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    <publishedYear>2024</publishedYear>
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    <title language="eng">Reincarnate: Shaping a sustainable future in construction through digital innovation</title>
    <abstract language="eng">We introduce the REINCARNATE project, funded by the European Union's Horizon Europe program, to boost circularity by merging digital innovations with practical applications and a focus on material reuse. The heart of REINCARNATE is the Circular Potential Information Model (CP-IM), a digital platform designed to assess and enhance the recyclability of construction materials, construction products, and buildings. The CP-IM integrates advanced technologies such as digital twins, AI, and robotics to revolutionize the handling of construction waste, turning it into valuable resources and cutting the environmental footprint of the sector. Among its features are digital tracing, material durability predictions, and CO2 reduction materials design. These are showcased in eleven European demonstration projects, highlighting the practical benefits of these technologies in reducing construction waste and CO2 emissions by up to 80% and 70% respectively. REINCARNATE aims to marry innovation with real-world application, providing the construction industry with strategies for sustainable and circular practices.</abstract>
    <enrichment key="eventName">Rilem Spring Convention</enrichment>
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    <author>Sabine Kruschwitz</author>
    <author>Christoph Völker</author>
    <author>Ghezal Ahmad Jan Zia</author>
    <author>Benjami Moreno Torres</author>
    <author>Timo Hartmann</author>
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      <language>eng</language>
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      <value>Construction sustainability</value>
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      <language>eng</language>
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      <value>European project</value>
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      <language>eng</language>
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      <language>eng</language>
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      <value>Digital construction</value>
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  <doc>
    <id>62458</id>
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    <volume>55</volume>
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    <publisherName>Springer Nature Switzerland</publisherName>
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    <title language="eng">An Adaptive Upscaling Approach for Assessing Materials’ Circularity Potential with Non-destructive Testing (NDT)</title>
    <abstract language="eng">Advancing towards a circular economy necessitates the efficient reuse and maintenance of structural materials, which relies on accurate, non-damaging condition assessments. This paper introduces an innovative AI-driven adaptive sampling (AS) technique integrated with Non-Destructive Testing (NDT) to optimize this process. AS focuses on critical data points, reducing the amount of data needed for precise assessments—evidenced by our method requiring on average only 7 samples for Logistic Regression and 8 for Random Forest, contrasted with 29 for traditional sampling.&#13;
By reducing the necessity for extensive data collection, our method not only streamlines the assessment process but also significantly contributes to the sustainability goals of the circular economy. These goals include resource efficiency, waste reduction, and material reuse. Efficient condition assessments promote infrastructure longevity, reducing the need for new materials and the associated environmental impact.&#13;
The circular economy aims to create a sustainable system where resources are reused, and waste is minimized. This is achieved by extending the lifecycle of materials, reducing the environmental footprint, and promoting recycling and reuse. Longevity directly contributes to the circular economy by maximizing the utility and lifespan of existing materials and structures. Longer-lasting infrastructure means fewer resources are needed for repairs or replacements, leading to reduced material consumption and waste generation. This aligns with the circular economy's principles of sustainability and resource efficiency. This research not only advances the field of structural health monitoring but also aligns with the broader objective of enhancing sustainable construction practices within the circular economy framework.</abstract>
    <parentTitle language="eng">Proceedings of the RILEM Spring Convention and Conference 2024</parentTitle>
    <identifier type="issn">2211-0844</identifier>
    <identifier type="isbn">978-3-031-70277-8</identifier>
    <identifier type="doi">10.1007/978-3-031-70277-8_38</identifier>
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    <author>Ghezal Ahmad Jan Zia</author>
    <author>Christoph Völker</author>
    <author>Benjami Moreno Torres</author>
    <author>Sabine Kruschwitz</author>
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      <language>eng</language>
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      <value>Adaptive Sampling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Random Sampling</value>
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    <subject>
      <language>eng</language>
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      <value>Machine Learning</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Non-Destructive Testing</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Condition Assessment</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Circular Economy</value>
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  <doc>
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    <publishedYear>2024</publishedYear>
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    <title language="eng">REINCARNATE: Shaping a Sustainable Future in Construction Through Digital Innovation</title>
    <abstract language="eng">We introduce the REINCARNATE project, funded by the European Union’s Horizon Europe program, to boost circularity by merging digital innovations with practical applications and a focus on material reuse. The heart of REINCARNATE is the Circular Potential Information Model (CP-IM), a digital platform designed to assess and enhance the recyclability of construction materials, construction products, and buildings. The CP-IM integrates advanced technologies such as digital twins, AI, and robotics to revolutionize the handling of construction waste, turning it into valuable resources and cutting the environmental footprint of the sector. Among its features are digital tracing, material durability predictions, and CO2 reduction materials design. These are showcased in eleven European demonstration projects, highlighting the practical benefits of these technologies in reducing construction waste and CO2 emissions by up to 80% and 70%  respectively. REINCARNATE aims to marry innovation with real-world application, providing the construction industry with strategies for sustainable and circular practices.</abstract>
    <parentTitle language="eng">Proceedings of the RILEM Spring Convention and Conference 2024</parentTitle>
    <identifier type="issn">2211-0844</identifier>
    <identifier type="isbn">978-3-031-70280-8</identifier>
    <identifier type="doi">10.1007/978-3-031-70281-5_32</identifier>
    <identifier type="issn">2211-0852</identifier>
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    <identifier type="isbn">978-3-031-70283-9</identifier>
    <note>Serientitel: RILEM Bookseries – Series title: RILEM Bookseries</note>
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    <enrichment key="eventPlace">Milano, Italy</enrichment>
    <enrichment key="eventEnd">12.04.2024</enrichment>
    <enrichment key="eventStart">07.04.2024</enrichment>
    <enrichment key="eventName">4 RILEM Spring Convention and Conference on  advanced construction materials and processes for a carbon neutral  society 2024</enrichment>
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    <author>Sabine Kruschwitz</author>
    <author>Christoph Völker</author>
    <author>Ghezal Ahmad Jan Zia</author>
    <author>Benjami Moreno Torres</author>
    <author>Timo Hartmann</author>
    <subject>
      <language>eng</language>
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      <value>Digital construction</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Construction sustainability</value>
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    <subject>
      <language>eng</language>
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      <value>European project</value>
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      <language>eng</language>
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      <value>Llife cycle</value>
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      <language>eng</language>
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      <value>Recycled materials</value>
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    <id>62459</id>
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    <title language="eng">Leveraging large language models for automated knowledge graphs generation in non-destructive testing</title>
    <abstract language="eng">This paper presents an innovative approach for the automatic generation of Knowledge Graphs (KGs) from heterogeneous scientific articles in the domain of Non-Destructive Testing (NDT) applied to building materials.&#13;
&#13;
Our methodology leverages large language models (LLMs) to extract and semantically relate concepts from diverse sources. We developed material-specific agents for concrete, wood, steel, and bricks, each equipped with a curated glossary of terms to ensure domain accuracy. These agents process PDF documents, extracting relevant information on deterioration mechanisms, physical changes, and applicable NDT methods. The extracted data is then normalized, validated, and structured into a Neo4j graph database, forming a comprehensive KG. Our results demonstrate the system’s ability to automatically discover and represent intricate relationships between materials, deterioration mechanisms, physical changes, and NDT techniques. The generated KG successfully captures complex interactions, such as the applicability of specific NDT methods to various materials under different deterioration conditions. This work not only highlights the potential of KGs in enhancing knowledge discovery and representation in NDT research but also provides a scalable framework for extending this approach to other scientific domains.</abstract>
    <parentTitle language="eng">SeMatS 2024 - Proceedings of the first international workshop on semantic materials science: Harnessing the power of semantic web technologies in materials science</parentTitle>
    <identifier type="issn">1613-0073</identifier>
    <identifier type="doi">10.5281/zenodo.13834164</identifier>
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    <note>Serientitel: CEUR workshop proceedings – Series title: CEUR workshop proceedings</note>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Ghezal Ahmad Jan Zia</author>
    <author>Andre Valdestilhas</author>
    <author>Benjami Moreno Torres</author>
    <author>Sabine Kruschwitz</author>
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      <language>eng</language>
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      <value>Materials science and engineering</value>
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      <language>eng</language>
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      <language>eng</language>
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      <value>Linked open data</value>
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      <language>eng</language>
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      <language>eng</language>
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      <value>RDF</value>
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      <language>eng</language>
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      <value>Semantic web</value>
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    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
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