Journal: Technology | Architecture + Design

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Abbreviation

Publisher

Taylor & Francis

Journal Volumes

ISSN

2475-143X
2475-1448

Description

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Publications 1 - 5 of 5
  • Simonen, Kathrina; Rodriguez Droguett, Barbara; De Wolf, Catherine (2017)
    Technology | Architecture + Design
    Greenhouse gas emissions from extracting and manufacturing building materials, often termed “embodied carbon,” are produced before buildings are occupied and are more critical to meeting global climate targets than commonly assumed. In order to motivate reductions in embodied carbon, we need better data and established benchmarks. Although Life Cycle Assessment (LCA) methods have been used to analyze individual buildings, there has not been an agreed-upon understanding of the order magnitude and range of variation of the embodied carbon of buildings. In order to address this knowledge gap, the largest known database of building embodied carbon was compiled, normalized, and analyzed. In addition to establishing the range of embodied carbon values, this research identified sources of uncertainty and proposed strategies to advance embodied carbon benchmarking practice.
  • Burger, Joris Jan; Lloret-Fritschi, Ena; Akermann, Marc; et al. (2023)
    Technology | Architecture + Design
    Concrete construction is one of the largest producers of CO2 emissions and waste from discarded formwork. 3D printing of formwork using polymer extrusion 3D printing can increase the sustainability of concrete construction by allowing the fabrication of optimized geometry. However, polymer extrusion printed formwork must be discarded after being used several times. Therefore, this paper explores the potential of recycling 3D printed formwork. We describe a workflow in which a formwork is 3D printed, filled with concrete, removed, recycled, and reprinted into a new formwork. Two case studies are presented: filament-printed PET-G formwork for a concrete column, and pellet-printed PIPG formwork for a series of columns. The results indicate that the printing material can be fully recycled for at least one cycle.
  • Graser, Konrad; Adel, Arash; Baur, Marco; et al. (2021)
    Technology | Architecture + Design
  • Schlüter, Arno (2023)
    Technology | Architecture + Design
  • Önalan, Beril; Triantafyllidis, Eleftherios; Mitropoulou, Ioanna; et al. (2025)
    Technology | Architecture + Design
    This paper introduces a computational approach to automate the reuse of concrete cutting waste in architectural elements during the early design phase. Prior research typically focuses on geometric matching, neglecting crucial performance objectives such as stability and environmental impact. We address this gap with a deep learning-based workflow. We used a deep neural network as a surrogate model to predict performance metrics for designs from a concrete waste inventory to facilitate performance-based design. Demonstrated through the design of a partitioning wall, our method shows high predictive accuracy, effectively predicting outcomes across diverse design scenarios while respecting material constraints. These findings underscore the potential of data-driven strategies to improve the scalability and efficiency of circular design by reducing the computational time required for performance evaluations.
Publications 1 - 5 of 5