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 - Moreno Torres, Benjami A1 - Völker, Christoph A1 - Munsch, Sarah Mandy A1 - Hanke, T. A1 - Kruschwitz, Sabine ED - Tosti, F. T1 - An Ontology-Based Approach to Enable Data-Driven Research in the Field of NDT in Civil Engineering N2 - Although measurement data from the civil engineering sector are an important basis for scientific analyses in the field of non-destructive testing (NDT), there is still no uniform representation of these data. An analysis of data sets across different test objects or test types is therefore associated with a high manual effort. Ontologies and the semantic web are technologies already used in numerous intelligent systems such as material cyberinfrastructures or research databases. This contribution demonstrates the application of these technologies to the case of the 1H nuclear magnetic resonance relaxometry, which is commonly used to characterize water content and porosity distri-bution in solids. The methodology implemented for this purpose was developed specifically to be applied to materials science (MS) tests. The aim of this paper is to analyze such a methodology from the perspective of data interoperability using ontologies. Three benefits are expected from this ap-proach to the study of the implementation of interoperability in the NDT domain: First, expanding knowledge of how the intrinsic characteristics of the NDT domain determine the application of semantic technologies. Second, to determine which aspects of such an implementation can be improved and in what ways. Finally, the baselines of future research in the field of data integration for NDT are drawn. KW - Ontology Engineering KW - Interoperability KW - Data-integration KW - NMR relaxometry KW - materials informatics PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-529716 DO - https://doi.org/10.3390/rs13122426 SN - 2072-4292 N1 - Geburtsname von Munsch, Sarah Mandy: Nagel, S. M. - Birth name of Munsch, Sarah Mandy: Nagel, S. M. VL - 13 IS - 12 SP - 2426 PB - Multidisciplinary Digital Publishing Institute (MDPI) CY - Basel, Switzerland AN - OPUS4-52971 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Unger, Jörg F. A1 - Kindrachuk, Vitaliy A1 - Hirthammer, Volker A1 - Titscher, Thomas A1 - Pohl, Christoph T1 - The importance of multiphysics and multiscale modelling of concrete to understand its complex macroscopic properties N2 - Concrete is a complex material. Its properties evolve over time, especially at early age, and are dependent on environmental conditions, i.e. temperature and moisture conditions, as well as the composition of the material. This leads to a variety of macroscopic phenomena such as hydration/solidification/hardening, creep and shrinkage, thermal strains, damage and inelastic deformations. Most of these phenomena are characterized by specific set of model assumptions and often an additive decomposition of strains into elastic, plastic, shrinkage and creep components is performed. Each of these phenomena are investigated separately and a number of respective independent models have been designed. The interactions are then accounted for by adding appropriate correction factors or additional models for the particular interaction. This paper discusses the importance of reconsider even in the experimental phase the model assumptions required to generalize the experimental data into models used in design codes. It is especially underlined that the complex macroscopic behaviour of concrete is strongly influenced by its multiscale and multiphyscis nature and two examples (shrinkage and fatigue) of interacting phenomena are discussed. T2 - International RILEM Conference on Materials, Systems and Structures in Civil Engineering CY - Lyngby, Denmark DA - 22.08.2016 KW - Concrete KW - Multiscale KW - Multiphysics PY - 2016 VL - 1 SP - 115 EP - 124 PB - International RILEM Conference on Materials, Systems and Structures in Civil Engineering, Conference segment on COST TU1404 AN - OPUS4-38651 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - 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 - GEN A1 - Recknagel, Christoph A1 - Eilers, Manfred A1 - Hoppe, Johannes A1 - Cetinkaya, Reha A1 - Machill, Nicole A1 - Weitz, Dennis A1 - Zscherpe, Oliver A1 - Sikinger, Thomas A1 - Loher, erich A1 - Oelerich, Martin A1 - Schäfer, Volker A1 - Schäfer, Thorsten A1 - Uslu, Cenk A1 - Virnau, Lucas A1 - Dudenhöfer, Bernd A1 - Rückert, Phillip A1 - Alte-Teigeler, Ralf A1 - Rau, Richard T1 - Merkblatt für die Herstellung von Abdichtungssystemen aus hohlraumreichen Asphalttraggerüsten mit nachträglicher Verfüllung (HANV) für Ingenieurbauten aus Beton N2 - Konventionelle Regelbauweisen für die Ausbildung von Abdichtungssystemen auf Brücken- und anderen Ingenieurbauwerken aus Beton machen signifikante material- und systembedingte Einbau- und Sperrzeiten erforderlich. Das innovative Abdichtungssystem „Hohlraumreiches Asphalttraggerüst mit nachträglicher Verfüllung“ (kurz: HANV) bietet eine deutliche Zeitersparnis von Ausführung bis Verkehrsfreigabe. Gleichzeitig zeichnet sich diese Bauweise mit einer hohen Deformationsbeständigkeit gegenüber Schwerlastverkehr aus. Auf der Grundlage eines mittlerweile ausgeprägten Erfahrungshintergrundes wird mit Hilfe des aufgestellten Regelwerkes der Baupraxis eine qualitätssichere Ausführung mit gütegesicherter Funktionalität zur Verfügung gestellt. KW - Abdichtungssysteme KW - Verkehrs-Infrastrukutur KW - Ingenieurbauwerke aus Beton PY - 2025 SN - 978-3-86446-421-8 VL - 2025 IS - 1 SP - 3 EP - 40 PB - FGSV-Verlag CY - Wesselinger Straße 15-17 in 50999 Köln ET - 1. Auflage AN - OPUS4-62798 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF 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 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 SN - 1435-4934 SP - 696 EP - 699 AN - OPUS4-34537 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Völker, Christoph A1 - Shokouhi, P. T1 - Clustering based multi sensor data fusion for honeycomb detection in concrete KW - Data fusion KW - Concrete evaluation KW - Honeycombing KW - Machine learning KW - Clustering KW - Density based clustering PY - 2015 DO - https://doi.org/10.1007/s10921-015-0307-7 SN - 0195-9298 SN - 1573-4862 VL - 34 IS - Article 32 SP - 1 EP - 10 PB - Plenum Press CY - New York, NY AN - OPUS4-35073 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Völker, Christoph A1 - Shokouhi, P. T1 - Multi sensor data fusion approach for automatic honeycomb detection in concrete PY - 2015 DO - https://doi.org/10.1016/j.ndteint.2015.01.003 SN - 0963-8695 VL - 71 SP - 54 EP - 60 PB - Butterworth-Heinemann CY - Oxford AN - OPUS4-35074 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Völker, Christoph A1 - Kruschwitz, Sabine A1 - Moreno Torres, Benjami A1 - Firdous, R. A1 - Zia, G. J. A. A1 - Stephan, D. T1 - Accelerating the search for alkali-activated cements with sequential learning N2 - With 8% of man-made CO2 emissions, cement production is an important driver of the climate crisis. By using alkali-activated binders, part of the energy-intensive clinker production process can be dispensed. However, as numerous raw materials are involved in the manufacturing process here, the complexity of the materials increases by orders of magnitude. Finding a properly balanced binder formulation is like looking for a needle in a haystack. We have shown for the first time that artificial intelligence (AI)-based optimization of alkali-activated binder formulations can significantly accelerate research. The "Sequential Learning App for Materials Discovery" (SLAMD) aims to accelerate practice transfer. With SLAMD, materials scientists have low-threshold access to AI through interactive and intuitive user interfaces. The value added by AI can be determined directly. For example, the CO2 emissions saved per ton of cement can be determined for each development cycle: the more efficient the AI optimization, the greater the savings. Our material database already includes more than 120,000 data points of alternative binders and is constantly being expanded with new parameters. We are currently driving the enrichment of the data with a life cycle analysis of the building materials. Based on a case study we show how intuitive access to AI can drive the adoption of techniques that make a real contribution to the development of resource-efficient and sustainable building materials of the future and make it easy to identify when classical experiments are more efficient. T2 - fib International Congress CY - Oslo, Norway DA - 12.06.2022 KW - Concrete KW - Materials Design KW - Sequential Learning KW - Machine Learning PY - 2022 SP - 1 EP - 9 AN - OPUS4-56634 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Völker, Tobias A1 - Millar, Steven A1 - Strangfeld, Christoph A1 - Wilsch, Gerd T1 - Identification of type of cement through laser-induced breakdown spectroscopy N2 - The composition of concrete determines its resistance to various degradation mechanisms such as ingress of ions, carbonation or reinforcement corrosion. Knowledge of the composition of the hardened concrete is therefore helpful to assess the remaining service life of an existing structure or evaluate the damage observed during inspections. For example, for most existing concrete structures the type of cement originally used is not known and must therefore be determined afterwards. This paper presents a preliminary study on the application of laser-induced breakdown spectroscopy (LIBS) to identify the type of cement. For this purpose, ten different types of cement were investigated. For every type, three cement paste prisms were produced: (i) prisms dried, ground and pressed into tablets, (ii) prisms dried and (iii) prisms untreated. LIBS measurements were performed with a diode-pumped low energy laser (1064 nm, 3 mJ, 1.5 ns, 100 Hz) in combination with two compact spectrometers which cover the UV and NIR spectral range. A reduced subset of spectral features was used to build a classification model based on linear discriminant analysis. The results show that the classification of homogenized pressed cement powder samples provides a high accuracy, however, factors such as a different sample matrix and moisture content can affect the accuracy of the classification. The study demonstrates that LIBS is a promising tool to identify the type of cement. KW - Spectroscopy KW - LIBS KW - Cement KW - Classification KW - Identification PY - 2020 DO - https://doi.org/10.1016/j.conbuildmat.2020.120345 VL - 258 SP - 120345 PB - Elsevier Ltd. AN - OPUS4-51157 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Völker, Christoph T1 - A machine-learning based data fusion approach for improved corrosion damage monitoring N2 - Half-cell potential mapping (HP) is the most popular nondestructive test (NDT)-method for the localization of corrosion damage in concrete. It is generally recognized, that HP is prone to the environmental factors that arise from salt induced deterioration, such as varying moisture and chloride gradients. Additional NDT-methods are capable to determine distinctive areas, but cannot yet be used to estimate more accurate testing results. We introduce a supervised machine learning (SML) based approach for data fusion to make use of the additional sensor information. SML are methods that explore relations between different (sensor) data from predefined data labels. We use a simple linear classifier named logistic regression to distinguish defect and intact areas. The test performance improves drastically compared to the best single method, HP. In order to generate representative, labeled data we conducted a comprehensive experiment that simulates the deterioration-cycle of a chloride-exposed building part in the lab. Our data set consist of 18 measurement campaigns, each containing HP-, ground-penetrating-radar-, microwave-moisture-, and Wenner-resistivity-data. We detail the challenges that arise with a data driven approach in NDT and how we addressed them. T2 - NDE2017 CY - Chennai, India DA - 14.12.2017 KW - Data fusion KW - NDT KW - Concrete KW - Corrosion KW - Machine learning PY - 2017 AN - OPUS4-44101 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Völker, Christoph A1 - Kruschwitz, Sabine A1 - Ebell, Gino T1 - Datengesteuerte Multisensor-Fusion zur Korrosionsprüfung von Stahlbetonbauteilen N2 - Potentialfeldmessung (PM) ist die beliebteste Methode der Zerstörungsfreien Prüfung (ZfP) zur Lokalisierung von aktiver Betonstahlkorrosion. PM wird durch Parameter wie z. B. Feuchtigkeits- und Chloridgradienten im Bauteil beeinflusst, so dass die Sensitivität gegenüber der räumlich sehr begrenzten, aber gefährlichen Lochkorrosion gering ist. Wir zeigen in dieser Studie, wie zusätzliche Messinformationen mit Multisensor-Datenfusion genutzt werden können, um die Detektionsleistung zu verbessern und die Auswertung zu automatisieren. Die Fusion basiert auf überwachtem maschinellen Lernen (ÜML). ÜML sind Methoden, die Zusammenhänge in (Sensor-) Daten anhand vorgegebener Kennzeichnungen (Label) erkennen. Wir verwenden ÜML um „defekt“ und „intakt“ gelabelte Bereiche in einem Multisensordatensatz zu unterscheiden. Unser Datensatz besteht aus 18 Messkampagnen und enthält jeweils PM-, Bodenradar-, Mikrowellen-Feuchte- und Wenner-Widerstandsdaten. Exakte Label für veränderliche Umweltbedingungen wurden in einer Versuchsanordnung bestimmt, bei der eine Stahlbetonplatte im Labor kontrolliert und beschleunigt verwittert. Der Verwitterungsfortschritt wurde kontinuierlich überwacht und die Korrosion gezielt erzeugt. Die Detektionsergebnisse werden quantifiziert und statistisch ausgewertet. Die Datenfusion zeigt gegenüber dem besten Einzelverfahren (PM) eine deutliche Verbesserung. Wir beschreiben die Herausforderungen datengesteuerter Ansätze in der zerstörungsfreien Prüfung und zeigen mögliche Lösungsansätze. T2 - DGZfP Jahrestagung 2018 CY - Leipzig, Germany DA - 07.05.2018 KW - Maschinelles Lernen KW - Datenfusion KW - ZfP KW - Beton KW - Korrosion PY - 2018 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-444852 UR - http://www.ndt.net/?id=23106 SN - 1435-4934 VL - 23 IS - 9 SP - 1 EP - 9 PB - NDT.net CY - Kirchwald AN - OPUS4-44485 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Völker, Christoph T1 - Toward Data based corrosion analysis of concrete with supervised machine learning N2 - Half-Cell-Potential Mapping (HP) is the most popular non-destructive testing (NDT) method for the detection of active corrosion in reinforced concrete. HP is influenced by parameters such as moisture and chloride gradients in the component. The sensitivity to the spatially small, but dangerous pitting is low. In this study we show how additional measurement information can be used with multi-sensor data fusion to improve the detection performance and to automate data evaluation. The fusion is based on supervised machine learning (SML). SML are methods that recognize relationships in (sensor) data based on given labels. We use SML to distinguish "defective" and "intact" labeled areas in our dataset. It consists of 18 measurement - each contains HP, ground radar, microwave moisture and Wenner resistivity data. Exact labels for changing environmental conditions were determined in a laboratory study on a reinforced concrete slab, which deteriorated controlled and accelerated. The deterioration progress was monitored continuously and corrosion was generated targeted at a predefined location. The detection results are quantified and statistically evaluated. The data fusion shows a significant improvement over the best single method (HP). We describe the challenges of data-driven approaches in nondestructive testing and show possible solutions. T2 - SMT/NDT-CE 2018 CY - New Brunswick, NJ, USA DA - 26.08.2018 KW - SMT KW - NDT-CE PY - 2018 AN - OPUS4-46261 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - 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 -