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Data driven design of alkali-activated concrete using sequential learning
- This paper presents a novel approach for developing sustainable building materials through Sequential Learning. Data sets with a total of 1367 formulations of different types of alkali-activated building materials, including fly ash and blast furnace slag-based concrete and their respective compressive strength and CO2-footprint, were compiled from the literature to develop and evaluate this approach. Utilizing this data, a comprehensive computational study was undertaken to evaluate the efficacy of the proposed material design methodologies, simulating laboratory conditions reflective of real-world scenarios. The results indicate a significant reduction in development time and lower research costs enabled through predictions with machine learning. This work challenges common practices in data-driven materials development for building materials. Our results show, training data required for data-driven design may be much less than commonly suggested. Further, it is more important toThis paper presents a novel approach for developing sustainable building materials through Sequential Learning. Data sets with a total of 1367 formulations of different types of alkali-activated building materials, including fly ash and blast furnace slag-based concrete and their respective compressive strength and CO2-footprint, were compiled from the literature to develop and evaluate this approach. Utilizing this data, a comprehensive computational study was undertaken to evaluate the efficacy of the proposed material design methodologies, simulating laboratory conditions reflective of real-world scenarios. The results indicate a significant reduction in development time and lower research costs enabled through predictions with machine learning. This work challenges common practices in data-driven materials development for building materials. Our results show, training data required for data-driven design may be much less than commonly suggested. Further, it is more important to establish a practical design framework than to choose more accurate models. This approach can be immediately implemented into practical applications and can be translated into significant advances in sustainable building materials development.…
Autor*innen: | Christoph VölkerORCiD, Benjami Moreno Torres, Tehseen Rug, Rafia Firdous, Ghezal Ahmad Zia, Stefan Lüders, Horacio Lisdero Scaffino, Michael Höpler, Felix Böhmer, Matthias Pfaff, Dietmar Stephan, Sabine KruschwitzORCiD |
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Dokumenttyp: | Zeitschriftenartikel |
Veröffentlichungsform: | Verlagsliteratur |
Sprache: | Englisch |
Titel des übergeordneten Werkes (Englisch): | Journal of Cleaner Production |
Jahr der Erstveröffentlichung: | 2023 |
Organisationseinheit der BAM: | 8 Zerstörungsfreie Prüfung |
8 Zerstörungsfreie Prüfung / 8.0 Abteilungsleitung und andere | |
Veröffentlichende Institution: | Bundesanstalt für Materialforschung und -prüfung (BAM) |
Verlag: | Elsevier |
Verlagsort: | Amsterdam |
Jahrgang/Band: | 418 |
Aufsatznummer: | 138221 |
Erste Seite: | 1 |
Letzte Seite: | 13 |
DDC-Klassifikation: | Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurwissenschaften und zugeordnete Tätigkeiten |
Freie Schlagwörter: | Alkali-activated building materials; Data-driven materials design; Sequential learning; Sustainable building materials |
Themenfelder/Aktivitätsfelder der BAM: | Material |
Material / Materialdesign | |
DOI: | 10.1016/j.jclepro.2023.138221 |
URN: | urn:nbn:de:kobv:b43-584376 |
URL: | https://www.sciencedirect.com/science/article/pii/S095965262302379X |
ISSN: | 0959-6526 |
ISSN: | 1879-1786 |
Verfügbarkeit des Dokuments: | Datei für die Öffentlichkeit verfügbar ("Open Access") |
Lizenz (Deutsch): | Creative Commons - CC BY - Namensnennung 4.0 International |
Datum der Freischaltung: | 26.09.2023 |
Referierte Publikation: | Ja |
Datum der Eintragung als referierte Publikation: | 26.09.2023 |
Paper des Monats: | Ja |
Schriftenreihen ohne Nummerierung: | Wissenschaftliche Artikel der BAM |