TY - GEN A1 - Aßmann, Norman A1 - Bühling, Benjamin T1 - Impact-echo dataset "Radarplatte" N2 - This dataset contains raw data resulting from Impact-Echo measurements at the reference conrete block "Radarplatte", located at BAM (German Federal Institute for Materials Research and Testing). This specimen has been described in detail by Niederleithinger et al. (2021), who applied muon tomography, ultrasonic echo measurements, radar and X-ray laminography to visualize its internal structure. The Impact-Echo method is based on the excitation of the zero-group-velocity frequency of the first symmetric Lamb mode of a plate-like structure, in order to assess its thickness. Numerous publication elaborate on Impact-Echo theory, examples are (Gibson and Popovics 2005, Schubert and Köhler 2008 , Abraham and Popovics 2010). The measurements have been conducted using a setup that contains only commercially available components. The setup consists of an Olson CTG-2 concrete thickness gauge (Olsen Instruments, USA) for actuation and sensing and an 8-bit NI USB-5132 digital storage oscilloscope (National Instruments, USA) combined with the Echolyst software (Schweizerischer Verein für technische Inspektionen (SVTI), Switzerland) for data acquisition. Measurements were conducted using a grid of 23x23 points with a spacing of 50 mm. At each point 8192 samples were recorded at a sampling rate of 1 MS/s. The dataset contains the (X,Y) location in mm of the individual measurement points as well as the raw measurement data at those points. The data is provided in the formats *.mir/*.mhdr (Echolyst), *.npy (Python) and *.mat (Matlab) and *.csv to ease the import in various post-processing tools. KW - Nondestructive testing KW - Impact-echo KW - S1-ZGV mode KW - Concrete PY - 2021 U6 - https://doi.org/10.7910/DVN/UNOH2U PB - Harvard College CY - Cambridge, MA, USA AN - OPUS4-53886 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Grotelüschen, Bjarne A1 - Bühling, Benjamin T1 - Impact-Echo Dataset "IE Platte" N2 - This dataset contains raw data resulting from Impact-Echo measurements at the reference concrete block "IE Platte", located at BAM (German Federal Institute for Materials Research and Testing). The specimen contains three polystyrene slabs and one polyethylene foil to act as reflectors. The specimen was produced in a three-step process. First, the base plate was cast. Second, the reflectors were taped to the base plate. Finally, the upper layer was cast on top of base plate and reflectors. A drawing is contained in the dataset. The Impact-Echo method is based on the excitation of the zero-group-velocity frequency of the first symmetric Lamb mode of a plate-like structure, in order to assess its thickness. Numerous publications elaborate on Impact-Echo theory, examples are (Gibson and Popovics 2005, Schubert and Köhler 2008 , Abraham and Popovics 2010). The measurements have been conducted using a setup that contains only commercially available components. The setup consists of an Olson CTG-2 concrete thickness gauge (Olsen Instruments, USA) for actuation and sensing and an 8-bit NI USB-5132 digital storage oscilloscope (National Instruments, USA) combined with the Echolyst software (Schweizerischer Verein für technische Inspektionen (SVTI), Switzerland) for data acquisition. Measurements were conducted using a grid of 29x29 points with a spacing of 50 mm. At each point 8192 samples were recorded at a sampling rate of 1 MS/s. The dataset contains the (X,Y) location in mm of the individual measurement points as well as the raw measurement data at those points. The data is provided in the formats *.mir/*.mhdr (Echolyst), *.npy (Python) and *.mat (Matlab) and *.csv to ease the import in various post-processing tools. KW - Concrete KW - Impact-Echo KW - Nondestructive testing PY - 2022 U6 - https://doi.org/10.7910/DVN/EH4E9G PB - Harvard College CY - Cambridge, MA, USA AN - OPUS4-55279 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Völker, Christoph A1 - Moreno Torres, Benjami T1 - SLAMD-FIB-Case-Study 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 with. However, because numerous chemicals are involved in the manufacturing process here, the complexity of the materials increases by orders of magnitude. Finding a properly balanced cement formulation is like looking for a needle in a haystack. We have shown for the first time that artificial intelligence (AI)-based optimization of cement 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 cements 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. KW - Alkali activated concrete KW - Concrete PY - 2022 UR - https://github.com/BAMcvoelker/SLAMD-FIB-Case-Study#slamd-fib-case-study PB - GitHub CY - San Francisco, CA, USA AN - OPUS4-56637 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -