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 - 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 - Moreno Torres, Benjamí 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 - 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 - Klewe, Tim A1 - Strangfeld, Christoph A1 - Ritzer, Tobias A1 - Kruschwitz, Sabine T1 - Combining Signal Features of Ground-Penetrating Radar to Classify Moisture Damage in Layered Building Floors N2 - To date, the destructive extraction and analysis of drilling cores is the main possibility to obtain depth information about damaging water ingress in building floors. The time- and costintensive procedure constitutes an additional burden for building insurances that already list piped water damage as their largest item. With its high sensitivity for water, a ground-penetrating radar (GPR) could provide important support to approach this problem in a non-destructive way. In this research, we study the influence of moisture damage on GPR signals at different floor constructions. For this purpose, a modular specimen with interchangeable layers is developed to vary the screed and insulation material, as well as the respective layer thickness. The obtained data set is then used to investigate suitable signal features to classify three scenarios: dry, damaged insulation, and damaged screed. It was found that analyzing statistical distributions of A-scan features inside one B-scan allows for accurate classification on unknown floor constructions. Combining the features with multivariate data analysis and machine learning was the key to achieve satisfying results. The developed method provides a basis for upcoming validations on real damage cases. KW - Radar KW - Material Moisture KW - Non-destructive testing KW - Signal Features KW - Civil Engineering KW - Machine Learning PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-533606 DO - https://doi.org/10.3390/app11198820 VL - 11 IS - 19 SP - 8820 PB - MDPI AN - OPUS4-53360 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 - JOUR A1 - Kruschwitz, Sabine A1 - Halisch, M. A1 - Dlugosch, R. A1 - Prinz, Carsten T1 - Toward a better understanding of low-frequency electrical relaxation - An enhanced pore space characterization N2 - Relaxation phenomena observed in the electrical low-frequency range (approximately 1 mHz-10 kHz) of natural porous media like sandstones is often assumed to be directly related to the dominant (modal) pore throat sizes measured, for instance, with mercury intrusion porosimetry. Attempts to establish a universally valid relationship between pore size and peak Spectral Induced Polarization (SIP) relaxation time have failed, considering sandstones from very different origins and featuring great variations in textural and chemical compositions as well as in geometrical pore space properties. In addition working with characteristic relaxation times determined in Cole-Cole or Debye decomposition fits to build the relationship have not been successful. In particular, samples with narrow pore throats are often characterized by long SIP relaxation times corresponding to long “characteristic length scales” in these media, assuming that the diffusion coefficients along the electrical double layer were constant. Based on these observations, three different types of SIP relaxation can be distinguished. We present a new way of assessing complex pore spaces of very different sandstones in a multi-methodical approach to combine the benefits of mercury intrusion porosimetry, micro-computed tomography, and nuclear magnetic resonance. In this way, we achieve much deeper insight into the pore space due to the different resolutions and sensitivities of the applied methods to both pore constrictions (throats) and wide pores (pore bodies). We experimentally quantify pore aspect ratios and volume distributions within the two pore regions. We clearly observe systematic differences between three SIP relaxation types identified previously and can attribute the SIP peak relaxation times to measured characteristic length scales within our materials. We highlight selected results for a total of nine sandstones. It seems that SIP relaxation behavior depends on the size difference of the narrow pore throats to the wide pore bodies, which increases from SIP Type 1 to Type 3. KW - µ-CT KW - Spectral induced polarization KW - Nuclear magnetic resonance KW - Pore space PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-509763 DO - https://doi.org/10.1190/GEO2019-0074.1 SN - 0016-8033 VL - 85 IS - 4 SP - MR257 EP - MR270 PB - Society of Exploration Geophysicists AN - OPUS4-50976 LA - eng 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 - JOUR A1 - Munsch, Sarah Mandy A1 - Strangfeld, Christoph A1 - Kruschwitz, Sabine ED - Kärger, J. ED - Heitjans, P. T1 - Determining the pore size distribution in synthetic and building materials using 1D NMR N2 - NMR is gaining increasing interest in civil engineering applications for the use of microstructure characterization as e.g. pore size determination and monitoring of moisture transport in porous materials. In this study, the use of NMR as a tool for pore size characterization was investigated. For our study we used screed and synthetic materials at partial and full saturation. A successful determination could be achieved when having a reference or calibration method, although partly diffusion effects have been registered. Due to these diffusion effects, for the determination of pore size distributions of synthetic materials another NMR device was needed. Finally, the determination of the surface relaxivity of screed (50 μm/s) led to a higher value than first expected from literature. T2 - 14th International Bologna Conference on Magnetic Resonance in Porous Media CY - Gainesville, FL, USA DA - 18.02.2018 KW - NMR relaxometry KW - Pore size distribution KW - Building materials KW - Porous materials KW - Surface relaxivity PY - 2019 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-483680 UR - https://diffusion.uni-leipzig.de/pdf/volume31/diff_fund_31(2019)02.pdf SN - 1862-4138 N1 - Geburtsname von Munsch, Sarah Mandy: Nagel, S. M. - Birth name of Munsch, Sarah Mandy: Nagel, S. M. VL - 31 IS - 2 SP - 1 EP - 9 PB - University of Leipzig CY - Leipzig AN - OPUS4-48368 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Klewe, Tim A1 - Strangfeld, Christoph A1 - Kruschwitz, Sabine T1 - Review of moisture measurements in civil engineering with ground penetrating radar – Applied methods and signal features N2 - When applying Ground Penetrating Radar (GPR) to assess the moisture content of building materials, different medium properties, dimensions, interfaces and other unknown influences may require specific strategies to achieve useful results. Hence, we present an overview of the various approaches to carry out moisture measurements with GPR in civil engineering (CE). We especially focus on the applied Signal features such as time, amplitude and frequency features and discuss their limitations. Since the majority of publications rely on one single feature when applying moisture measurements, we also hope to encourage the consideration of approaches that combine different signal features for further developments. KW - Ground Penetrating Radar KW - Moisture KW - Civil engineering KW - Signal features PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-520684 DO - https://doi.org/10.1016/j.conbuildmat.2021.122250 VL - 278 SP - 122250 PB - Elsevier Ltd. AN - OPUS4-52068 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -