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    <title language="eng">Nondestructive determination of moisture damage in layered building floors</title>
    <abstract language="eng">In this ongoing research project, we study the influence of moisture damage on Ground Penetrating Radar (GPR) in different floor constructions. For this purpose, a measurement setup with interchangeable layers is developed to vary the screed material (cement or anhydrite) and insulation material (glass wool, perlite, expanded and extruded polystyrene), as well as the respective layer thickness. The evaluation of the 2 GHz common-offset radar measurements is focused on the extraction of distinctive signal features that can be used to classify the underlying case of damage without any further information about the hidden materials or layer thicknesses. In the collected dataset, we analyze the horizontal distribution of A-scan features in corresponding B-scans to detect water in the insulation layer. Furthermore, possible combinations of these features are investigated with the use of multivariate data analysis and machine learning (logistic regression) in order to evaluate the mutual dependencies. In this study, the combination of an amplitude- and frequency-based feature achieved an accuracy of 93.2 % and performed best to detect a damage in floor insulations.</abstract>
    <parentTitle language="eng">18th International Conference on Ground Penetrating Radar</parentTitle>
    <identifier type="doi">10.1190/gpr2020-045.1</identifier>
    <identifier type="issn">2159-6832</identifier>
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    <author>Tim Klewe</author>
    <author>Christoph Strangfeld</author>
    <author>Tobias Ritzer</author>
    <author>Sabine Kruschwitz</author>
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      <language>deu</language>
      <type>uncontrolled</type>
      <value>Radar</value>
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      <value>Feuchte</value>
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      <language>eng</language>
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      <value>Moisture</value>
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      <language>eng</language>
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      <value>Building floors</value>
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    <title language="eng">Overview of NDT techniques for moisture measurements in building materials</title>
    <abstract language="eng">Overview of NDT techniques for moisture measurements in building materials used in department 8 of BAM.</abstract>
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    <author>Tim Klewe</author>
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      <value>Moisture</value>
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    <title language="eng">Laser-based sorting of construction and demolition waste for the circular economy</title>
    <abstract language="eng">Closed material cycles and unmixed material fractions are required to achieve high recovery and recycling rates in the building industry. The growing diversity of construction and demolition waste is leading to increasing difficulties in separating the individual materials. Manual sorting involves many risks and dangers for the executing staff and is merely based on obvious, visually detectable differences for separation. An automated, sensor-based sorting of these building materials could complement or replace this practice to improve processing speed, recycling rates, sorting quality, and prevailing health conditions.&#13;
A joint project of partners from industry and research institutions approaches this task by investigating and testing the combination of laser-induced breakdown spectroscopy (LIBS) and visual (VIS)/ near-infrared (NIR) spectroscopy. Joint processing of information (data fusion) is expected to significantly improve the sorting quality of various materials like concrete, main masonry building materials, organic components, etc., and may enable the detection and separation of impurities such as SO3-containing building materials (gypsum, aerated concrete, etc.). Focusing on Berlin as an example, the entire value chain will be analyzed to minimize economic/technological barriers and obstacles at the cluster level and to sustainably increase recovery and recycling rates.&#13;
First LIBS measurements show promising results in distinguishing various material types. A meaningful validation shall be achieved with further practical samples. Future works will investigate the combination of LIBS and VIS/NIR spectroscopy in a fully automated measurement setup with conveyor belt speeds of 3 m/s.</abstract>
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    <author>Tim Klewe</author>
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      <language>eng</language>
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    <publisherPlace>Bad Breisig</publisherPlace>
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    <title language="eng">Sorting of construction and demolition waste by combining LIBS with NIR spectroscopy</title>
    <abstract language="eng">In a joint project of partners from industry and research, the automated recycling of construction and demolition waste (CDW) is investigated and tested by combing laser-induced breakdown spectroscopy (LIBS) and near-infrared (NIR) spectroscopy. Joint processing of information (data fusion) is expected to significantly improve the sorting quality of various materials like concrete, main masonry building materials, organic components, etc., and may enable the detection and separation of impurities such as SO3-cotaining building materials (gypsum, aerated concrete, etc.). The project focuses primarily on the Berlin site to analyze the entire value chain, minimize economic/technological barriers and obstacles at the cluster level, and sustainably increase recovery and recycling rates. First measurements with LIBS and NIR spectroscopy show promising results in distinguishing various material types and indicate the potential for a successful combination. In addition, X-ray fluorescence (XRF) spectroscopy is being performed to obtain more information about the quantitative elemental composition of the different building materials. Future work will apply the developed sorting methodology in a fully automated measurement setup with CDW on a conveyor belt.</abstract>
    <parentTitle language="eng">International Symposium Non-Destructive Testing in Civil Engineering (NDTCE 2022)</parentTitle>
    <identifier type="url">http://www.ndt.net/?id=27220</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Tim Klewe</author>
    <author>Tobias Völker</author>
    <author>J. Götz</author>
    <author>M. Landmann</author>
    <author>Gerd Wilsch</author>
    <author>Sabine Kruschwitz</author>
    <subject>
      <language>eng</language>
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      <value>LIBS</value>
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      <language>eng</language>
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      <value>Material classification</value>
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    <title language="eng">Sorting of construction and demolition waste by combining LIBS with NIR spectroscopy</title>
    <abstract language="eng">In a joint project of partners from industry and research, the automated recycling of construction and demolition waste (CDW) is investigated and tested by combing laser-induced breakdown spectroscopy (LIBS) and near-infrared (NIR) spectroscopy. Joint processing of information (data fusion) is expected to significantly improve the sorting quality of various materials like concrete, main masonry building materials, organic components, etc., and may enable the detection and separation of impurities such as SO3-cotaining building materials (gypsum, aerated concrete, etc.). The project focuses primarily on the Berlin site to analyze the entire value chain, minimize economic/technological barriers and obstacles at the cluster level, and sustainably increase recovery and recycling rates. First measurements with LIBS and NIR spectroscopy show promising results in distinguishing various material types and indicate the potential for a successful combination. In addition, X-ray fluorescence (XRF) spectroscopy is being performed to obtain more information about the quantitative elemental composition of the different building materials. Future work will apply the developed sorting methodology in a fully automated measurement setup with CDW on a conveyor belt.</abstract>
    <enrichment key="eventName">NDT-CE 2022 - The International Symposium on Nendestructive Testing in Civil Engineering</enrichment>
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    <title language="eng">Laser-based sorting of construction and demolition waste for the circular economy</title>
    <abstract language="deu">Closed material cycles and unmixed material fractions are required to achieve high recovery and recycling rates in the building industry. The growing diversity of construction and demolition waste is leading to increasing difficulties in separating the individual materials. Manual sorting involves many risks and dangers for the executing staff and is merely based on obvious, visually detectable differences for separation. An automated, sensor-based sorting of these building materials could complement or replace this practice to improve processing speed, recycling rates, sorting quality, and prevailing health conditions.&#13;
A joint project of partners from industry and research institutions approaches this task by investigating and testing the combination of laser-induced breakdown spectroscopy (LIBS) and visual (VIS)/ near-infrared (NIR) spectroscopy. Joint processing of information (data fusion) is expected to significantly improve the sorting quality of various materials like concrete, main masonry building materials, organic components, etc., and may enable the detection and separation of impurities such as SO3-containing building materials (gypsum, aerated concrete, etc.). Focusing on Berlin as an example, the entire value chain will be analyzed to minimize economic/technological barriers and obstacles at the cluster level and to sustainably increase recovery and recycling rates.&#13;
First LIBS measurements show promising results in distinguishing various material types. A meaningful validation shall be achieved with further practical samples. Future works will investigate the combination of LIBS and VIS/NIR spectroscopy in a fully automated measurement setup with conveyor belt speeds of 3 m/s.</abstract>
    <parentTitle language="eng">Proceedings of the 6th fib Congress 2022</parentTitle>
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    <author>Tim Klewe</author>
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    <author>Gerd Wilsch</author>
    <author>Sabine Kruschwitz</author>
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      <language>deu</language>
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      <value>LIBS</value>
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      <language>deu</language>
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      <language>deu</language>
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      <language>deu</language>
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      <value>Recycling</value>
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      <language>deu</language>
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      <value>Material classification</value>
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    <title language="eng">Laser Induced Breakdown Spectroscopy – A Tool for Imaging the Chemical Composition of Concrete</title>
    <abstract language="eng">One of the most common causes of damage is the ingress of harmful ions into the concrete, which can lead to deterioration processes and affect structural performance. Therefore, the increasingly aging infrastructure is regularly inspected to assess durability. Regular chemical analysis can be useful to determine the extent and evolution of ion ingress and to intervene in a timely manner. This could prove more economical than extensive repairs for major damage, particularly for critical infrastructure. In addition to already established elemental analysis techniques in civil engineering such as potentiometric titration or X-ray fluorescence analysis, laser-induced breakdown spectroscopy (LIBS) can provide further important complementary information and benefits. The possibilities of LIBS are demonstrated using the example of a drill core taken from a parking garage.</abstract>
    <parentTitle language="eng">ICCRRR 2022 Book of extended abstracts</parentTitle>
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    <author>Gerd Wilsch</author>
    <author>Tobias Völker</author>
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    <author>Sabine Kruschwitz</author>
    <subject>
      <language>eng</language>
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      <language>eng</language>
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      <value>Concrete</value>
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      <value>Chlorine</value>
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    <title language="eng">Laser-induced breakdown spectroscopy to investigate the chemical composition of concrete</title>
    <abstract language="eng">Laser-induced breakdown spectroscopy (LIBS) is a spectroscopic method for the analysis of the chemical composition of sample materials. Generally, the measurement of all elements of the periodic table is possible. In particular, light elements such as H, Li, Be, S, C, O, N and halogens can be measured. Calibration with matrix-matching standards allows the quantification of element concentrations. In combination with scanner systems, the two-dimensional element distribution can be determined. Even rough surfaces can be measured by online adjustment of the laser focus. LIBS can also be used on-site with mobile systems. Hand-held systems are available for point measurements.&#13;
Common applications include the investigation of material deterioration due to the ingress of harmful ions and their interaction in porous building materials. Due to the high spatial resolution of LIBS and the consideration of the heterogeneity of concrete, the determination of precise input parameters for simulation and modelling of the remaining lifetime of a structure is possible. In addition to the identification of materials, it is also possible to assess the composition for example of hardened concrete, which involves the cement or aggregate type used. Other important fields of application are the detection of environmentally hazardous elements or the material classification for sorting heterogeneous material waste streams during dismantling. Non-contact NDT for “difficult to assess” structures as an example application through safety glass or in combination with robotics and automation are also possible. &#13;
In this work, an overview of LIBS investigations on concrete is given based on exemplary laboratory and on-site applications.</abstract>
    <parentTitle language="eng">Proceedings of the International Conference on Non-destructive Evaluation of Concrete in Nuclear Applications</parentTitle>
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    <title language="deu">Methodenentwicklung der sensorgestützten Sortierung von Bau- und Abbruchabfällen</title>
    <abstract language="deu">Beim Recycling von Bau- und Abbruchabfällen werden bisher bevorzugt einfache, aber bewährte Techniken eingesetzt, um große Mengen an Bauschutt in kurzer Zeit zu verarbeiten. Dies steht im Gegensatz zu den immer komplexer werdenden Verbundwerkstoffen und Strukturen in der Mineralbaustoffindustrie. Die manuelle Sortierung birgt viele Risiken und Gefahren für das ausführende Personal und basiert lediglich auf offensichtlichen, visuell erkennbaren Unterschieden zur Trennung. Eine automatisierte, sensorgestützte Sortierung dieser Baustoffe könnte diese Praxis ergänzen oder ersetzen, um die Verarbeitungsgeschwindigkeit, die Recyclingquoten, die Sortierqualität und die gesundheitlichen Bedingungen zu verbessern.&#13;
Ein gemeinsames Projekt von Partnern aus Industrie und Forschungseinrichtungen nähert sich dieser Aufgabe, indem es die Kombination von Laser-induzierter Breakdown-Spektroskopie (LIBS) und VIS/NIR-Spektroskopie untersucht und testet. Durch die gemeinsame Verarbeitung der Informationen soll die Sortierqualität verschiedener Materialien wie Beton, Hauptmauerwerksbaustoffe, organische Bestandteile usw. deutlich verbessert werden. Darüber hinaus könnten Verunreinigungen wie SO3-haltige Baustoffe (Gips, Porenbeton usw.) erkannt und getrennt werden.&#13;
Wir stellen aktuelle Fortschritte und Ergebnisse der Entwicklung eines Prüfstands vor, der LIBS mit NIR-Spektroskopie und visueller Bildgebung kombiniert. In Zukunft wird dieser Laborprototyp als vollautomatischer Messaufbau dienen, um eine Echtzeitklassifizierung von Bau- und Abbruchabfällen auf einem Förderband zu ermöglichen.</abstract>
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    <title language="eng">LIBS‐ConSort: Development of a sensor‐based sorting method for construction and demolition waste</title>
    <abstract language="eng">AbstractA joint project of partners from industry and research institutions approaches the challenge of construction and demolition waste (CDW) sorting by investigating and testing the combination of laser‐induced breakdown spectroscopy (LIBS) with near‐infrared (NIR) spectroscopy and visual imaging. Joint processing of information (data fusion) is expected to significantly improve the sorting quality of various materials like concrete, main masonry building materials, organic components, etc., and may enable the detection and separation of impurities such as SO3‐cotaining building materials (gypsum, aerated concrete, etc.)Focusing on Berlin as an example, the entire value chain will be analyzed to minimize economic / technological barriers and obstacles at the cluster level and to sustainably increase recovery and recycling rates.The objective of this paper is to present current progress and results of the test stand development combining LIBS with NIR spectroscopy and visual imaging. In the future, this laboratory prototype will serve as a fully automated measurement setup to allow real‐time classification of CDW on a conveyor belt.</abstract>
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    <title language="eng">LIBS-ConSort: Sensor-based sorting of construction and demolition waste</title>
    <abstract language="eng">In construction and demolition waste (CDW) recycling, the preference to date has been to apply simple but proven techniques to sort and process large quantities of construction rubble in a short time. This contrasts with the increasingly complex composite materials and structures in the mineral building materials industry. An automated, sensor-based sorting of these building materials could complement or replace the practice of manual sorting to improve processing speed, recycling rates, sorting quality, and prevailing health conditions for the executing staff. &#13;
A joint project of partners from industry and research institutions approaches this task by investigating and testing the combination of laser-induced breakdown spectroscopy (LIBS) with near-infrared (NIR) spectroscopy and visual imaging. Joint processing of information (data fusion) is expected to significantly improve the sorting quality of CDW, and may enable the detection and separation of impurities such as SO3-cotaining building materials (gypsum, aerated concrete, etc.) &#13;
We present current advances and results about the methodological development combining LIBS with NIR spectroscopy and visual imaging. Here, applying data fusion proves itself beneficial to improve recognition rates. In the future, a laboratory prototype will serve as a fully automated measurement setup to allow real-time classification of CDW on a conveyor belt.</abstract>
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    <title language="eng">LIBS ConSort: Development of a sensor-based sorting method for constuction and demolition waste</title>
    <abstract language="eng">Closed material cycles and unmixed material fractions are required to achieve high recovery and recycling rates in the building industry. In construction and demolition waste (CDW) recycling, the preference to date has been to apply simple but proven techniques to process large quantities of construction rubble in a short time. This is in contrast to the increasingly complex composite materials and structures in the mineral building materials industry. Manual sorting involves many risks and dangers for the executing staff and is merely based on obvious, visually detectable differences for separation. An automated, sensor-based sorting of these building materials could complement or replace this practice to improve processing speed, recycling rates, sorting quality, and prevailing health conditions. A joint project of partners from industry and research institutions approaches this task by investigating and testing the combination of laser-induced breakdown spectroscopy (LIBS) with near-infrared (NIR) spectroscopy and visual imaging. Joint processing of information (data fusion) is expected to significantly improve the sorting quality of various materials like concrete, main masonry building materials, organic components, etc., and may enable the detection and separation of impurities such as SO3-cotaining building materials (gypsum, aerated concrete, etc.) Focusing on Berlin as an example, the entire value chain will be analyzed to minimize economic / technological barriers and obstacles at the cluster level and to sustainably increase recovery and recycling rates. We present current advances and results about the test stand development combining LIBS with NIR spectroscopy and visual imaging. In the future, this laboratory prototype will serve as a fully automated measurement setup to allow real-time classification of CDW on a conveyor belt.</abstract>
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  </doc>
  <doc>
    <id>52068</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>122250</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
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    <title language="eng">Review of moisture measurements in civil engineering with ground penetrating radar – Applied methods and signal features</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Construction and Building Materials</parentTitle>
    <identifier type="doi">10.1016/j.conbuildmat.2021.122250</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-520684</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">29.04.2021</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Tim Klewe</author>
    <author>Christoph Strangfeld</author>
    <author>Sabine Kruschwitz</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Ground Penetrating Radar</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Moisture</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Civil engineering</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Signal features</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
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  </doc>
  <doc>
    <id>52222</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
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    <pageLast/>
    <pageNumber/>
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    <title language="deu">Feuchtemessung an Fußböden mit Radar und Neutronensonde – Ein Vergleich von Labor und Praxis</title>
    <abstract language="deu">Nach Auftreten eines Feuchteschadens in Fußbodenaufbauten sind sowohl die zeitnahe Feststellung als auch Eingrenzung betroffener Bereiche von besonderer Bedeutung. So können erforderliche Renovierungsmaßnahmen effizient geplant und mögliche Folgeschäden vermieden werden. Zur messtechnischen Beantwortung dieser Fragestellungen werden bereits seit vielen Jahren Neutronensonden eingesetzt, welche allerdings keine Tiefenzuordnung des schadensträchtigen Flüssigwassers zulassen. Hierzu müssen mit hohem zeitlichem und finanziellem Aufwand zerstörende Sondierungsbohrungen vorgenommen werden, welche zukünftig durch den parallelen Einsatz des Radarverfahrens vermieden werden könnten. In systematischen Laborstudien wurden gängige Feuchteschäden an häufig anzutreffenden Fußbodenaufbauten simuliert und deren Einfluss auf das Messsignal untersucht. Hierbei kamen Zementund Anhydritestriche, sowie die Dämmmaterialen Styropor, Styrodur, Glaswolle und Perlite-Schüttung mit variierenden Schichtdicken zum Einsatz, um die vielfältigen Konfigurationen der Praxis abzubilden. Für den gewonnenen Datensatz konnten geeignete Signalmerkmale extrahiert werden, welche mithilfe multivariater Datenauswertung eine Klassifizierung des vorliegenden Schadenfalls zulassen. Zum Ende des Forschungsvorhabens steht besonders die Anwendbarkeit der entwickelten Methoden für die Praxis im Fokus. Hierzu wurden Messungen an realen Schadensfällen durchgeführt und deren Ergebnisse mit den Laboruntersuchungen verglichen. Hierbei stellten sich variierende Schichtdicken, sowie vorkommende Fußbodenheizungen und Armierungsgitter als mögliche Fehlerquellen für eine vollständig automatisierte Auswertung heraus.</abstract>
    <identifier type="doi">10.23689/fidgeo-3955</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-522227</identifier>
    <enrichment key="eventName">81. Jahrestagung der Deutschen Geophysikalischen Gesellschaft (DGG)</enrichment>
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    <enrichment key="eventStart">01.03.2021</enrichment>
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    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Tim Klewe</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Radar</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Feuchte</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Fußboden</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Neutronensonde</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
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    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/52222/2021_DGG_ePoster_Klewe_2.pdf</file>
  </doc>
  <doc>
    <id>50414</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
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    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Signalmerkmale des Radarverfahrens zur Klassifizierung von Feuchteschäden in Fußbodenaufbauten</title>
    <abstract language="deu">Zur Eingrenzung und Klassifizierung auftretender Feuchteschäden in geschichteten Fußbodenaufbauten wird bereits seit vielen Jahren das Neutronensondenverfahren eingesetzt. Während eine Eingrenzung des Schadens bereits durch die zerstörungsfrei aufgenommenen Messdaten möglich ist, erfordert die Klassifizierung zeit- und kostenintensive Sondierungsbohrungen. Die somit gewonnenen Tiefeninformationen zur Lage des Flüssigwassers tragen maßgeblich zur Planung der erforderlichen Reparaturarbeiten bei, was die Frage nach einer zerstörungsfreien Alternative zur vertikalen Lokalisierung von Feuchte in Fußböden stellt. Das Radarverfahren ist mit seiner hohen Sensitivität für Wasser besonders geeignet und soll künftig zur automatisierten Klassifizierung typischer Schadensfälle beitragen.&#13;
In einem laufenden Forschungsvorhaben werden in systematischen Laborstudien gängige Schadensfälle an häufig anzutreffenden Fußbodenaufbauten simuliert und deren Einfluss auf die genannten Verfahren untersucht. Hierbei kommen Zement- und Anhydritestriche, sowie unterschiedliche Dämmmaterialen mit variierenden Schichtdicken zum Einsatz.&#13;
Wesentlicher Bestandteil der Auswertung ist die Extraktion signifikanter Signalmerkmale des Radarverfahrens, welche Rückschlüsse auf den Schadensfall und ggf. die Wassermenge zulassen. Die Robustheit gegenüber unterschiedlichsten Aufbauten mit wechselnden Schichtdicken und Materialien steht hierbei besonders im Fokus. Erste Ergebnisse empfehlen die Betrachtung der örtlichen Verteilung bestimmter Signalmerkmale im Radargramm (B-Scan), um eine Klassifizierung der Schadensfälle vornehmen zu können.</abstract>
    <identifier type="urn">urn:nbn:de:kobv:b43-504143</identifier>
    <enrichment key="eventName">Fachtagung Bauwerksdiagnose 2020</enrichment>
    <enrichment key="eventPlace">Berlin, Germany</enrichment>
    <enrichment key="eventStart">13.02.2020</enrichment>
    <enrichment key="eventEnd">14.02.2020</enrichment>
    <licence>Creative Commons - CC BY-ND - Namensnennung - Keine Bearbeitungen 4.0 International</licence>
    <author>Tim Klewe</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Radar</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Feuchte</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Signalmerkmale</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Klassifikation</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
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    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/50414/Poster_6_Klewe.pdf</file>
  </doc>
  <doc>
    <id>59104</id>
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    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <pageLast>146</pageLast>
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    <issue/>
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    <type>doctoralthesis</type>
    <publisherName>Technische Universität Berlin</publisherName>
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    <title language="eng">Non-destructive classification of moisture deterioration in layered building floors using ground penetrating radar</title>
    <abstract language="eng">In the event of moisture deterioration, rapid detection and localization is particularly important to prevent further deterioration and costs. For building floors, the layered structure poses a challenging obstacle for most moisture measurement methods. But especially here, layer-specific information on the depth of the water is crucial for efficient and effective repairs. Ground Penetrating Radar (GPR) shows the potential to generate such depth information. Therefore, the present work investigates the suitability of GPR in combination with machine learning methods for the automated classification of the typical deterioration cases (i) dry, (ii) wet insulation, and (iii) wet screed.&#13;
First, a literature review was conducted to identify the most common methods for detecting moisture in building materials using GPR. Here, it especially became clear that all publications only investigated individual time-, amplitude- or frequency features separately, without combining them. This was seen as a potential aspect for innovation, as the multivariate application of several signal features can help to overcome individual weaknesses and limitations.&#13;
Preliminary investigations carried out on drying screed samples confirmed the profitable use of multivariate evaluations. In addition to the general suitability and dependencies of various features, first limitations due to possible interference between the direct wave and the reflection wave could be identified. This is particularly evident with thin or dry materials, for which the two-way travel times of the reflected radar signals become shorter.&#13;
An extensive laboratory experiment was carried out, for which a modular test specimen was designed to enable the variation of the material type and thickness of screed and insulation, as well as the simulation of moisture deteriorations. The data collected revealed clear differences between dry and deteriored structures within measured B-scans. These deviations were to be detected with the newly introduced B-scan features, which evaluate the statistical deviation of A-scan features within a survey line. In this way, deteriorations to unknown floor structures are recognized, regardless of the material parameters present. In a subsequent training and cross-validation process of different classifiers, accuracies of over 88 \% of the 504 recorded measurements (252 different experimental setups) were achieved. For that, the combination of amplitude and frequency features, which covered all relevant reflections of the radar signals, was particularly beneficial. Furthermore, the data set showed only small differences between dry floors and deteriored screeds for the B-scan features, which could be attributed to a homogeneous distribution of the added water in the screeds. The successfully separation of these similar feature distributions raised the suspicion of overfitting, which was examined in more detail by means of a validation with on-site data.&#13;
For this purpose, investigations were carried out at five different locations in Germany, using the identical measurement method like in the laboratory. By extracting drilling cores, it was possible to determine the deterioration case for each measurement point and thus generate a corresponding reference. However, numerous data had to be sorted out before classification, since disturbances due to underfloor heating, screed reinforcements, steel beams or missing insulation prevented comparability with the laboratory experiments. Validation of the remaining data (72 B-scans) achieved only low accuracy with 53 \% correctly classified deterioration cases. Here, the previously suspected overfitting of the small decision boundary between dry setups and deteriored screeds within the laboratory proved to be a problem. The generally larger deviations within (also dry) on-site B-scans were thus frequently misclassified as screed deterioration. In addition, there were sometimes strongly varying layer thicknesses or changing cases of deterioration within a survey line, which caused additional errors due to the local limitation of the drilling core reference. Nevertheless, individual on-site examples also showed the promising potential of the applied signal features and the GPR method in general, which partly allowed a profound interpretation of the measurements. However, this interpretation still requires the experience of trained personnel and could not be automated using machine learning with the available database. Nevertheless, such experience and knowledge can be enriched by the findings of this work, which provide the basis for further research.&#13;
Future work should aim at building an open GPR data base of on-site moisture measurements on floors to provide a meaningful basis for applying machine learning. Here, referencing is a crucial point, whose limitations with respect to the moisture present and its distribution can easily reduce the potential of such efforts. The combination of several reference methods might help to overcome such limitations. Similarly, a focus on monitoring approaches can also help to reduce numerous unknown variables in moisture measurements and increase confidence in the detection of different deterioration cases.</abstract>
    <identifier type="urn">urn:nbn:de:kobv:b43-591044</identifier>
    <identifier type="doi">10.14279/depositonce-19306</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Tim Klewe</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>NDT</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Moisture measurement</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Ground penetrating radar</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Building floor</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
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    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <thesisGrantor>Technische Universität Berlin</thesisGrantor>
    <file>https://opus4.kobv.de/opus4-bam/files/59104/klewe_tim.pdf</file>
  </doc>
  <doc>
    <id>60793</id>
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    <publishedYear>2024</publishedYear>
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    <language>eng</language>
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    <edition/>
    <issue>3</issue>
    <volume>43</volume>
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    <title language="eng">Classification of Practical Floor Moisture Damage Using GPR - Limits and Opportunities</title>
    <abstract language="eng">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).</abstract>
    <parentTitle language="eng">Journal of Nondestructive Evaluation</parentTitle>
    <identifier type="doi">10.1007/s10921-024-01111-7</identifier>
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    <author>Tim Klewe</author>
    <author>Christoph Strangfeld</author>
    <author>Tobias Ritzer</author>
    <author>Sabine Kruschwitz</author>
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      <value>GPR</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Material moisture</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Building floor</value>
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    <subject>
      <language>eng</language>
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      <value>Machine Learning</value>
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    <title language="deu">Anwendung von KI für die Materialoptimierung im Bauwesen</title>
    <abstract language="deu">Diese Arbeit untersucht den Einsatz von Künstlicher Intelligenz (KI) in der Materialentwicklung für Beton, mit einem Fokus auf Karbonatisierungswiderstand, CO₂-Reduktion und Kostenoptimierung. Das KI-Tool SLAMD, das auf inverses Materialdesign setzt, konnte in Studien die Entwicklungszeit für Betonmischungen um bis zu 80 % verkürzen. Durch die Integration zusätzlicher 1H-Kernspinresonanz-Daten wurde der Prozess um weitere 40 % beschleunigt. Insgesamt zeigt sich, dass der Einsatz von KI die Effizienz und Nachhaltigkeit in der Materialentwicklung signifikant steigern kann.</abstract>
    <parentTitle language="deu">11. Jahrestagung des DAfStb mit 63. Forschungskolloquium der BAM - Beiträge zum 63. Forschungskolloquium Green Intelligent Building am 16. und 17. Oktober 2024</parentTitle>
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    <author>Sarah Mandy Munsch</author>
    <author>Tim Klewe</author>
    <author>K. Schumacher</author>
    <author>Melissa Telong</author>
    <author>Lili Grobla</author>
    <author>C. Völker</author>
    <author>K. Yared</author>
    <author>Sabine Kruschwitz</author>
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    <title language="eng">Combining Signal Features of Ground-Penetrating Radar to Classify Moisture Damage in Layered Building Floors</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Applied Sciences</parentTitle>
    <identifier type="doi">10.3390/app11198820</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Tim Klewe</author>
    <author>Christoph Strangfeld</author>
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      <value>Civil Engineering</value>
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    <subject>
      <language>eng</language>
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      <value>Machine Learning</value>
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    <title language="eng">GPR Dataset of Moisture Measurements on Building Floors in Laboratory and On-Site</title>
    <abstract language="eng">Related work&#13;
&#13;
Laboratory Study:&#13;
Combining Signal Features of Ground-Penetrating Radar to Classify Moisture Damage in Layered Building Floors&#13;
https://doi.org/10.3390/app11198820&#13;
&#13;
On-Site Study:&#13;
TBA&#13;
&#13;
Doctoral Thesis:&#13;
Non-destructive classification of moisture deterioration in layered building floors using ground penetrating radar&#13;
https://doi.org/10.14279/depositonce-19306&#13;
&#13;
Measurement Parameters&#13;
&#13;
The GPR measurements were carried out with the SIR 20 from GSSI and a 2 GHz antenna pair (bandwidth 1 GHz to 3 GHz) in common-offset configuration. Each B-Scan consists of N A-Scans, each including 512 samples of a 11 ns time window. Survey lines were recorded with 250 A-Scans/ meter, which equals a 4 mm spacing between each A-Scan No Gains were applied. &#13;
&#13;
Folder Description:&#13;
&#13;
Lab_dry, Lab_insulDamage, Lab_screedDamage&#13;
- each contain 168 Measurements (B-Scans)  in .csv on 84 dry floors, floors with insulation damage and screed damage.&#13;
- each floor setup was measured twice on two orthogonal survey lines, indicated by _Line1_ and _Line2_ in the file name.&#13;
- the file names encode the building floor setup e.g. CT50XP100 describes a 50 mm cement screed with 100 mm extruded polystyrene below&#13;
- the material codes are&#13;
   CT: cement screed, CA: anhydrite screed, EP: expanded polystyrene, XP: extruded polystyrene, GW: glass wool, PS: perlites&#13;
&#13;
further information can be found in the publication https://doi.org/10.3390/app11198820&#13;
&#13;
OnSite_&#13;
- 5 folders containing B-Scans on 5 different practical moisture damages&#13;
- the building floor setup is encoded according to the lab with an additional measurement point numbering at the start and a damage case annotation at the end of the file name with _dry, _insulationDamage and_screedDamage&#13;
&#13;
File Description:&#13;
&#13;
B-Scans, Measurement files - no header&#13;
- dimension: 512 x N data point with N beeing the number of A-Scans including 512 samples of a 11 ns time window.&#13;
- survey lines were recorded with 250 A-Scans/ meter, which equals a 4 mm spacing between each A-Scan&#13;
&#13;
Moisture References&#13;
- Moist_Reference of On-Site Locations include the columns MeasPoint: Measurement point, wt%Screed: moisture content of screed layer in mass percent; wt%Insul: moisture content of insulation layer in mass percent. References were obtained by drilling cores with 68 mm diameter in the center of each survey line.&#13;
- Moist_Reference_Screed of Lab data include the columns Screed: Screed material and thickness in mm, wt%Screed moisture content of screed layer in mass percent&#13;
- Moist Reference_Insul of Lab data include the columns Insulation: Insulation material and thickness in mm, water addition in l: water added to the insulation layer in liters, V%Insulation: water added to the insulation layer in volume percent, RH%: resulting relative humidy in the insulation layer during measurement. These References are only avaible for Lab measurements on insulation damages.</abstract>
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    <author>Tim Klewe</author>
    <author>Christoph Strangfeld</author>
    <author>Tobias Ritzer</author>
    <author>Sabine Kruschwitz</author>
    <subject>
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      <value>GPR</value>
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    <subject>
      <language>eng</language>
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    <subject>
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      <value>Building Floors</value>
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      <value>Laboratory</value>
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  <doc>
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    <title language="deu">Multivariate Auswertung von Radarsignalen zur Bestimmung typischer Feuchteschäden in Fußböden</title>
    <abstract language="deu">Leitungswasserschäden nahmen im Jahr 2018 mit rund 2,9 Milliarden Euro den größten Posten bei Gebäudeversicherungen ein und verursachten damit mehr Kosten, als Elementar-, Feuer, Sturm- und Hagelschäden zusammen. Neutronensonden helfen hier bereits bei der horizontalen Eingrenzung eines Feuchteschadens, wobei die vertikale Unterscheidung betroffener Schichten nur durch die zerstörende Entnahme von Bohrkernen vorgenommen werden kann. Der parallele Einsatz des Radarverfahrens soll hier mit seiner hohen Sensitivität für Wasser künftig zu einem erhöhten Informationsgehalt beitragen und eine kostengünstigere Schadensdiagnose und Planung der Reparaturmaßnahmen ermöglichen. &#13;
In einem laufenden Forschungsvorhaben werden in systematischen Laborstudien gängige Schadensfälle an häufig anzutreffenden Fußbodenaufbauten simuliert und deren Einfluss auf das Radarverfahren untersucht. Die Bewertung der Schadensträchtigkeit zugeführter Wassermengen erfolgt mithilfe von Luftfeuchtesensoren, welche in der Dämmebene platziert sind. Beim Aufbau der Fußbodenstrukturen kommen Zement- und Anhydritestriche, sowie die Dämmmaterialen Styropor, Styrodur, Glaswolle und Perlite-Schüttung mit variierenden Schichtdicken zum Einsatz. Die Robustheit der zu entwickelnden Messmethode gegenüber derartig verschiedenen und in der Praxis häufig unbekannten Schichtstrukturen stellt eine besondere Herausforderung dar und soll durch den Einsatz multivariater Datenauswertung erhöht werden. Diesbezüglich empfehlen bisherige Ergebnisse die Betrachtung der örtlichen Verteilungen qualitativer Signalmerkmale wie Amplituden, Laufzeiten und Frequenzen im Radargramm (B-Scan). Deren Eignung für reale Schadensfälle außerhalb der Laborumgebung wird hierbei stets kritisch hinterfragt und soll weiterführend an Praxiseinsätzen validiert werden.</abstract>
    <enrichment key="eventName">80. Jahrestagung der Deutschen Geophysikalischen Gesellschaft</enrichment>
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    <enrichment key="eventStart">18.05.2020</enrichment>
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    <author>Tim Klewe</author>
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      <value>Radar</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Feuchte</value>
    </subject>
    <subject>
      <language>deu</language>
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      <value>Fußboden</value>
    </subject>
    <subject>
      <language>deu</language>
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      <value>Multivariate Datenauswertung</value>
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    <title language="deu">Zerstörungsfreie Lokalisierung von Flüssigwasser in Fußböden durch Kombination von Radar und Neutronensonde</title>
    <abstract language="deu">Das Neutronensondenverfahren wird bereits seit vielen Jahren erfolgreich zur Eingrenzung und Quantifizierung auftretender Feuchteschäden an Fußböden eingesetzt. Hierzu bedarf es jedoch einer Vielzahl zerstörender Sondierungsbohrungen, welche die gewonnenen Messdaten kalibrieren und eine Tiefenzuordnung des Flüssigwassers zulassen. Dadurch entsteht ein zeitlicher und finanzieller Aufwand, der durch den parallelen Einsatz des elektromagnetischen Radarverfahrens vermieden werden könnte. Mit seiner hohen Sensitivität für Wasser bietet diese Messmethode die Möglichkeit der vertikalen Lokalisierung von Feuchte, was zu einer automatisierten Klassifizierung typischer Schadensfälle beitragen soll.&#13;
In einem laufenden Forschungsvorhaben werden in systematischen Laborstudien gängige Schadensfälle an häufig anzutreffenden Fußbodenaufbauten simuliert und deren Einfluss auf die genannten Verfahren untersucht. Hierbei kommen Zement- und Anhydritestriche, sowie unterschiedliche Dämmmaterialen mit variierenden Schichtdicken zum Einsatz. Wesentlicher Bestandteil der Auswertung ist die Extraktion signifikanter Signalmerkmale des Radarverfahrens, welche Rückschlüsse auf den Schadensfall und ggf. die Wassermenge zulassen. Weiterführend sollen die Kombinationsmöglichkeiten der verschiedenen Signalmerkmale und der Neutronensondendaten durch Methoden der multivariaten Datenauswertung und des maschinellen Lernens geprüft werden. Die Unabhängigkeit gegenüber wechselnden Schichtdicken und Materialien steht hierbei besonders im Fokus und soll anhand der erzielten Ergebnisse evaluiert werden.</abstract>
    <parentTitle language="deu">10. Kolloquium Industrieböden - Fachtagung über stark beanspruchte Bodenkonstruktionen - Tagungshandbuch 2020</parentTitle>
    <identifier type="isbn">ISBN 978-3-8169-8505-1 (ePDF)</identifier>
    <identifier type="isbn">ISBN 978-3-8169-3505-6 (Print)</identifier>
    <enrichment key="eventName">10. Kolloquium Industrieböden - Fachtagung über stark beanspruchte Bodenkonstruktionen</enrichment>
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    <author>Tim Klewe</author>
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    <subject>
      <language>deu</language>
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      <value>Radar</value>
    </subject>
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      <language>deu</language>
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      <value>Feuchte</value>
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    <subject>
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      <value>Neutronensonde</value>
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    <title language="eng">Classification of moisture damage in layered building floors with GPR and neutron probe</title>
    <abstract language="eng">In 2019, 3.1 billion Euro of damage was caused by piped water, accounting for the largest share (53%) of building insurance claims in Germany. In the event of damage, the accurate determination and localization of water ingress is essential to plan for and perform efficient renovations. Neutron probes are already applied successfully on building floors to localize the source of damage and other affected areas. However, additional information about the depth of moisture penetration can only be obtained by the destructive extraction of drilling cores, which is a time- and cost-intensive procedure. With its high sensitivity to water and fast measurement procedure, Ground Penetrating Radar (GPR) can serve as a suitable extension to the neutron probe, enabling more precise characterization of common forms of moisture damage.&#13;
In this research project, we study the influence of common types of moisture damage in differing floor constructions using GPR and a neutron probe. A measurement setup with interchangeable layers is used to vary the screed material (cement or anhydrite) and insulation material (Styrofoam, Styrodur, glass wool, perlite), as well as the respective layer thickness. Every configuration is measured for the following main cases: 1) dry state; 2) with a damaged insulation layer and 3) a damaged screed layer.&#13;
The evaluation is focused on the extraction of distinctive signal features for GPR, which can be used to classify the underlying case of damage. Furthermore, possible combinations of these features are investigated using multivariate data analysis and machine learning in order to evaluate the influence of different floor constructions.&#13;
To validate the developed methods, practical measurements on real damage cases in Germany are carried out and compared to reference data obtained from drilling cores.</abstract>
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    <title language="eng">Circular B-I/O: Building Ecosystems for Bio-Based Concrete with AI-Driven Supply Streams</title>
    <abstract language="eng">Concrete is a cornerstone of modern infrastructure but is responsible for about 10% of global carbon emissions. With no large-scale alternative available, the challenge is to make concrete production more sustainable through the use of supplementary cementitious materials (SCMs) and admixtures. Depending on the region, different types of SCMs are generally conceivable– one alternative is bio-based SCMs derived from agricultural waste. This is especially important in rapidly growing regions like Sub-Saharan Africa, where cities such as Nairobi and Lagos are expected to become some of the largest in the world. Since 80% of the buildings needed by 2050 are yet to be built, these regions have a great opportunity to avoid traditional, carbon-intensive construction practices and adopt innovative, sustainable solutions.&#13;
A key challenge when using bio-based materials is the variability and seasonality of agricultural residues—such as maize cobs, rice husks, and sugarcane bagasse—which make their large-scale use in concrete difficult. To address this, the Circular B-I/O project funded by the Volkswagen Stiftung applies an AI-driven materials discovery tool to develop strategies for transforming these heterogeneous and seasonal materials into consistent and scalable supply streams. The goal is to optimize material selection, blending, and processing to integrate them seamlessly into concrete while maintaining high performance.&#13;
This contribution introduces the Circular B-I/O project, detailing its goals, methodologies, and the potential of AI in creating circular supply chains for bio-based building materials. Additionally, initial concepts and frameworks for integrating agricultural residues into concrete production will be discussed. By showcasing the project’s approach, the aim is to highlight both the challenges and opportunities of leveraging AI to make construction more sustainable.</abstract>
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    <title language="deu">KI und Robotik im Dienst der Nachhaltigkeit: Beschleunigung innovativer Lösungen im Bausektor</title>
    <abstract language="deu">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.</abstract>
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    <title language="eng">Synthetic Dataset for Sequential Learning-Based Optimisation of Bio-Ash Binder Formulations under Seasonal Availability Constraints</title>
    <abstract language="eng">This dataset accompanies the study on sequential learning–based optimisation of bio-ash–cement binder formulations under seasonally varying material availability. It provides a fully synthetic but chemically inspired benchmark design space for evaluating data-driven optimisation strategies in cementitious materials research.&#13;
&#13;
The dataset comprises 5,006 unique binder formulations, each defined by the mass fractions of cement and five bio-based ash components (A1–A5). Ash components represent generic bio-ash types derived from agricultural residues (e.g. rice husk ash, cassava peel ash), and their internal proportions are systematically varied under mass-balance constraints. Cement content ranges from 0 to 100 wt% in discrete steps.&#13;
&#13;
To reflect dynamic supply conditions, the dataset includes season-specific ash usage metrics for four seasons (S1–S4), expressing the fraction of available ash resources consumed by each formulation. A synthetic compressive strength value is assigned to every formulation using a nonlinear scoring function based on chemically inspired descriptors, with added noise to generate a structured yet non-trivial optimisation landscape. These strength values do not represent calibrated physical predictions and are intended solely as a hidden objective function for benchmarking sequential learning algorithms.&#13;
&#13;
The dataset is designed for in silico benchmarking, reproducibility studies, and methodological comparisons of optimisation and active learning strategies. It enables systematic evaluation of algorithmic performance without the need for physical experiments.</abstract>
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