The RAW project introduces a transformative resource model for the architecture, engineering, and construction (AEC) sector, focusing on utilizing waste-sourced and fast-growing bio-based materials. By accounting for the inherent variability in material quality and availability due to climatic and local ecological factors, the model expands the range of usable bio-materials.
Towards the RAW resource model
The RAW New Resource Model establishes a cutting-edge digital infrastructure linking material sourcing, computational design, and fabrication. This model is adaptable, functioning across various bio-based material classes and scales. Innovations include developing machine learning techniques for non-destructive characterization of bio-material properties at the level of individual elements and small batches. This allows for precise descriptions of variability within bio-material streams and detailed analysis of the mechanical properties of specific batches or pieces. The project specifically focuses on three emerging material streams—reclaimed timber from southern Norway, biopolymer composites from Danish agricultural waste, and fast-growing hemp fibers from the Alpine region. By exploring these materials, RAW aims to address their specific variabilities and derive broader solutions for pressing questions in the field. These case studies provide crucial data to evaluate the environmental and social impacts of the technologies developed, paving the way for co-designed digital transformation and circularity strategies in material sourcing and the broader AEC industry.
Diagram
Reclaimed Timber and Timber Composites are becoming vital for complex building applications. Traditionally, high-volume timber production relies on uniform timber grades. However, previous consortium projects have demonstrated the potential of using the unique properties of individual timber pieces to manage the hygroscopic behavior of building elements or to broaden the range of wood qualities suitable for timber beams. This approach strategically places materials based on local performance requirements, allowing for the inclusion of large quantities of low-quality timber typically discarded or burned.
A key obstacle is the accurate identification of individual timber qualities through nondestructive technologies. Traditional grading often categorizes timber into broad classes with significant variations in properties, primarily focusing on strength due to its high unpredictability, rather than stiffness, which is more crucial for timber construction. In response, RAW is developing methods to utilize reclaimed timber for innovative Glue Laminated Timber (GLT) assemblies. These methods leverage specific material data obtained via non-destructive testing and incorporate performance predictions from our resource-driven, non-prescriptive design model to optimize the use of diverse timber grades effectively.
Photographer: Wendy Wuyts, Omtre
Photographer: Rose-Ann Melis
Among the diverse group of fast-growing fibers, flax and hemp stand out as successful raw materials for creating composite semi-finished products and yarns. Hemp, in particular, is one of the fastest-growing plants globally, capable of reaching heights of 4 meters within just 100 days. It is also highly efficient at sequestering carbon, with a single hectare of hemp able to absorb between 8 to 22 tonnes of CO2 annually. The quality of the extracted hemp fiber is influenced by several factors including the cultivation location and type, variety of hemp, timing of the harvest, processing methods, and the weather conditions throughout the growing season.
Hemp has several advantages over flax; it is simpler to cultivate organically, yields more biomass per hectare, and does not compete with food production—instead, it produces both nutritious seeds and fibers. Given its widespread cultivation across Europe, there is significant variability in hemp's raw material quality, which is further influenced by farming practices, particularly in pesticide-free and fertilizer-free organic agriculture. This variability impacts not only the fiber's quality but also its availability, as hemp cannot be repeatedly grown on the same plot each year without degrading the soil.
Traditional methods like single fiber tensile testing in a dry state often fail to provide accurate results due to the high variance in fiber characteristics, as demonstrated by preliminary experiments by UIBK in the Alpenhanf 360° network. Conversely, tensile testing and advanced imaging techniques such as CT and X-ray scans of embedded fibers (in composites or yarn) have produced more reliable data, allowing these characteristics to be effectively correlated with initial growth and processing conditions.
Fiber winding systems, which create composites from fibers and binders or rely solely on fiber-friction mechanisms, are highly efficient in resource utilization. Advances in computational design and fabrication have transformed traditional homogeneous textile systems into additively fabricated, graded systems. This innovation allows for variations in fiber density, type, and interfaces to be tailored to local requirements for optimal performance.
Photographer: Rose-Ann Melis
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Agricultural Waste - Biopolymer 3D Printing. Biopolymer composites, derived from biodegradable waste materials, are being explored for their potential in robotic 3D printing for architectural uses, as demonstrated in prior work by the applicants. Although multi-material printheads can blend different biopolymer mixes during extrusion, the performance of these mixes greatly depends on the consistency of the source materials, typically limiting use to highly refined inputs from food (like collagen) or agricultural products (like fibers).
To overcome this challenge, RAW is developing a 3D printing system designed to accommodate the variability of waste-sourced biopolymers. This system will feature continuous in-line monitoring to assess the rheological performance of the feed mixture, enabling adjustments in real-time to ensure optimal printing outcomes.
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From a Material characterisation model to Design and fabrication models
The primary challenge in current construction practices is the inability to accommodate material variability, especially with bio-based materials. Traditional methods rely on materials being homogeneous, stable, and consistent, which suits mass-production but does not account for the natural diversity in bio-based materials like timber. These materials vary in strength, durability, and quality due to environmental factors such as growth cycles, soil composition, and weather conditions, leading to a significant amount of biomass being deemed unsuitable for construction.
For example, timber is sorted into grades based on characteristics often established pre-digitally, focusing more on appearance than actual material properties. This results in increased use of safety factors for bio-based materials in the architecture, engineering, and construction (AEC) sector, which in turn raises material consumption and costs. Moreover, traditional methods of material characterization are limited and fail to assess the full range of bio-based materials, including alternative types.
To address these issues, RAW is developing innovative digital techniques that recognize and utilize the inherent variability of bio-based materials. These new methods enable precise, batch-specific characterization, integrating this data directly into design, analysis, specification, and fabrication processes. This approach allows for the optimization of building components through non-destructive testing methods and the strategic deployment of material behaviors in advanced fabrication processes, paving the way for more efficient and sustainable construction practices.
Building a Semantic Data model creating connections between separate domains
Recent projects by members of this consortium have moved beyond mere geometric concerns to explore how measured material properties can be integrated directly into design goals. This involves utilizing data from advanced imaging techniques like CT-Scans and vision-based methods to consider timber qualities and fiber orientations. These innovations use matchmaking algorithms to strategically allocate materials in building components to meet performance objectives. Although these approaches demonstrate the potential for integrating material variability into design processes, they still largely depend on virgin resources and lack a comprehensive framework that fully accounts for their impact on future resource streams and CO2 emissions.
To address these limitations, RAW has developed a Resource-driven Non-Prescriptive Design System. This system enables the AEC sector to incorporate the variability of bio-based resources throughout the design and fabrication stages. Our focus is on two major innovations: 1) the Joint Semantic Data Model, which bridges the gap between resource data and material data models in AEC, creating a structured digital framework for material-related information, and 2) the Resource-Driven Non-Prescriptive Design Model, a novel computational design approach that offers design targets and scenarios instead of fixed geometry and fabrication instructions.
The Joint Semantic Data Model abandons traditional, rigid interoperability models used in BIM, like the IFC format, and adopts a semantic web approach. This allows us to develop an interdisciplinary semantic data model capable of capturing the variability inherent in bio-based materials, updating and enriching information from material sourcing to fabrication. By extending ontology features such as data hierarchy, taxonomic flexibility, and attribute variability, RAW not only represents various material characterizations but also understands their significance and variability. This model is built using RDF, making it interoperable and compatible with web technologies and AI, which enhances its capacity to integrate data across disciplines and adapt to material variability in real-time during fabrication phases.
Material flows and whole life cycle thinking
Given the uncertainties around digital innovations and the developing alternative bio-based resource flows, a forward-looking and comprehensive sustainability assessment is crucial for facilitating the bio-based transition in the architecture, engineering, and construction (AEC) sector. To effectively support decarbonization efforts, it is essential to adopt a whole life cycle thinking approach. This perspective ensures that solutions and technologies not only address immediate needs but also contribute positively throughout their entire lifespan, from resource extraction and material production to usage and eventual disposal or recycling.
RAW is set to enhance the current methodologies by integrating ex-ante life cycle assessment (LCA) with advanced dynamic material flow analysis (MFA). This combination will provide a more sophisticated prospective analysis that surpasses existing practices. By doing so, we aim to ensure that our digital and material innovations genuinely lead to a reduction in carbon emissions across the full life cycle of building materials.
Moreover, RAW employs a procedural scenario-making approach grounded in Transdisciplinary Learning. This approach is structured around the four levels of society’s metabolism: product/process, process cluster, life cycle/material cycle, and the economy-wide level as outlined by Pauliuk. This structured approach allows us to systematically evaluate how innovations in the AEC sector can contribute to sustainable building practices that are essential for long-term environmental and economic health.
Ecosystem exploitation approach
We will employ a combination of the theoretical technological innovation system framework and the methodological ZIP analysis (with Z for zoom, I for intervention and P for painpoint) to elucidate which digital technologies, processes, and interventions are optimally suited to address the specific challenges within the ecosystem that our RAW technology aims to transform. This helps us to list recommendations to policy makers and officers of the European Innovation Council and other stakeholders who can help to remove pain points hindering the acceptance of our technologies and other solutions.
Speculative approaches to understand the impact of emerging technologies
We employ speculative approaches to enhance our prospective impact assessments, particularly in scenarios where life cycle assessments of existing construction products and technologies are hindered by data unavailability. Given the RAW project's focus on pioneering digital technologies for materials not yet produced on an industrial scale, we rely heavily on imaginative speculation, drawing on diverse expert insights to inform and validate our scenarios. Our main method is Science Fiction Prototyping.
Coordinator: Martin Tamke, martin.tamke@kglakademi.dk
Media: Wendy Wuyts, wendy@omtre.no
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Funded by the European Union (Project Number 101161441). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Innovation Council (EIC). Neither the European Union nor the granting authority can be held responsible for them.