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. 

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. 

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. 

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. 

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.