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 - Klewe, Tim A1 - Strangfeld, Christoph A1 - Ritzer, Tobias A1 - Kruschwitz, Sabine T1 - Classification of Practical Floor Moisture Damage Using GPR - Limits and Opportunities N2 - Machine learning in non-destructive testing (NDT) offers significant potential for efficient daily data analysis and uncovering previously unknown relationships in persistent problems. However, its successful application heavily depends on the availability of a diverse and well-labeled training dataset, which is often lacking, raising questions about the transferability of trained algorithms to new datasets. To examine this issue closely, the authors applied classifiers trained with laboratory Ground Penetrating Radar (GPR) data to categorize on-site moisture damage in layered building floors. The investigations were conducted at five different locations in Germany. For reference, cores were taken at each measurement point and labeled as (i) dry, (ii) with insulation damage, or (iii) with screed damage. Compared to the accuracies of 84 % to 90 % within the laboratory training data (504 B-Scans), the classifiers achieved a lower overall accuracy of 53 % for on-site data (72 B-Scans). This discrepancy is mainly attributable to a significantly higher dynamic of all signal features extracted from on-site measurements compared to laboratory training data. Nevertheless, this study highlights the promising sensitivity of GPR for identifying individual damage cases. In particular the results showing insulation damage, which cannot be detected by any other non-destructive method, revealed characteristic patterns. The accurate interpretation of such results still depends on trained personnel, whereby fully automated approaches would require a larger and diverse on-site data set. Until then, the findings of this work contribute to a more reliable analysis of moisture damage in building floors using GPR and offer practical insights into applying machine learning to non-destructive testing for civil engineering (NDT-CE). KW - GPR KW - Material moisture KW - Building floor KW - Machine Learning PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-607932 DO - https://doi.org/10.1007/s10921-024-01111-7 SN - 0195-9298 VL - 43 IS - 3 SP - 1 EP - 16 PB - Springer Science and Business Media LLC AN - OPUS4-60793 LA - eng 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 -