A 5D Digital Circular Workflow: Digital Transformation Towards Matchmaking of Environmentally Sustainable Building Materials through Reuse from Disassembly

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Byers, Deepika Raghu, Matthew Gordon, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4349460/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract The intersection of digital transformation, circular economy, and innovative construction practices presents a nascent field with significant potential to mitigate environmental impacts through optimised material reuse. This research aims to understand how digital technologies can augment the reuse of construction materials. We evaluated a plethora of digital tools, including digital product passports, artificial intelligence (AI)-assisted material classification, reality capture, computational and generative AI-aided design, digital fabrication techniques, and blockchain technology, for their efficacy in facilitating building material reuse. The practical component of the study involved disassembling buildings and then designing and executing a construction project reusing the salvaged materials. Findings demonstrate that the successful application of circular economy principles is facilitated by digital forms of cataloguing, inventory management, design, and construction. The research proposes a workflow for incorporating digital innovations into circular construction, suggesting a pathway for future implementation and scalability. LiDAR scanning photogrammetry scan-to-Building Information Modeling (BIM) computer vision extended reality (XR) Digital Product Passports (DPP) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Introduction New digital methods – such as Building Information Modelling (BIM), computational design, big data, robotics, Computer-Aided Manufacturing (CAM), and Artificial Intelligence (AI) – have the potential to move the entire Architecture, Engineering and Construction (AEC) sector towards a circular model more rapidly. The challenge for all stakeholders of the AEC sector is to respond to global housing needs while reducing environmental impact. Yet, given that the building sector contributes 37% of greenhouse gas emissions, this is no easy task [ 1 ]. Additionally, construction is responsible for 50% of all material consumption and more than a third of all solid waste [ 2 ]. One of the main reasons for this is that we construct our buildings following a linear model – we extract, produce, use, and dispose of building materials and resources. A circular model is needed whereby we would derive maximum value from building materials by reusing them at the end of their service life as new resources. A “circular economy” [ 3 ], [ 4 ], [ 5 ], [ 6 ] promotes a regenerative system where repair, reuse, recycling, and energy recovery minimise resource use and emissions [ 7 ]. More specifically, the reuse of materials in the built environment is essential to tackle challenges such as the scarcity of resources, waste treatment, and the climate crisis. Indeed, the Intergovernmental Panel on Climate Change (IPCC) encourages the reuse of materials as a near-term response to climate change [ 8 ]. Similarly, the United Nations Environment Programme (UNEP) has launched an initiative to transform the building sector towards near-zero emissions and resilience by 2030, underscoring the need for sustainable building practices globally [ 9 ]. Implementing circular principles benefits the environment, economy, and society [ 10 ], [ 11 ], [ 12 ], [ 13 ], [ 14 ], [ 15 ], [ 16 ], [ 17 ], [ 18 ]. While pioneering circular building projects [ 19 ], [ 20 ], [ 21 ], [ 22 ] and innovative reuse platforms and marketplaces construction [ 23 ], [ 24 ], [ 25 ], [ 26 ], [ 27 ], [ 28 ] [ 29 ] have shown promise in localised settings, global implementation on a large scale has not yet been successful. Material recovery remains labour-intensive, with experts manually measuring and cataloguing data on buildings set for demolition, as well as designing and assembling new projects with these reclaimed materials. Building projects are often complex, multi-variant problems addressed by numerous parties working in separate silos. As a result, to the best of the authors’ knowledge, there is not yet a feasible circular design strategy that can be broadly applied in construction practice. Digital transformation can fill this gap by streamlining these processes and enhancing efficiency, thereby expanding circular design practices in the construction industry. The construction industry, which accounts for 13% of the world’s GDP, remains the least digitised sector due to its fragmentation and risk aversion, thus struggling to attract digital talent and impeding its adoption of digital technologies [ 30 ], [ 31 ]. Therefore, a viable digital circular construction management system needs to be developed. Upscaling current initiatives of circular construction globally could be achieved more rapidly by embracing digitalisation. This paper proposes a 5D Digital Circular Workflow that assesses existing buildings for their materials’ reuse in deconstruction, design, and construction using digital technologies (Table 1 ) with different steps validated by their application to test cases. Research in circular building practices [ 32 ] highlights challenges that digital technologies can help overcome. Frameworks for digital technologies towards circular construction have been developed for sustainable construction using specific materials, such as upcycled wood [ 33 ], or through literature reviews [ 32 ], [ 34 ]. Yet, entire workflows of digital innovations for material reuse have rarely been integrated and validated on a full building scale. Our research focuses on case studies that analyse how digital technologies can facilitate deconstruction for reuse of materials in construction. Table 1 shows our proposed workflow for these cases, with digital tools integrated in each step: Step one – Detection : Use urban data combined with Computer Vision (CV) Machine Learning (ML) algorithms to identify sites sutiable for material reuse and incorporate these materials into BIM systems. Step two – Disassembly : Catalog materials extensively, employing reality capture, scan-to-BIM and CV to enable robotics and extended reality (XR) for disassembly. Step three – Distribution : Create digital product passports to efficiently track, trace, and trade materials from demolition to new construction sites. Step four – Design : Apply generative AI and computational design algorithms to create and match designs with reclaimed materials. Step five – Deployment : Use subtractive and additive manufacturing to integrate bespoke reclaimed elements, assembling them in new constructions with XR techniques. Ongoing research on ML and CV has made great advances in spatial and temporal mapping of existing public housing stocks on the urban scale [ 35 ], [ 36 ]. These advancements have enhanced the interoperability of data representations, allowing different systems (e.g. Geographical Information System (GIS), BIM, construction management software) to effectively exchange and use information. They also provide scalability, enabling the application of these technologies beyond specific sites to broader urban areas across neighbourhoods or entire cities. Additionally, these technologies facilitate the efficient matching of available material resources (supply) with the material needs for new construction that incorporate reused materials (demand). ML, particularly through deep learning, using neural networks modeled after the human brain [ 37 ], [ 38 ], [ 39 ] has been used extensivelyin other sectors to process data and recognize patterns. Recently, this technology has been applied in the construction industry to process large, unstructurted datasets such as building demolition records. Practitioners in the demolition industry use deep learning methods to forecast the amount of salvage and waste materials obtainable at the end-of-life of buildings [ 40 ], aiding in the planning of cost efficient processes for construction waste management. CV, which trains computers to interpret the visual world, is also increasingly being applied to construction sites to enhance operations, such as building inspections. For example,industry projects such as Spotr.ai [ 41 ] and AeroScan [ 42 ] use imagery from various sources such as unmanned aerial vehicles (UAV), satellite imagery, or Google Street View to assess buildings at an urban scale. Additionally, imagery available from diverse sources such as social media, public webcams , and capturing cars or drones [ 43 ] are also used for semantic and dynamic city modelling. To harness the full potential of this technology in facilitating circular construction, the integration of global datasets, including cadastral information and data extracted from imagery must be explored to enable circular strategies such as the forecasting of building materials that could be available for reuse. The digitisation of buildings through scan-to-BIM has enabled cataloguing materials for their further reuse. In new construction, an increasing number of architecture and engineering industries are using BIM to store information in three-dimensional (3D) models of their building projects, e.g., the material types, schedule, and cost [ 30 ], [ 44 ]. Therefore, BIM could also be used to create a database of materials for the building project, i.e., a DPP (similar to a material passport in the AEC industry) [ 45 ] capturing information about the type, configuration, volume, and location of materials. However, most existing buildings that will be demolished in the next few decades do not have a ready-made BIM model available. Therefore, there is a need to digitise information into a BIM model , which can be facilitated by scanning the existing building. Processing point clouds remains difficult due to their unstructured, irregular form [ 46 ]. Techniques such as PointNet, adapted from 2D image classification, now facilitate semantic analysis and are used in Scan-to-BIM methods for building elements [ 47 ], [ 48 ], [ 49 ]. However, these techniques often target specific components like structural steel or scaffolding [ 50 ]. CV has also been used on the material scale, for instance, to detect damage such as concrete cracks, steel corrosion, and steel delamination [ 51 ]. Methods using precise data capture have been developed for the large-scale reuse of concrete as dry masonry with minimal shaping [ 52 ]. In addition, support-vector machine (SVM)-based systems has also been developed to help classify building materials, aiding in automated digital reconstruction for progress monitoring [ 53 ], although it is currently focused on individual classifications and has not been integrated with 3D reconstruction techniques. Despite these advances, there are several challenges in bringing CV to construction environments, which tend to be relatively disorderly. Modern CV systems often mistakenly put greater attention on background details. Additionlly, an active building or demolition site tends to contain many conditions abrsent in carefully constructed training sets [ 54 ]. These challenges need to be tackled with techniques such as background randomisation in training sets, selective masking by depth during analysis to focus only on relevant elements, and an in-depth analysis of the common misclassifications This would help enable the system to effectively recognize and adapt to patterns of materials commonly found adjacent to each other, enhancing its accuracy and reliability. For distribution, existing platforms [ 23 ] are designed to capture data about the quantity, quality, location, financial value, and circular utility of materials available for reuse. Increaing efforts from research aim to link material platforms to BIM platforms and integrate product tracking and DPPs into these platforms [ 32 ], [ 55 ]. Blockchain technology is now explored for its potential to enable decentralized data management and enhance transparency and traceability in circular construction [ 56 ], [ 57 ], [ 58 ]. While Madaster employs BIM for MPs without blockchain, Excess Material Exchange (EME) [ 59 ] plans to use blockchain for supply chain tracking. Digital platforms commodify buildings as material banks, making building elements tradable and enabling organisations to meet market demands efficiently through economies of scale and scope, as seen in the BoKlok concept by Skanska and IKEA [ 60 ]. The Reflow project exemplifies such a system, connecting economic actors digitally on a large scale [ 61 ]. Successful platforms (e.g., Amazon) have shown that growth depends on increasing both supply and demand users [ 30 ]. Circular platforms should act as catalysts, linking those dealing with building disposal to those starting new constructions. Leveraging digital technology, especially AI algorithms similar to dating app algorithms [ 62 ], could enhance matchmaking in the construction industry. Unlike one-to-one matching systems (e.g. dating apps), a matchmaking service for the reuse of building materials should focus on a many-to-many relationship between reusable building components and potential new constructions, while considering factors like timing, permits, and material characteristics [ 63 ]. The tracking and tracing of materials in large material databases would enable the available building materials to be matched for resource allocation. For the design, the potential of unleashing co-creativity between humans and generative AI [ 64 ] is particularly promising, especially in creative processes, whereby these exhibit promising implementations [ 65 ], [ 66 ], [ 67 ]. Generative AI has the potential to enhance early-phase circular design processes with advanced data management capabilities. A critical component of this shift is handling extensive digital databases that catalogue dismantled building components, allowing architects to effectively match these materials with new design projects. Leveraging AI, specifically through ML techniques and applying match-making algorithms, can further streamline this process and foster an environment where AI augments human creativity in generating innovative design solutions. Generative AI is a subfield of ML and a form of DL, which, in addition, uses parts of Natural Language Processing (NLP) for working with natural text in, for instance, Text-to-Image Generators, which were used in this paper (Fig. 1 ). The next stage would be to optimise the design to match the aims and the salvaged materials, through computational design tools such as parametric design space exploration and rule- or grammar-based design approaches. Given a design space, many methods for optimisation are possible, based on genetic algorithms, flocking behaviours or parameter vector manipulation [ 68 ], [ 69 ]. Two matching approaches have been studied: (i) a bottom-up approach, similar to building with blocks, initiated with the available objects, which are then algorithmically aggregated into architectural assemblies [ 70 ], [ 71 ], [ 72 ], (ii) a top-down approach commencing with a target design and then searching the inventory algorithmically, selecting the best fits for the design [ 73 ]. Evaluation of these designs includes multiple factors, covering feasibility, costs, and impact, and automating these requires a multi-objective optimisation approach [ 74 ]. Design workflows must still be developed to handle truly diverse material stocks, with shorter processing times, guaranteeing the best match. For deployment, digital fabrication , a combination of computer-aided design (CAD) data, computer-aided manufacturing (CAM) software, and computer numerical controlled (CNC) hardware, has been increasingly explored in the construction industry to produce rapid prototypes, complex elements, and to perform tasks that are repetitive, dangerous, or require precision [ 75 ]. In a circular built environment, digital fabrication can be used to design complex connections [ 76 ], [ 77 ] to make new buildings easier to disassemble [ 78 ]. Moreover, XR tools have emerged as an immersive and interactive asset both in the AEC industry and in the broader context of human-centered applications [ 79 ], [ 80 ], [ 81 ]. These advancements can benefit the AEC industry and in particular dis- and re-assembly processes. The planning logistics of disassembling existing buildings are hard to align with the construction of new ones. Consequently, the end-of-life of buildings need to be connected with the start-of-life of other buildings, making the reuse of building materials more effective, user-friendly, and widespread, by, for example, minimising storage times. Existing platforms list materials available for reuse, but in practice, the major challenge is to find the right match. Companies are struggling to find (i) architects who design with reused materials; (ii) construction sites in which these fit; (iii) contractors with reuse skills; (iv) facility managers who can store and process reused materials; and (v) certifiers who can inspect and warranty the performance of reused materials. The creative challenge shifts from original fabrication to adaptation to existing resources, introducing additional complexity to an already intricate field. Table 1 also indicates the case studies used to validate the developed 5D Digital Circular Workflow. One case study is the Zurich City, for which CV and ML was used to document building materials and predict material stocks. Another set of studies focused on the disassembly of a Geneva warehouse and a Zurich music pavilion, where automated material cataloging and sorting technologies were tested to optimise the reuse process. Additionally, full-scale applications involved the disassembly of these buildings, followed by the design and assembly of Dome 5.1 and Dome 5.2. Emerging technologies were further explored in Domes 5.x, using generative AI and XR to innovate design and improve structural assembly processes. By taking an action research approach we prototype digital circular techniques on real-world sites. The problem this research aims to address is the need for upscaling sustainable building practices by integrating digital technologies into the circular economy, enhancing material reuse in the construction industry. The action plan employed is to test and improve the technical setup, accuracy, and applicability of our 5D Digital Circular Workflow on different case studies to enhance circular strategies with emerging technologies. By implementing this workflow in real-world deconstruction and construction scenarios, our contribution lies in developing effective strategies for scalable, globally applicable circular building practices. This research aims to strengthen on- and off-site collaboration across the entire value chain towards a circular, low-carbon, zero-waste built environment. Results 5D Digital Circular Workflow Through the case studies, we developed a 5D Digital Circular Workflow (Fig. 2 ) to match the supply and demand of reused materials. To do so, construction stakeholders need to know who has waste materials to offer, where they can store them, and who wants to turn them into resources. Our research demonstrated that the collected material information could link to Swiss material marketplaces for broader distribution. By developing the digital foundations for this matchmaking, the research project contributes to the upscaling of circular economy principles within the built environment. Table 2 summarises the digital technologies which were tested in our case studies to develop the workflow. Table 2 Main digital technologies explored in the case studies for developing the 5D Digital Circular Workflow, use and predominant step they were used in throughout our case studies Digital Technologies Use Steps Machine Learning (ML) To conduct comprehensive assessments of existing building stocks from building records, in combination with geographical information systems (GIS) and assess the identified stocks to accurately estimate the potential for reusing building components Detection Computer Vision (CV) To advance material recognition from visual data and automate the disassembly-for-reuse process – identifying material types and conditions during deconstruction for precise classification Detection Disassembly Reality capture To generate 3D geometric data of existing materials, integrating this information with BIM systems as cyber-physical elements– in combination with robotics, these technologies enable systematic deconstruction and sorting processes to facilitate the careful disassembly of building materials Detection Disassembly Extended Reality (XR) To aid in the dis- and reassembly of materials, simplifying the process and ensuring accuracy in fitting reclaimed components – robotics are also explored to disassemble building elements carefully Disassembly Deployment Digital Product Passports (DPPs) To extract data to feed into specialised algorithms tailored for the construction industry to effectively match the supply of available materials with demand, serving as digital intermediaries for stakeholders across the value chain Distribution Track & trace technologies (including Internet of Things (IoT) & data carriers) To track information on materials to connect DPPs and material databases Distribution Decentralised storage technologies (e.g., blockchain) To trace the history of the material and information providence Distribution Generative AI To enhance creativity in the architectural design process, optimising the use of available reused materials Design Computational design algorithms (e.g., parametric design) To plan and model buildings specifically using reused materials. Algorithms are improved to accommodate existing material inventories while factoring in the variances necessary for working with reclaimed stock Design Digital Fabrication (e.g. additive and subtractive manufacturing) To produce precise connectors that facilitate the integration of reused materials Disassembly Deployment Step one: Detection Learnings from our exploration of the city of Zurich demonstrated how to use a data-centric approach to harness urban data sources, such as Google Street View, cadastral records, and diverse photography. Before any demolition activity, buildings should be classified as as vast repositories of reusable materials. CV and ML algorithms can identify materials in existing buildings, helping to catalogue elements that will be available for reuse. Advanced scanning technologies aid in generating 3D representations, which can be used in combination with the digitised information and further integrated into BIM systems. Existing cadastral information and other public record data (e.g., Open Data Zurich) form a foundational base for the creation of building classification maps outlining material-specific characteristics. Building imagery downloaded from the Google Street View API, with associated metadata, can be used in combination with CV and ML to understand the existing material stock that needs to be dismantled or renovated. Such tasks are traditionally labour-intensive if pursued without automated methods. Therefore, algorithms are developed to enrich building databases, amalgamating data collected from images, public records and cadasters. By establishing the composition and condition of materials in urban structures, resource optimisation strategies can be developed to minimise waste generation and foster circular economy practices. The CV detection method can be adapted to analyse real-time images or digital feeds from building owners, contractors, or even citizens. This method can improve the database by incorporating a broader and more current range of materials and conditions, thereby increasing the model’s accuracy and usefulness. This enhanced spatial and temporal understanding of material concentrations (i.e., insights into where specific materials are most abundant) can be a valuable strategic tool for urban planners pursuing a circular economy, by aiding in targeted deconstruction or renovation efforts (Fig. 3 ). Step two: Disassembly Learnings from disassembly sites in Geneva and Zurich, Switzerland, demonstrated how to further catalogue materials into an expansive database with scan-to-BIM and CV tools and how to document geometries and material specifications so that robotics and XR can be used to assist in dissassembling the materials marked for further reuse. In typical deconstruction projects, only 1% of the building materials are usually reclaimed [ 19 ]. The disassembly of building materials for reuse has several stages: (a) the dismantling of the building on-site; (b) the sorting of the materials on- and off-site; (c) the cataloguing of the materials in a database that can be used for further distribution; and (d) the precise reshaping of materials to adapt to new construction. For each of these stages, we explored how digital technologies can support systemised building disassembly to make the process less dangerous, cheaper, more efficient, and healthier than conventional demolition practices. Capture systems for spatial data vary in technology and user interaction, and these include photogrammetry with software like Agisoft Metashape or COLMAP and real-time geometry reconstruction such as RealityCapture. Various methods were used to evaluate imagery capture, such as smartphone and helmet-mounted cameras for perspective and spherical capture (Fig. 4 and Fig. 5 , Left), drone-based systems for exterior scans, and LiDAR for high-accuracy measurements. These technologies were applied in a Scan-to-BIM process to pre-assess recoverable building components, using point-density, CV, and graph-based matching to estimate the ease of material removal before deconstruction. Using CV, we enhanced pre-demolition material recognition by detailing material types and conditions (Fig. 5 , Right). As images of these conditions are rare in bulk, training data was supplemented with industry images of materials rejected due to defects or damage. Lastly, by integrating advanced CV, XR, and robotics, disassembly processes can incorporate human reasoning to automate complex tasks and adhere to industrial protocols. Step three: Distribution Learnings from the distribution of the disassembled materials from the demolition sites in Geneva and Zurich, Switzerland, demonstrated how to conceptualise digital product passports (DPPs) or digital identities for efficiently tracking, tracing, and trading building materials, facilitating the transition from demolition sites to new construction sites. To upscale circular construction, it is important to (a) monitor data – the supply chain of building materials and their properties must be understood; (b) manage data –materials must be labelled so they can be tracked and used to create more accessible data sets; and, (c) match supply and demand – information is needed on who has waste materials to offer, where they can be stored, and who wants to turn them into resources. To facilitate communication and collaboration between value chain actors, BIM can be used to match data from design, procurement, and construction for recording data in a DPP. This DPP then contains relevant information such as building geometry, material properties, and quantities of components. However, these DPPs must be standardized. Indeed, a rapid proliferation of passport-type mechanisms (building, material, or product passports) resulted in market confusion throughout the entire construction sector, as various platforms are collecting different levels of detail for different purposes (marketplace, circularity calculator, etc.) [ 82 ]. To reach a consensus on these passport mechanisms, we collaborate with stakeholders and policymakers from the building industry. Tagging technologies such as Quick Response (QR) codes (Fig. 7 ) and Radio Frequency Identification (RFID) chips, using Internet of Things (IoT) to connect components to DPPs, enable this transparent material tracking and tracing [ 83 ]. Step four: Design Learnings from designing domes with the reclaimed materials from the disassembly sites demonstrated how to use generative AI to stimulate creative building design with reclaimed component, and then activate computational design algorithms to match the available materials with new construction projects. Generative AI tools were applied in the early design stage of the dome case studies in the form of Text-to-Image engines from RunwayML [ 84 ] and Midjourney [ 85 ] (Fig. 8 and Fig. 9 ). The aim is to explore if these tools can inspire architects to come up with creative design solutions for repurposed, often non-standardized materials. This approach helps incorporate repurposed materials into the design process by including them in text prompts. However, this method has limitations in generating practical solutions, as the outputs from the Text-to-Image models are two-dimensional images that do not represent structurally viable designs, nor do they respect the given material passports geometrically or physically. Despite these limitations, generative AI has potential for driving inspiration in future architectural design processes, particularly if integrated with three-dimensional data to produce spatial outputs that can be more thoroughly evaluated. The selected scenario builds upon a Grasshopper-based tool that we developed in collaboration with the Massachusetts Institute of Technology [ 86 ]. This tool allows for the creation of one or more geodesic domes, each with an adjustable radius and frequency, using wooden beams of various sizes. To apply this tool to our case studies (Domes 5.1 and 5.2), the matching strategy was adjusted from a one-to-one assignment problem to a one-to-many cutting stock problem, using integer linear programming (ILP) [ 87 ]. Expanding further on previous reuse research [ 88 ], a design optimisation algorithm adjusts design parameters to maximise floor area and material usage while minimising cutoff waste. The subsequent cutting-stock optimisation identifies the optimal match between available stock and specific design requirements. Constructing the design exposed unique challenges associated with reuse. Primarily, the matching system aimed to minimise cutoff waste, which inadvertently increased operator time during construction due to frequent tool setup changes and the non-sequential order of production of components relative to their placement in the design. Consequently, two additional goals were integrated into the ILP optimisation: reducing the variety of components cut from a single stock piece and arranging component production in a specific sequence to maintain efficiency while still reducing waste. The design’s stability also factored in, accounting for potential uncertainties or errors in the stock availability (Fig. 10 ). Step five: Deployment: applying digital fabrication to reused materials Learnings from assembling the designed domes demonstrated how subtractive and additive manufacturing facilitate the making of connections between bespoke reclaimed elements and how techniques such as XR facilitate the assembly of the refabricated components with reclaimed materials in new construction projects. The integration of additive and subtractive manufacturing techniques plays a pivotal role in the adaptation and reuse of materials, thereby contributing to more efficient circular supply chains. Specifically, Computer Numerical Control (CNC) milling—a form of subtractive manufacturing—was employed to create bespoke connectors from waste oriented strand board (OSB) plates in the Domes 5.1 and 5.2 case studies. These connectors were designed to enhance the structural integrity of the reclaimed water pipes. Indeed, reused materials are not always the exact dimensions needed for the new construction and need to be augmented to meet the required dimensions. This customisation capability afforded by digital fabrication methods proves essential in accommodating the non-standard sizes frequently encountered in reclaimed materials (Fig. 11 , Left). Furthermore, additive manufacturing enabled robotic additive joining —a form of additive manufacturing— with steel to produce unique connections for reused steel beams [ 89 ] (Fig. 11 , Right). This approach not only demonstrates the feasibility of tailored component interfaces in construction but also underscores the potential of digital fabrication technologies to foster material reuse and reduce waste in building environments. Using a state-of-the-art XR head-mounted display (HMD), in this instance the Microsoft Hololens 2, enabled a step-by-step guided assembly of a dome with reused materials (Fig. 12 ). With Unity3D, a popular simulation engine facilitated the visualisation of the 3D dome based on an accurate 1:1 3D model of the structure. The 3D dome was projected on the HMD, with a slight transparency to it, within a physical space located at the facilities of ETH Zurich. Using different colour schemes directly projected to the HMD (such as the orange in Fig. 12 ) can provide valuable spatiotemporal information, which is critical in engaging and immersing the user as intuitively as possible, especially when incorporating XR technologies [ 79 ]. From a practical perspective, this color-coding system guides the user in the assembly process in identifying which exact component needs to be assembled and in what particular order across the diverse components (temporal information) as well as in determining where exactly the component needs to be placed and mounted (spatial information) [ 81 ]. Discussion Research on information management in circular supply chains reveals the difficulties in coordinating data among stakeholders across different stages of a material’s lifecycle [ 90 ]. Several studies have consistently pointed out that a lack of information is a major obstacle to implementing circular supply chains and material reuse [ 90 ], [ 91 ], [ 92 ]. However, aligning information and material flows beyond the initial phases of product and construction life cycles is rare, due to the complexities of managing and owning information in construction projects and the fragmented nature of the industry [ 93 ]. A structured approach to information flow is needed to identify key steps for reusing materials. Figure 13 illustrates such a process, showing the transition from the end-of-life of one building project to the start of another project, highlighting the differences between the digital and physical processes for construction reuse. Through the use of digital technologies, the data and material supply chain are aligned in our proposed 5D Digital Circular Workflow. For the detection of materials available for reuse, CV and ML techniques achieved high accuracy in classifying building façade materials using street-level imagery and demolition datasets. The integration of temporal data is vital in enhancing its accuracy in real-world scenarios. This necessitates a continual updation of data to encompass alterations due to renovations. Furthermore, addressing visual and cadastral data sparsity in specific regions remains critical for scalability, urging the utilisation of innovative data augmentation strategies to avoid biases and ensure model reliability. As building inventories require analyses beyond the surface level, extending examinations beyond visual assessments to include detailed evaluations that account for underlying structural and material complexities also become essential. Classifying elements based on their safety and identifying areas needing further structural or performance analysis is also needed. This involves advancing the current state-of-the-art in CV, using SVM-based methods tailored for the AEC sector and CNN-based methods known for their accuracy in general vision tasks. The approach could integrate specific data from our process, such as detailed depth maps for precise analysis, BIM-type mappings for comprehensive architectural data, and non-photographic sensor data to enrich the overall dataset. For the disassembly of buildings, the development of material geometry analysis has been validated to catalogue the materials. Previous research primarily provided either general spatial overviews (such as rooms and floor plans) or detailed analyses of specific features (such as space frame node positions) [ 94 ]. Methods for analyzing beam and column components were tailored to accommodate the anticipated geometry range, ensuring accurate data extraction. Moreover, error correction methods were specifically designed for different component types. Significant discrepancies often arose from beams being incorrectly split or joined, resulting in substantial inaccuracies in size estimation. Although our focus was on dimensional accuracy, there currently are no established standards defining the criteria for what constitutes a ‘usable’ reconstruction in this context. While the application of CV in material recognition and inventory during disassembly is still emerging, future studies should aim to enhance the accuracy and robustness of these techniques to improve material identification and classification, even under complex conditions. Implementing real-time visual data analysis can aid immediate decision-making during disassembly. Developing specialised datasets and annotations for these scenarios could improve the training and evaluation of CV models. Additionally, combining CV with robotic systems might automate disassembly, especially when coupled with state-of-the-art ML-based algorithms and paradigms [ 95 ], optimising material sorting. Integrating these technologies with environmental impact assessment tools could also provide a more detailed analysis of the benefits of material reuse and recycling. Contemporary photogrammetry techniques are nearing a critical juncture compared to LiDAR-based methods, with advancements like NeRF [ 96 ] enhancing photo-based component analysis and material volume estimation. While LiDAR still offers better coverage and lower noise, the gap is narrowing. Strategies that divide a site into areas best suited for each method and subsequently integrate the data could balance differences in capture time and accuracy. Furthermore, CV shows promise for real-time material recognition and inventory during disassembly. Integrating 2D and 3D techniques could address their respective limitations—for example, using local shapes for identifying painted structural steel and color analysis for characterising featureless components such as interior walls and floors. This integration, along with multi-sensor approaches that incorporate non-visible data, presents significant research opportunities. In future studies, the breadth and depth of digitisation sources should be increased. This includes the integration of non-geometric sensors, such as thermal cameras, to provide additional insights in scenarios where other methods may lack detail or clarity, deeper testing of mobile-phone LiDAR for accessibility, and usability of NeRF-based photogrammetry. A matchmaking database that combines data collected through photogrammetry, LiDAR, and sensing with data extracted through ML and CV should be created. This comprehensive database would enable architects to design innovative buildings based on the available stock, thanks to a new design approach. For distribution , material tracking and tracing in building construction have been streamlined through the use of QR codes linked to unique DPPs. These passports provide detailed histories and potential applications of building materials, information derived from data collected during the monitoring phase. This innovative system has the potential to transform the industry by creating a dynamic marketplace for the redistribution of materials from deconstructed buildings, effectively matching supply with demand. Furthermore, it could seamlessly integrate with broader material marketplaces, establishing a more extensive and sustainable network for material reuse across the construction industry. This approach not only facilitates efficient material management but also contributes to the development of more sustainable construction practices. The construction industry’s fragmented supply chain presents challenges in material traceability. Blockchain technology, a secure distributed peer-to-peer system, is offers a potential solution for transparent value transactions without the need for central authorities and intermediaries. Part of the challenges associated with reusing elements is the lack of information availability and distribution, both digital and physical. Data from physically monitoring components allows for an understanding of how properties change over time, which could inform its future use case. The subsequent question is how they are being monitored and where that information is being stored. Future data distribution and sharing can leverage linked data principles, distributed ledger technologies, cloud computing, decentralised identity and storage, and open data platforms. For the design step, AI-driven designs, while currently showing limited constructability, have significantly advanced traditional architectural thinking and practice. While AI could generate numerous innovative design possibilities, these showcased limited practical applications in terms of actual construction. However, these outcomes proved invaluable in demonstrating the potential of AI to inspire creative thought processes among architects. The designs, although not always directly usable, sparked new ideas and discussions about the possibilities of material reuse, thus contributing significantly to the conceptual phase of the architectural design of future domes. This underscores the need for continual refinement of AI models to better meet practical construction requirements, merging human oversight with AI’s innovative capabilities for a balance between creativity and functionality. The future of AI in circular architecture is promising, particularly for enhancing sustainable design by facilitating effective material reuse and encouraging a culture of innovation within the AEC industry. Using three-dimensional data to train machines and produce outputs holds the potential to further integrate AI in circular design strategies. Early integration of design optimisation is also critical, even if the changes it introduces are minor. The connection of optimisation tools with design tools at the initial stages of design development and adapting design mindsets to broader definitions of optimisation are essential. This involves considering factors like future usability and production processes of components. Additionally, the design system’s approach to changes—as mere material reassignments—overlooks significant acceptance and aesthetic impacts, highlighting an area for improvement in optimisation strategies. Design optimisation scenarios should cover more realistic conditions, especially multi-source to multi-design over single-source to single-design, to further assess the affects of variable transport distances, component conditions, and manufacturers. While the problems of one- and two-dimensional material cutting and matching are well studied, a more generalisable strategy should be developed for assessing the matchability of arbitrary components. The deployment of buildings with reused materials can be transformed by recent advancements in XR technologies, AI, robotics, and digital fabrication research in construction. These innovations improve the efficiency and effectiveness of complex assembly processes in the AEC industry, particularly in reassembly and disassembly, as showcased in the dome case studies. However, further research is necessary to quantify the benefits of XR technologies and develop a multifaceted evaluation strategy with diverse spatio-temporal metrics. This will provide a deeper understanding of XR's value and allow comparison with traditional methods. Understanding how XR enhances assembly and human motor performance could lead to interfaces that augment user capabilities and automate labor-intensive tasks, freeing engineers and architects to focus on high-level decision-making [ 81 ], [ 95 ]. Integrating multi-sensory feedback (sound and tactile) in the assembly of reused materials can enhance performance by reducing reliance on visual cues and leveraging human sensorimotor strengths [ 79 ], [ 80 ], [ 97 ]. By incorporating XR technologies with robotics, enhanced sensory feedback allows users to intuitively interact with their surroundings, enabling robots to automate complex tasks through advanced AI methods [ 81 ]. Such integration of XR, AI, and robotics facilitates the replication of complex human behaviors and skills, optimizing the automation of demanding, lengthy, and arduous tasks in robotic manipulation and assembly [ 95 ], [ 98 ]. In conclusion, this paper presents a unique approach that harnesses digital innovations from other sectors to enhance the skills of stakeholders in the AEC sector through integration from the broader field of computer science and in the narrower context of human-computer interaction . By leveraging these digital tools, our research aims to disrupt the current linear value chain of the construction sector and establish a data-driven digital circular design and construction approach that promotes effective, user-friendly, and widespread reuse of building materials. The learnings from the case studies not only contribute to the advancement of a sustainable architecture workflow yet also showcasesthe power of interdisciplinary collaboration. The 5D Digital Circular Workflow is essential in achieving the ambitious goal of zero-carbon buildings by 2050. Furthermore, our hands-on, project-based learning approach demonstrates that it is an effective way to acquire engineering and design skills in a real-world design and construction project. Future research aims to expand the workflow into a matchmaking service that pairs supply (materials available for reuse, skills, tools, etc.) and demand (builders in need of materials, skills, tools, etc.) through cloud computing, blockchain, automation, robotics, and big data analytics. Methods We adopted an action research-based methodology to constructively bridge the gap between theoretical insights and practical applications in the AEC sector, as endorsed in recent studies highlighting the efficacy of action research in real-world construction settings [ 99 ]. Following problem identification, we developed an action plan for digitizing circular construction processes, called the 5D Digital Circular Workflow, and evaluated the learnings in each of the 4D Steps. Subsequently, these 5D steps were discretely validated through multiple case studies. To be able apply this circular model to the current and future AEC sector, we worked closely together with construction industry practitioners to exchange and disseminate knowledge [ 99 ]. The case studies are described in Table 3 and the integrated learnings from each implemented action are synthesised in the results. Table 3 Case studies in which we applied the steps of the 5D Digital Circular Workflow Case Study Description 5D Steps Zurich City GIS data, cadastral data, demolition audit data, and google streetview data were collected Detection [ 100 ], [ 101 ] Geneva Warehouse A steel and timber floor structure were disassembled pre-demolition. The timber beams, pipes, and OSB plates were reused for Dome 5.1. Disassembly [ 102 ] Zurich Music Pavilion An entire two-floors timber building was disassembled. The timber beams were reused for Dome 5.2. Disassembly [ 103 ] K118 A reused steel structure and interior of the building was scanned, making a material inventory. Disassembly [ 104 ], [ 105 ] Dome 5.1 A first dome was built with the reused elements from the Geneva warehouse. The dome has been disassembled and re-assembled at 4 different locations. QR codes were engraved for the material passport tracking. CNC milling was used for the connection fabrication. Distribution Design Deployment [ 83 ] Dome 5.2 A second dome was built, improving the tracking technologies, design modelling, and fabrication technologies. Distribution Design Deployment [ 55 ] Domes 5.x Generative AI and XR workflows were tested out on building new domes with reused materials. Design Deployment [ 81 ] Urban-scale case study: Zurich City The city of Zurich was taken as a case study to explore how patterns for material reuse could be identified using CV, ML and MLGIS data. This investigation aimed to identify opportunities for material reuse in buildings that are marked for demolition, enhancing circular construction at urban scale. Since data on existing building materials was limited, ML and CV were applied to assist in documenting building facade materials. The method combines street-level imagery and CV techniques to scale up the documentation of building facade materials [ 100 ]. The use of publicly accessible street view imagery and GIS data ensures that the methodology is not restricted by proprietary data limitations. This open-access approach democratises the technology, allowing even smaller companies and independent researchers to implement and build upon it. In a second step, a method that extends previous research to create a data-driven model for estimating material stock, specifically tailored to datasets available in Zurich was developed [ 101 ]. A dataset was compiled by merging open-access cadastre data with semi-open demolition audit records, resulting in information on 409 residential buildings. Three machine-learning algorithms were employed for predictive analysis: linear regression (LR), random forest regressor (RFR), and extreme gradient boosting (XGBoost). The proof of concept focused on predicting quantities of various materials like wood, mineral, metal, glass, and roof tiles in residential building stock. This approach has the potential to create a more structured, quality-assured, and up-to-date material stock dataset at the urban level, thus facilitating better planning and management. By integrating ML techniques with real-world data in Zurich, this study advances the capability to accurately estimate material stock in urban environments. Disassembly case studies: Geneva warehouse and Zurich music pavilion A car warehouse in Geneva, Switzerland, and a music pavilion of a hospital in Zurich, Switzerland, were carefully disassembled by the authors to test the automation of material cataloguing and sorting using reality capture technologies. First, physical digitisation methods were evaluated for use in initial assessments of buildings about to be demolished. Each method explicitly or implicitly produces geometric information in the form of point clouds, coloured 2D imagery, and a spatial record of the locations of capture in the site. Capture methods were first evaluated for their feasibility in terms of accuracy and efficiency at the building scale [ 47 ]. Available capture systems vary by both their underlying technology as well as the specifics of user interaction. First, photogrammetry reconstructs spatial data based on dense collections of 2D site photography. The relevant software may be an all-in-one product, such as Agisoft Metashape, or a modular pipeline such as COLMAP that can be further integrated with other analysis tools. Additionally, software may operate off-site after data collection (as in the previous examples), or reconstruct live geometry as successive photos are taken (for example RealityCapture) [ 106 ]. To investigate their density of data and ease of use, several methods of imagery capture were compared, including perspective capture using a smartphone and spherical capture using a helmet-mounted camera, capturing both individual frames and video. These methods of human-guided image capture, appropriate for interior and detail work, differ significantly from drone-based capture, which is appropriate for larger areas and facades but more difficult for interiors. Next, we tested the efficiency and accuracy of LiDAR-based sensor systems. This covered the application of smartphone-based and handheld models with greater operator control, and tripod models with higher density and accuracy. The capture methods were compared based on their capture time, point density, overall accuracy, and applicability for simple geometric analysis. LiDAR methods were also evaluated for their use in material volume estimation [ 104 ]. Scan-to-BIM methods were compared in terms of accurate element counts, major dimensions, and element relationships, given that the intended goal of the project was to pre-assess recoverable components from a space before deconstruction. Methods based on point-density statistics and computer-vision detection (Fig. 14 ) were used to determine the location and bounds of elements for disassembly. Individual adjacent elements were progressively connected to the BIM model to represent the system as a graph, assigning each element an ease-of-removal score based on how many elements depend on it [ 103 ], [ 107 ]. A stack-based model using both a bag-of-visual-words and CNN classifier achieved a 0.78 accuracy in classifying interior materials under varying conditions, with the CNN submodel consuming most of the training time [ 108 ]. Reuse building case study: K118 As part of the development of reality capture technologies for the detection and disassembly steps, the K118 Halle [ 21 ], [ 104 ], [ 105 ], a building famous for its reused materials in Switzerland, has been used as a case study. The quality of scan sources was measured on factors including the density by surface area, degree of statistically detected noise, average deviation from a ground truth BIM model, and degree of surface coverage compared to a model (examples of this method tested on different buildings are shown in Fig. 15 ). The noise profiles of each capture method also affected component classification and localisation. Throughout the application of scan-to-BIM to identify steel beam and column systems, photogrammetric methods outperformed mobile-phone LiDAR capture. There was also significant variation between the results for different software used to control the LiDAR capture. Full workflow case studies: Dome 5.1 and Dome 5.2 With the materials disassembled from the disassembly sites (see above), two domes were built on the campus, then disassembled, distributed, and re-assembled. This enabled the full-scale application of our workflow. Tracking and tracing technologies were explored for the Dome 5.1 and Dome 5.2 case studies [ 55 ]. In the monitoring data phase of the projects, data collected from building audits (primarily through the visual audits mentioned above), deconstruction, and reprocessing was compiled in DPPs that were used to create new designs. Next, to manage the data, materials had to be labelled to enable tracking and tracing. To ensure the continuity of information over several life cycles, approaches were developed to connect the web-stored DPPs to the physical building elements. The case studies primarily used QR codes as a means for easy production and read access to the data (Fig. 16 ). The material data collected in the monitoring phase is now visible when reading the QR codes of the components. Tracking and tracing enables us to store all information in one extendable database that can then be used to design new structures with reused materials. The amount of new cutoff waste was used as the objective value for the one-to-many stock cutting problem (Fig. 17 ). With the ultimate goal of optimising material usage, an objective to minimize waste was tested against one maximizing the contiguous length of remaining pieces to potentially enhance the value of larger components. In simulations matching a test dome design requiring 170 m of material to various inventory scenarios, the waste-minimizing objective generally resulted in a more favorable waste score, typically by 4 m or less, but up to 12 m as the available inventory increased. However, the objective focusing solely on contiguous lengths produced scores that were only marginally better, within 1% of those achieved by the waste objective and both within 10% of the theoretical maximum, suggesting that it might be more suitable as a secondary factor. New objectives focusing on production order and fabrication tooling changes were also compared. An objective centered on production order generally resulted in waste scores that were 15 m worse than those achieved by the waste-minimizing objective, and it did not surpass 95% of the theoretical maximum contiguous score. Similarly, the tool-change objective led to waste scores that were 20 m or more and did not exceed 90% of the theoretical maximum contiguous score. These findings suggest that while these objectives may not be as suitable for smaller-scale constructions like the dome, they could be more beneficial in larger projects with more complex scheduling requirements. The large difference in objective results and timing between goals indicated the need to precisely define the production needs of a particular design. While goals focusing first on cut-off waste will consistently produce the best results for this one factor, this is unlikely to be the only consideration in real-world scenarios. Further emerging technology case studies: Domes 5.x The design, dis-, and re-assembly of the domes have been explored further, to evaluate rapidly evolving technologies such as generative AI and XR, as well as to test the ease of reuse of the materials from the original demolition sites throughout several different life cycles. This research used generative AI tools, specifically Text-to-Image engines from RunwayML [ 84 ] and Midjourney [ 85 ], in the early design stage of dome projects to inspire architects with creative designs for repurposed materials, although the generated two-dimensional images were not structurally practical or material-specific. Moreover, XR technologies were employed for the case study as a proof-of-concept for the structural assembly of a timber dome on campus, composed of the reused materials from previously disassembled dome structures (Fig. 12 ). XR offers real-time, step-by-step visual guidance that transcends the spatial and temporal limitations of conventional methods. This case study highlights XR’s potential in enhancing assembly processes, with promising prospects for integrating XR with robotics to automate complex tasks through advanced AI, thereby improving efficiency and reducing manual labor. Future research on the intersection between XR technologies and robotics is being explored on these Domes 5.x. Declarations Data Availability Data will be made available. Author contributions Conceptualisation: CDW; Analysis Design: CDW, BB, DR, MG; Data collection: BB, DR, MG, VS; Analysis: CDW (supervision), BB (distribution), DR (detection), MG (disassembly, design), VS (design), ET (deployement); Writing: CDW, BB, DR, MG, VS, ET; Editing: CDW, BB, VS, ET; Figures: CDW, BB, DR, VS, ET. Acknowledgments The authors are grateful to the industry partners who participated closely in the dis- and re-assembly of the case studies with reused materials, including baubüro in situ, Wiederverwerckle, Herzog & de Meuron, Rotor, and Materiuum, as well as the students who participated in the de- and re-construction sites as well as the design and logistics processes and greatly contributed to the learnings. The authors also thank Design++ for making the digital equipment available for experimentation. Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article. References Y. C. for E. + A. United Nations Environment Programme, “Building Materials and the Climate: Constructing a New Future,” Sep. 2023, [Online]. 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De Wolf, “Digitizing Building Materials for Reuse with Reality Capture and Scan-to-BIM Technologies,” in A Circular Built Environment in the Digital Age , Switzerland: Springer Nature, 2023. M. Gordon, “Mattersite. Thesis Master Degree,” Institute for Advanced Architecture of Catalonia, Vargas Calvo, R., 2021. Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files GraphicalAbstract.png Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 24 May, 2024 Reviews received at journal 22 May, 2024 Reviews received at journal 14 May, 2024 Reviewers agreed at journal 06 May, 2024 Reviewers agreed at journal 06 May, 2024 Reviewers agreed at journal 06 May, 2024 Reviewers invited by journal 04 May, 2024 Editor assigned by journal 01 May, 2024 Submission checks completed at journal 01 May, 2024 First submitted to journal 30 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4349460","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":299199006,"identity":"16a1bfc7-432f-4efd-93b6-1e4036da1ab5","order_by":0,"name":"Catherine De Wolf","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIie3RMWvCQBTA8ReEc7kka0KlfoUXMkhB6ld5Iji1dHNKJSBcl/p1nAMHlyXYVbBgXJxaCHRph4JnsOById0KvT/cDY/7DY8DsNn+Ypk+5XDIsQPsPOI/8wZC0+kFYe2IBIS2xMufo5LopTfoAiu/xCP4/aWsIHk1krAoYiTa8psFdKOlyCEUHiGog5Hg5n4VjD+3HCWwwBUKUHEkJ5VmsntfBUTrmoTfmow0yRrJxj2RrCZXrkgAGY/SJhIWDx96l4nexXmKe+uMB+ouBlJm4uVqXFZ0Oxr4Uu3fZvNrf1HEVZWYyUVOqi9ZfwpQG3Bu/ou3NpvN9l86Atv7VPGjI4EZAAAAAElFTkSuQmCC","orcid":"","institution":"ETH Zurich","correspondingAuthor":true,"prefix":"","firstName":"Catherine","middleName":"","lastName":"De Wolf","suffix":""},{"id":299199007,"identity":"9f560559-e7a1-4270-8412-c8a70cbc73c7","order_by":1,"name":"Brandon S. 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3","display":"","copyAsset":false,"role":"figure","size":326992,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSpatial distribution of building materials for a subset of buildings in Zurich, Switzerland, adapted from [36]\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4349460/v1/a7ca35bf4cd5afa575970837.png"},{"id":55980772,"identity":"05d057d1-2d42-4634-8cd7-a2047a96e450","added_by":"auto","created_at":"2024-05-07 06:40:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1945536,"visible":true,"origin":"","legend":"\u003cp\u003ePointclouds (Left) of the beams disassembled in the Geneva warehouse for building Dome 5.1 and actual disassembly (Right) of these beams\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4349460/v1/bf0b501424eb80941a699f26.png"},{"id":55981645,"identity":"ca4a2fd2-8a09-47a1-92a2-eddff98a6ec5","added_by":"auto","created_at":"2024-05-07 06:56:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1782742,"visible":true,"origin":"","legend":"\u003cp\u003eRaw point cloud generated through scanning (Left) and segmented point cloud processed through CV (Right) applied to Zurich music pavillion, in order to catalogue materials that could be disassembled for the building of Dome 5.2.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4349460/v1/73e065cff564b083180ab1ac.png"},{"id":55980774,"identity":"60e616ae-14c7-4e7b-a014-4ef0df410b23","added_by":"auto","created_at":"2024-05-07 06:40:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":22478736,"visible":true,"origin":"","legend":"\u003cp\u003eDisassembly of the Zurich music pavillion, after scanning and material identification enabled the cataloguing and sorting of the materials, carried out by the authors in collaboration with industry practitioners and academic students (Photography: Buser Hill Photography)\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4349460/v1/101fea32fd7723945cc7fb86.png"},{"id":55981101,"identity":"2329fdb6-33c6-4466-a35a-1979f6f9103e","added_by":"auto","created_at":"2024-05-07 06:48:43","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":54202,"visible":true,"origin":"","legend":"\u003cp\u003eTesting the QR codes\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4349460/v1/70cc175c11c9f2919fd37e05.png"},{"id":55980777,"identity":"b61348db-43fd-4eaa-a854-e9cc8569c9a6","added_by":"auto","created_at":"2024-05-07 06:40:44","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2575975,"visible":true,"origin":"","legend":"\u003cp\u003eImages generated with RunwayML based on the text prompt: “Outside picture of a dome structure similar to Buckminster Fuller’s dome made out of repurposed wooden beams”\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4349460/v1/0104dc7bc4e45aaa53a9f7b0.png"},{"id":55980783,"identity":"8493d77a-65fe-4b15-be28-4c9b0d3c3ab2","added_by":"auto","created_at":"2024-05-07 06:40:45","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":3099341,"visible":true,"origin":"","legend":"\u003cp\u003eImages generated with Midjourney based on the text prompt: “Outside picture of a dome structure similar to Buckminster Fuller’s dome made out of repurposed wooden beams”\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-4349460/v1/af5d882918a3b4b80f40af6d.png"},{"id":55980776,"identity":"c8db06bc-a4cf-4385-bc53-b636c79d3e76","added_by":"auto","created_at":"2024-05-07 06:40:44","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":2024251,"visible":true,"origin":"","legend":"\u003cp\u003eComputational design [86] and test the construction of one dome with disassembled materials (Photography: Daniel 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Photography: Inés Ariza);\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-4349460/v1/2949597799b4c2ac8501b12a.png"},{"id":55980784,"identity":"e725a591-adcb-489e-9f0f-40976905d61b","added_by":"auto","created_at":"2024-05-07 06:40:46","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":9982006,"visible":true,"origin":"","legend":"\u003cp\u003eUsing XR for the structural assembly of a dome with reclaimed elements\u003c/p\u003e","description":"","filename":"Figure12.png","url":"https://assets-eu.researchsquare.com/files/rs-4349460/v1/e50863ab93def74877b7167d.png"},{"id":55980778,"identity":"b6719654-bd14-4d2b-b003-a20ab572397c","added_by":"auto","created_at":"2024-05-07 06:40:44","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":192220,"visible":true,"origin":"","legend":"\u003cp\u003eData and material flow management for circular building projects\u003c/p\u003e","description":"","filename":"Figure13.png","url":"https://assets-eu.researchsquare.com/files/rs-4349460/v1/e429f5a1552b209effadb57e.png"},{"id":55980779,"identity":"824ab4a2-f748-481b-bc08-1ab67d2518a3","added_by":"auto","created_at":"2024-05-07 06:40:44","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":1807729,"visible":true,"origin":"","legend":"\u003cp\u003eCV-assisted segmentation of a scanned point cloud at Zurich Music Pavilion, showcasing digital material cataloging techniques that could be used for optimized disassembly planning.\u003c/p\u003e","description":"","filename":"Figure14.png","url":"https://assets-eu.researchsquare.com/files/rs-4349460/v1/a976d8f4f72e2bccb0857631.png"},{"id":55981104,"identity":"3029841e-5fbf-421a-9158-a8c04580855e","added_by":"auto","created_at":"2024-05-07 06:48:44","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":2880115,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative photogrammetry input image and resulting cloud for case study building K118\u003c/p\u003e","description":"","filename":"Figure15.png","url":"https://assets-eu.researchsquare.com/files/rs-4349460/v1/e8ff35e1aca3660960173642.png"},{"id":55980780,"identity":"5a4e91f9-1faf-4ef6-b224-dc24b8bb2c4b","added_by":"auto","created_at":"2024-05-07 06:40:45","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":46879224,"visible":true,"origin":"","legend":"\u003cp\u003eAttachment of QR Codes linked to digital DPPs for reused wooden elements\u003c/p\u003e","description":"","filename":"Figure16.png","url":"https://assets-eu.researchsquare.com/files/rs-4349460/v1/39b256934a064b47d4abc3f3.png"},{"id":55981105,"identity":"3e23794b-958f-4db9-b839-5f439a1758aa","added_by":"auto","created_at":"2024-05-07 06:48:45","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":496479,"visible":true,"origin":"","legend":"\u003cp\u003eLeft: Design extents of dome, with typical parts for each case below. Right: Example of matching scenario, where certain predefined parts are produced from a non-standard stock piece, leading to some amount of waste. Adapted from [103]\u003c/p\u003e","description":"","filename":"Figure17.png","url":"https://assets-eu.researchsquare.com/files/rs-4349460/v1/d1fc195554c72e31bf056f1e.png"},{"id":55982368,"identity":"454212bd-a3e4-4636-b441-6af65619a7a2","added_by":"auto","created_at":"2024-05-07 07:04:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":16809591,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4349460/v1/cb80fa7d-f680-47bf-a016-4b568525fe0e.pdf"},{"id":55980768,"identity":"bc49d934-cfa7-4756-a1bf-5f0be0daa04c","added_by":"auto","created_at":"2024-05-07 06:40:43","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":46420,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-4349460/v1/f0d8e1d8df20fc93d03d7236.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"A 5D Digital Circular Workflow: Digital Transformation Towards Matchmaking of Environmentally Sustainable Building Materials through Reuse from Disassembly","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNew digital methods \u0026ndash; such as Building Information Modelling (BIM), computational design, big data, robotics, Computer-Aided Manufacturing (CAM), and Artificial Intelligence (AI) \u0026ndash; have the potential to move the entire Architecture, Engineering and Construction (AEC) sector towards a circular model more rapidly. The challenge for all stakeholders of the AEC sector is to respond to global housing needs while reducing environmental impact. Yet, given that the building sector contributes 37% of greenhouse gas emissions, this is no easy task [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. Additionally, construction is responsible for 50% of all material consumption and more than a third of all solid waste [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. One of the main reasons for this is that we construct our buildings following a linear model \u0026ndash; we extract, produce, use, and dispose of building materials and resources. A circular model is needed whereby we would derive maximum value from building materials by reusing them at the end of their service life as new resources. A \u0026ldquo;circular economy\u0026rdquo; [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e] promotes a regenerative system where repair, reuse, recycling, and energy recovery minimise resource use and emissions [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]. More specifically, the reuse of materials in the built environment is essential to tackle challenges such as the scarcity of resources, waste treatment, and the climate crisis. Indeed, the Intergovernmental Panel on Climate Change (IPCC) encourages the reuse of materials as a near-term response to climate change [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. Similarly, the United Nations Environment Programme (UNEP) has launched an initiative to transform the building sector towards near-zero emissions and resilience by 2030, underscoring the need for sustainable building practices globally [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. Implementing circular principles benefits the environment, economy, and society [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. While pioneering circular building projects [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e] and innovative reuse platforms and marketplaces construction [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e] have shown promise in localised settings, global implementation on a large scale has not yet been successful. Material recovery remains labour-intensive, with experts manually measuring and cataloguing data on buildings set for demolition, as well as designing and assembling new projects with these reclaimed materials. Building projects are often complex, multi-variant problems addressed by numerous parties working in separate silos. As a result, to the best of the authors\u0026rsquo; knowledge, there is not yet a feasible circular design strategy that can be broadly applied in construction practice. Digital transformation can fill this gap by streamlining these processes and enhancing efficiency, thereby expanding circular design practices in the construction industry.\u003c/p\u003e\n\u003cp\u003eThe construction industry, which accounts for 13% of the world\u0026rsquo;s GDP, remains the least digitised sector due to its fragmentation and risk aversion, thus struggling to attract digital talent and impeding its adoption of digital technologies [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. Therefore, a viable digital circular construction management system needs to be developed. Upscaling current initiatives of circular construction globally could be achieved more rapidly by embracing digitalisation. This paper proposes a \u003cem\u003e5D Digital Circular Workflow\u003c/em\u003e that assesses existing buildings for their materials\u0026rsquo; reuse in deconstruction, design, and construction using digital technologies (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) with different steps validated by their application to test cases.\u003c/p\u003e\n\u003cp\u003eResearch in circular building practices [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e] highlights challenges that digital technologies can help overcome. Frameworks for digital technologies towards circular construction have been developed for sustainable construction using specific materials, such as upcycled wood [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e], or through literature reviews [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]. Yet, entire workflows of digital innovations for material reuse have rarely been integrated and validated on a full building scale. Our research focuses on case studies that analyse how digital technologies can facilitate deconstruction for reuse of materials in construction. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows our proposed workflow for these cases, with digital tools integrated in each step:\u003c/p\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eStep one \u0026ndash; \u003cstrong\u003eDetection\u003c/strong\u003e: Use urban data combined with Computer Vision (CV) Machine Learning (ML) algorithms to identify sites sutiable for material reuse and incorporate these materials into BIM systems.\u003c/p\u003e\n \u003cp\u003eStep two \u0026ndash; \u003cstrong\u003eDisassembly\u003c/strong\u003e: Catalog materials extensively, employing reality capture, scan-to-BIM and CV to enable robotics and extended reality (XR) for disassembly.\u003c/p\u003e\n \u003cp\u003eStep three \u0026ndash; \u003cstrong\u003eDistribution\u003c/strong\u003e: Create digital product passports to efficiently track, trace, and trade materials from demolition to new construction sites.\u003c/p\u003e\n \u003cp\u003eStep four \u0026ndash; \u003cstrong\u003eDesign\u003c/strong\u003e: Apply generative AI and computational design algorithms to create and match designs with reclaimed materials.\u003c/p\u003e\n \u003cp\u003eStep five \u0026ndash; \u003cstrong\u003eDeployment\u003c/strong\u003e: Use subtractive and additive manufacturing to integrate bespoke reclaimed elements, assembling them in new constructions with XR techniques.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eOngoing research on \u003cstrong\u003eML and CV\u003c/strong\u003e has made great advances in spatial and temporal mapping of existing public housing stocks on the urban scale [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. These advancements have enhanced the interoperability of data representations, allowing different systems (e.g. Geographical Information System (GIS), BIM, construction management software) to effectively exchange and use information. They also provide scalability, enabling the application of these technologies beyond specific sites to broader urban areas across neighbourhoods or entire cities. Additionally, these technologies facilitate the efficient matching of available material resources (supply) with the material needs for new construction that incorporate reused materials (demand). ML, particularly through deep learning, using neural networks modeled after the human brain [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e] has been used extensivelyin other sectors to process data and recognize patterns. Recently, this technology has been applied in the construction industry to process large, unstructurted datasets such as building demolition records. Practitioners in the demolition industry use deep learning methods to forecast the amount of salvage and waste materials obtainable at the end-of-life of buildings [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e], aiding in the planning of cost efficient processes for construction waste management.\u003c/p\u003e\n\u003cp\u003eCV, which trains computers to interpret the visual world, is also increasingly being applied to construction sites to enhance operations, such as building inspections. For example,industry projects such as Spotr.ai [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e] and AeroScan [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e] use imagery from various sources such as unmanned aerial vehicles (UAV), \u003cstrong\u003esatellite\u003c/strong\u003e imagery, or \u003cstrong\u003eGoogle Street View\u003c/strong\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eto assess buildings at an urban scale.\u003c/span\u003e Additionally, imagery available from diverse sources such as \u003cstrong\u003esocial media, public webcams\u003c/strong\u003e, and \u003cstrong\u003ecapturing cars or drones\u003c/strong\u003e [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e] are also used for semantic and dynamic city modelling. To harness the full potential of this technology in facilitating circular construction, the integration of global datasets, including cadastral information and data extracted from imagery must be explored to enable circular strategies such as the forecasting of building materials that could be available for reuse.\u003c/p\u003e\n\u003cp\u003eThe digitisation of buildings through scan-to-BIM has enabled cataloguing materials for their further reuse. In new construction, an increasing number of architecture and engineering industries are using \u003cstrong\u003eBIM\u003c/strong\u003e to store information in three-dimensional (3D) models of their building projects, e.g., the material types, schedule, and cost [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]. Therefore, BIM could also be used to create a database of materials for the building project, i.e., a DPP (similar to a \u003cstrong\u003ematerial passport\u003c/strong\u003e in the AEC industry) [\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e] capturing information about the type, configuration, volume, and location of materials. However, most existing buildings that will be demolished in the next few decades do not have a ready-made BIM model available. Therefore, there is a need to \u003cstrong\u003edigitise information into a BIM model\u003c/strong\u003e, which can be facilitated by scanning the existing building.\u003c/p\u003e\n\u003cp\u003eProcessing \u003cstrong\u003epoint clouds\u003c/strong\u003e remains difficult due to their unstructured, irregular form [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e]. Techniques such as PointNet, adapted from 2D image classification, now facilitate semantic analysis and are used in Scan-to-BIM methods for building elements [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e]. However, these techniques often target specific components like structural steel or scaffolding [\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eCV has also been used on the material scale, for instance, to detect damage such as concrete cracks, steel corrosion, and steel delamination [\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e]. Methods using precise data capture have been developed for the large-scale reuse of concrete as dry masonry with minimal shaping [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e]. In addition, support-vector machine (SVM)-based systems has also been developed to help classify building materials, aiding in automated digital reconstruction for progress monitoring [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e], although it is currently focused on individual classifications and has not been integrated with 3D reconstruction techniques. Despite these advances, there are several challenges in bringing CV to construction environments, which tend to be relatively disorderly. Modern CV systems often mistakenly put greater attention on background details. Additionlly, an active building or demolition site tends to contain many conditions abrsent in carefully constructed training sets [\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e]. These challenges need to be tackled with techniques such as background randomisation in training sets, selective masking by depth during analysis to focus only on relevant elements, and an in-depth analysis of the common misclassifications This would help enable the system to effectively recognize and adapt to patterns of materials commonly found adjacent to each other, enhancing its accuracy and reliability.\u003c/p\u003e\n\u003cp\u003eFor distribution, existing \u003cstrong\u003eplatforms\u003c/strong\u003e [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e] are designed to capture data about the quantity, quality, location, financial value, and circular utility of materials available for reuse. Increaing efforts from research aim to link material platforms to BIM platforms and integrate product tracking and DPPs into these platforms [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e]. \u003cstrong\u003eBlockchain\u003c/strong\u003e technology is now explored for its potential to enable decentralized data management and enhance transparency and traceability in circular construction [\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e]. While Madaster employs BIM for MPs without blockchain, Excess Material Exchange (EME) [\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e] plans to use blockchain for supply chain tracking. Digital platforms commodify buildings as material banks, making building elements tradable and enabling organisations to meet market demands efficiently through economies of scale and scope, as seen in the BoKlok concept by Skanska and IKEA [\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e]. The Reflow project exemplifies such a system, connecting economic actors digitally on a large scale [\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eSuccessful platforms (e.g., Amazon) have shown that growth depends on increasing both supply and demand users [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. Circular platforms should act as catalysts, linking those dealing with building disposal to those starting new constructions. Leveraging digital technology, especially AI algorithms similar to dating app algorithms [\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e], could enhance matchmaking in the construction industry. Unlike one-to-one matching systems (e.g. dating apps), a matchmaking service for the reuse of building materials should focus on a many-to-many relationship between reusable building components and potential new constructions, while considering factors like timing, permits, and material characteristics [\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e]. The tracking and tracing of materials in large material databases would enable the available building materials to be matched for resource allocation.\u003c/p\u003e\n\u003cp\u003eFor the design, the potential of unleashing co-creativity between humans and \u003cstrong\u003egenerative AI\u003c/strong\u003e [\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e] is particularly promising, especially in creative processes, whereby these exhibit promising implementations [\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e]. \u003cstrong\u003eGenerative AI\u003c/strong\u003e has the potential to enhance early-phase circular design processes with advanced data management capabilities. A critical component of this shift is handling extensive digital databases that catalogue dismantled building components, allowing architects to effectively match these materials with new design projects. Leveraging AI, specifically through ML techniques and applying match-making algorithms, can further streamline this process and foster an environment where AI augments human creativity in generating innovative design solutions. Generative AI is a subfield of ML and a form of DL, which, in addition, uses parts of Natural Language Processing (NLP) for working with natural text in, for instance, Text-to-Image Generators, which were used in this paper (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe next stage would be to optimise the design to match the aims and the salvaged materials, through \u003cstrong\u003ecomputational design\u003c/strong\u003e tools such as parametric design space exploration and rule- or grammar-based design approaches. Given a design space, many methods for optimisation are possible, based on genetic algorithms, flocking behaviours or parameter vector manipulation [\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e]. Two matching approaches have been studied: (i) a bottom-up approach, similar to building with blocks, initiated with the available objects, which are then algorithmically aggregated into architectural assemblies [\u003cspan class=\"CitationRef\"\u003e70\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e], (ii) a top-down approach commencing with a target design and then searching the inventory algorithmically, selecting the best fits for the design [\u003cspan class=\"CitationRef\"\u003e73\u003c/span\u003e]. Evaluation of these designs includes multiple factors, covering feasibility, costs, and impact, and automating these requires a multi-objective optimisation approach [\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e]. Design workflows must still be developed to handle truly diverse material stocks, with shorter processing times, guaranteeing the best match.\u003c/p\u003e\n\u003cp\u003eFor deployment, \u003cstrong\u003edigital fabrication\u003c/strong\u003e, a combination of computer-aided design (CAD) data, computer-aided manufacturing (CAM) software, and computer numerical controlled (CNC) hardware, has been increasingly explored in the construction industry to produce rapid prototypes, complex elements, and to perform tasks that are repetitive, dangerous, or require precision [\u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e]. In a circular built environment, digital fabrication can be used to design complex connections [\u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e77\u003c/span\u003e] to make new buildings easier to disassemble [\u003cspan class=\"CitationRef\"\u003e78\u003c/span\u003e]. Moreover, \u003cstrong\u003eXR\u003c/strong\u003e tools have emerged as an immersive and interactive asset both in the AEC industry and in the broader context of human-centered applications [\u003cspan class=\"CitationRef\"\u003e79\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e80\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e81\u003c/span\u003e]. These advancements can benefit the AEC industry and in particular dis- and re-assembly processes.\u003c/p\u003e\n\u003cp\u003eThe planning logistics of disassembling existing buildings are hard to align with the construction of new ones. Consequently, the end-of-life of buildings need to be connected with the start-of-life of other buildings, making the reuse of building materials more effective, user-friendly, and widespread, by, for example, minimising storage times. Existing platforms list materials available for reuse, but in practice, the major challenge is to find the right match. Companies are struggling to find (i) architects who design with reused materials; (ii) construction sites in which these fit; (iii) contractors with reuse skills; (iv) facility managers who can store and process reused materials; and (v) certifiers who can inspect and warranty the performance of reused materials. The creative challenge shifts from original fabrication to adaptation to existing resources, introducing additional complexity to an already intricate field.\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e also indicates the case studies used to validate the developed 5D Digital Circular Workflow. One case study is the Zurich City, for which CV and ML was used to document building materials and predict material stocks. Another set of studies focused on the disassembly of a Geneva warehouse and a Zurich music pavilion, where automated material cataloging and sorting technologies were tested to optimise the reuse process. Additionally, full-scale applications involved the disassembly of these buildings, followed by the design and assembly of Dome 5.1 and Dome 5.2. Emerging technologies were further explored in Domes 5.x, using generative AI and XR to innovate design and improve structural assembly processes.\u003c/p\u003e\n\u003cp\u003eBy taking an action research approach we prototype digital circular techniques on real-world sites. The problem this research aims to address is the need for upscaling sustainable building practices by integrating digital technologies into the circular economy, enhancing material reuse in the construction industry. The action plan employed is to test and improve the technical setup, accuracy, and applicability of our 5D Digital Circular Workflow on different case studies to enhance circular strategies with emerging technologies. By implementing this workflow in real-world deconstruction and construction scenarios, our contribution lies in developing effective strategies for scalable, globally applicable circular building practices. This research aims to strengthen on- and off-site collaboration across the entire value chain towards a circular, low-carbon, zero-waste built environment.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e5D Digital Circular Workflow\u003c/h2\u003e\n \u003cp\u003eThrough the case studies, we developed a 5D Digital Circular Workflow (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e) to match the supply and demand of reused materials. To do so, construction stakeholders need to know who has waste materials to offer, where they can store them, and who wants to turn them into resources. Our research demonstrated that the collected material information could link to Swiss material marketplaces for broader distribution. By developing the digital foundations for this matchmaking, the research project contributes to the upscaling of circular economy principles within the built environment. Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e summarises the digital technologies which were tested in our case studies to develop the workflow.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMain digital technologies explored in the case studies for developing the 5D Digital Circular Workflow, use and predominant step they were used in throughout our case studies\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDigital Technologies\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUse\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSteps\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMachine Learning (ML)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo conduct comprehensive assessments of existing building stocks from building records, in combination with geographical information systems (GIS) and assess the identified stocks to accurately estimate the potential for reusing building components\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDetection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eComputer Vision (CV)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo advance material recognition from visual data and automate the disassembly-for-reuse process \u0026ndash; identifying material types and conditions during deconstruction for precise classification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDetection\u003c/p\u003e\n \u003cp\u003eDisassembly\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eReality capture\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo generate 3D geometric data of existing materials, integrating this information with BIM systems as cyber-physical elements\u0026ndash; in combination with robotics, these technologies enable systematic deconstruction and sorting processes to facilitate the careful disassembly of building materials\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDetection\u003c/p\u003e\n \u003cp\u003eDisassembly\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eExtended Reality (XR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo aid in the dis- and reassembly of materials, simplifying the process and ensuring accuracy in fitting reclaimed components \u0026ndash; robotics are also explored to disassemble building elements carefully\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisassembly Deployment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDigital Product Passports (DPPs)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo extract data to feed into specialised algorithms tailored for the construction industry to effectively match the supply of available materials with demand, serving as digital intermediaries for stakeholders across the value chain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistribution\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrack \u0026amp; trace technologies (including Internet of Things (IoT) \u0026amp; data carriers)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo track information on materials to connect DPPs and material databases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistribution\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDecentralised storage technologies (e.g., blockchain)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo trace the history of the material and information providence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistribution\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenerative AI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo enhance creativity in the architectural design process, optimising the use of available reused materials\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDesign\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eComputational design algorithms (e.g., parametric design)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo plan and model buildings specifically using reused materials. Algorithms are improved to accommodate existing material inventories while factoring in the variances necessary for working with reclaimed stock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDesign\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDigital Fabrication (e.g. additive and subtractive manufacturing)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo produce precise connectors that facilitate the integration of reused materials\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisassembly\u003c/p\u003e\n \u003cp\u003eDeployment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003eStep one: Detection\u003c/h2\u003e\n \u003cp\u003e\u003cem\u003eLearnings from our exploration of the city of Zurich demonstrated how to use a data-centric approach to harness urban data sources, such as Google Street View, cadastral records, and diverse photography. Before any demolition activity, buildings should be classified as as vast repositories of reusable materials. CV and ML algorithms can identify materials in existing buildings, helping to catalogue elements that will be available for reuse. Advanced scanning technologies aid in generating 3D representations, which can be used in combination with the digitised information and further integrated into BIM systems.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eExisting cadastral information and other public record data (e.g., Open Data Zurich) form a foundational base for the creation of building classification maps outlining material-specific characteristics. Building imagery downloaded from the Google Street View API, with associated metadata, can be used in combination with CV and ML to understand the existing material stock that needs to be dismantled or renovated. Such tasks are traditionally labour-intensive if pursued without automated methods. Therefore, algorithms are developed to enrich building databases, amalgamating data collected from images, public records and cadasters. By establishing the composition and condition of materials in urban structures, resource optimisation strategies can be developed to minimise waste generation and foster circular economy practices.\u003c/p\u003e\n \u003cp\u003eThe CV detection method can be adapted to analyse real-time images or digital feeds from building owners, contractors, or even citizens. This method can improve the database by incorporating a broader and more current range of materials and conditions, thereby increasing the model\u0026rsquo;s accuracy and usefulness. This enhanced spatial and temporal understanding of material concentrations (i.e., insights into where specific materials are most abundant) can be a valuable strategic tool for urban planners pursuing a circular economy, by aiding in targeted deconstruction or renovation efforts (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003eStep two: Disassembly\u003c/h2\u003e\n \u003cp\u003e\u003cem\u003eLearnings from disassembly sites in Geneva and Zurich, Switzerland, demonstrated how to further catalogue materials into an expansive database with scan-to-BIM and CV tools and how to document geometries and material specifications so that robotics and XR can be used to assist in dissassembling the materials marked for further reuse.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eIn typical deconstruction projects, only 1% of the building materials are usually reclaimed [\u003cspan\u003e19\u003c/span\u003e]. The disassembly of building materials for reuse has several stages: (a) the dismantling of the building on-site; (b) the sorting of the materials on- and off-site; (c) the cataloguing of the materials in a database that can be used for further distribution; and (d) the precise reshaping of materials to adapt to new construction. For each of these stages, we explored how digital technologies can support systemised building disassembly to make the process less dangerous, cheaper, more efficient, and healthier than conventional demolition practices.\u003c/p\u003e\n \u003cp\u003eCapture systems for spatial data vary in technology and user interaction, and these include photogrammetry with software like Agisoft Metashape or COLMAP and real-time geometry reconstruction such as RealityCapture. Various methods were used to evaluate imagery capture, such as smartphone and helmet-mounted cameras for perspective and spherical capture (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e, Left), drone-based systems for exterior scans, and LiDAR for high-accuracy measurements. These technologies were applied in a Scan-to-BIM process to pre-assess recoverable building components, using point-density, CV, and graph-based matching to estimate the ease of material removal before deconstruction.\u003c/p\u003e\n \u003cp\u003eUsing CV, we enhanced pre-demolition material recognition by detailing material types and conditions (Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e, Right). As images of these conditions are rare in bulk, training data was supplemented with industry images of materials rejected due to defects or damage. Lastly, by integrating advanced CV, XR, and robotics, disassembly processes can incorporate human reasoning to automate complex tasks and adhere to industrial protocols.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003eStep three: Distribution\u003c/h2\u003e\n \u003cp\u003e\u003cem\u003eLearnings from the distribution of the disassembled materials from the demolition sites in Geneva and Zurich, Switzerland, demonstrated how to conceptualise digital product passports (DPPs) or digital identities for efficiently tracking, tracing, and trading building materials, facilitating the transition from demolition sites to new construction sites.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eTo upscale circular construction, it is important to (a) monitor data \u0026ndash; the supply chain of building materials and their properties must be understood; (b) manage data \u0026ndash;materials must be labelled so they can be tracked and used to create more accessible data sets; and, (c) match supply and demand \u0026ndash; information is needed on who has waste materials to offer, where they can be stored, and who wants to turn them into resources.\u003c/p\u003e\n \u003cp\u003eTo facilitate communication and collaboration between value chain actors, BIM can be used to match data from design, procurement, and construction for recording data in a DPP. This DPP then contains relevant information such as building geometry, material properties, and quantities of components. However, these DPPs must be standardized. Indeed, a rapid proliferation of passport-type mechanisms (building, material, or product passports) resulted in market confusion throughout the entire construction sector, as various platforms are collecting different levels of detail for different purposes (marketplace, circularity calculator, etc.) [\u003cspan\u003e82\u003c/span\u003e]. To reach a consensus on these passport mechanisms, we collaborate with stakeholders and policymakers from the building industry. Tagging technologies such as Quick Response (QR) codes (Fig.\u0026nbsp;\u003cspan\u003e7\u003c/span\u003e) and Radio Frequency Identification (RFID) chips, using Internet of Things (IoT) to connect components to DPPs, enable this transparent material tracking and tracing [\u003cspan\u003e83\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003eStep four: Design\u003c/h2\u003e\n \u003cp\u003e\u003cem\u003eLearnings from designing domes with the reclaimed materials from the disassembly sites demonstrated how to use generative AI to stimulate creative building design with reclaimed component, and then activate computational design algorithms to match the available materials with new construction projects.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eGenerative AI tools were applied in the early design stage of the dome case studies in the form of Text-to-Image engines from RunwayML [\u003cspan\u003e84\u003c/span\u003e] and Midjourney [\u003cspan\u003e85\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan\u003e8\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan\u003e9\u003c/span\u003e). The aim is to explore if these tools can inspire architects to come up with creative design solutions for repurposed, often non-standardized materials. This approach helps incorporate repurposed materials into the design process by including them in text prompts. However, this method has limitations in generating practical solutions, as the outputs from the Text-to-Image models are two-dimensional images that do not represent structurally viable designs, nor do they respect the given material passports geometrically or physically. Despite these limitations, generative AI has potential for driving inspiration in future architectural design processes, particularly if integrated with three-dimensional data to produce spatial outputs that can be more thoroughly evaluated.\u003c/p\u003e\n \u003cdiv\u003e\n \u003c/div\u003e\n \u003cp\u003eThe selected scenario builds upon a Grasshopper-based tool that we developed in collaboration with the Massachusetts Institute of Technology [\u003cspan\u003e86\u003c/span\u003e]. This tool allows for the creation of one or more geodesic domes, each with an adjustable radius and frequency, using wooden beams of various sizes. To apply this tool to our case studies (Domes 5.1 and 5.2), the matching strategy was adjusted from a one-to-one assignment problem to a one-to-many cutting stock problem, using integer linear programming (ILP) [\u003cspan\u003e87\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eExpanding further on previous reuse research [\u003cspan\u003e88\u003c/span\u003e], a design optimisation algorithm adjusts design parameters to maximise floor area and material usage while minimising cutoff waste. The subsequent cutting-stock optimisation identifies the optimal match between available stock and specific design requirements. Constructing the design exposed unique challenges associated with reuse. Primarily, the matching system aimed to minimise cutoff waste, which inadvertently increased operator time during construction due to frequent tool setup changes and the non-sequential order of production of components relative to their placement in the design. Consequently, two additional goals were integrated into the ILP optimisation: reducing the variety of components cut from a single stock piece and arranging component production in a specific sequence to maintain efficiency while still reducing waste. The design\u0026rsquo;s stability also factored in, accounting for potential uncertainties or errors in the stock availability (Fig.\u0026nbsp;\u003cspan\u003e10\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003eStep five: Deployment: applying digital fabrication to reused materials\u003c/h2\u003e\n \u003cp\u003e\u003cem\u003eLearnings from assembling the designed domes demonstrated how subtractive and additive manufacturing facilitate the making of connections between bespoke reclaimed elements and how techniques such as XR facilitate the assembly of the refabricated components with reclaimed materials in new construction projects.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eThe integration of additive and subtractive manufacturing techniques plays a pivotal role in the adaptation and reuse of materials, thereby contributing to more efficient circular supply chains. Specifically, Computer Numerical Control (CNC) milling\u0026mdash;a form of subtractive manufacturing\u0026mdash;was employed to create bespoke connectors from waste oriented strand board (OSB) plates in the Domes 5.1 and 5.2 case studies. These connectors were designed to enhance the structural integrity of the reclaimed water pipes. Indeed, reused materials are not always the exact dimensions needed for the new construction and need to be augmented to meet the required dimensions. This customisation capability afforded by digital fabrication methods proves essential in accommodating the non-standard sizes frequently encountered in reclaimed materials (Fig.\u0026nbsp;\u003cspan\u003e11\u003c/span\u003e, Left). Furthermore, additive manufacturing enabled robotic additive joining \u0026mdash;a form of additive manufacturing\u0026mdash; with steel to produce unique connections for reused steel beams [\u003cspan\u003e89\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan\u003e11\u003c/span\u003e, Right). This approach not only demonstrates the feasibility of tailored component interfaces in construction but also underscores the potential of digital fabrication technologies to foster material reuse and reduce waste in building environments.\u003c/p\u003e\n \u003cp\u003eUsing a state-of-the-art XR head-mounted display (HMD), in this instance the Microsoft Hololens 2, enabled a step-by-step guided assembly of a dome with reused materials (Fig.\u0026nbsp;\u003cspan\u003e12\u003c/span\u003e). With Unity3D, a popular simulation engine facilitated the visualisation of the 3D dome based on an accurate 1:1 3D model of the structure. The 3D dome was projected on the HMD, with a slight transparency to it, within a physical space located at the facilities of ETH Zurich. Using different colour schemes directly projected to the HMD (such as the orange in Fig.\u0026nbsp;\u003cspan\u003e12\u003c/span\u003e) can provide valuable spatiotemporal information, which is critical in engaging and immersing the user as intuitively as possible, especially when incorporating XR technologies [\u003cspan\u003e79\u003c/span\u003e]. From a practical perspective, this color-coding system guides the user in the assembly process in identifying \u003cstrong\u003ewhich\u003c/strong\u003e exact component needs to be assembled and in what particular order across the diverse components (temporal information) as well as in determining \u003cstrong\u003ewhere\u003c/strong\u003e exactly the component needs to be placed and mounted (spatial information) [\u003cspan\u003e81\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eResearch on information management in circular supply chains reveals the difficulties in coordinating data among stakeholders across different stages of a material\u0026rsquo;s lifecycle [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. Several studies have consistently pointed out that a lack of information is a major obstacle to implementing circular supply chains and material reuse [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e], [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e], [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]. However, aligning information and material flows beyond the initial phases of product and construction life cycles is rare, due to the complexities of managing and owning information in construction projects and the fragmented nature of the industry [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. A structured approach to information flow is needed to identify key steps for reusing materials. Figure\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e13\u003c/span\u003e illustrates such a process, showing the transition from the end-of-life of one building project to the start of another project, highlighting the differences between the digital and physical processes for construction reuse. Through the use of digital technologies, the data and material supply chain are aligned in our proposed 5D Digital Circular Workflow.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the \u003cb\u003edetection\u003c/b\u003e of materials available for reuse, CV and ML techniques achieved high accuracy in classifying building fa\u0026ccedil;ade materials using street-level imagery and demolition datasets. The integration of temporal data is vital in enhancing its accuracy in real-world scenarios. This necessitates a continual updation of data to encompass alterations due to renovations. Furthermore, addressing visual and cadastral data sparsity in specific regions remains critical for scalability, urging the utilisation of innovative data augmentation strategies to avoid biases and ensure model reliability. As building inventories require analyses beyond the surface level, extending examinations beyond visual assessments to include detailed evaluations that account for underlying structural and material complexities also become essential.\u003c/p\u003e \u003cp\u003eClassifying elements based on their safety and identifying areas needing further structural or performance analysis is also needed. This involves advancing the current state-of-the-art in CV, using SVM-based methods tailored for the AEC sector and CNN-based methods known for their accuracy in general vision tasks. The approach could integrate specific data from our process, such as detailed depth maps for precise analysis, BIM-type mappings for comprehensive architectural data, and non-photographic sensor data to enrich the overall dataset.\u003c/p\u003e \u003cp\u003eFor the \u003cb\u003edisassembly\u003c/b\u003e of buildings, the development of material geometry analysis has been validated to catalogue the materials. Previous research primarily provided either general spatial overviews (such as rooms and floor plans) or detailed analyses of specific features (such as space frame node positions) [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]. Methods for analyzing beam and column components were tailored to accommodate the anticipated geometry range, ensuring accurate data extraction. Moreover, error correction methods were specifically designed for different component types. Significant discrepancies often arose from beams being incorrectly split or joined, resulting in substantial inaccuracies in size estimation. Although our focus was on dimensional accuracy, there currently are no established standards defining the criteria for what constitutes a \u0026lsquo;usable\u0026rsquo; reconstruction in this context. While the application of CV in material recognition and inventory during disassembly is still emerging, future studies should aim to enhance the accuracy and robustness of these techniques to improve material identification and classification, even under complex conditions. Implementing real-time visual data analysis can aid immediate decision-making during disassembly. Developing specialised datasets and annotations for these scenarios could improve the training and evaluation of CV models. Additionally, combining CV with robotic systems might automate disassembly, especially when coupled with state-of-the-art ML-based algorithms and paradigms [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e], optimising material sorting. Integrating these technologies with environmental impact assessment tools could also provide a more detailed analysis of the benefits of material reuse and recycling.\u003c/p\u003e \u003cp\u003eContemporary photogrammetry techniques are nearing a critical juncture compared to LiDAR-based methods, with advancements like NeRF [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e] enhancing photo-based component analysis and material volume estimation. While LiDAR still offers better coverage and lower noise, the gap is narrowing. Strategies that divide a site into areas best suited for each method and subsequently integrate the data could balance differences in capture time and accuracy. Furthermore, CV shows promise for real-time material recognition and inventory during disassembly. Integrating 2D and 3D techniques could address their respective limitations\u0026mdash;for example, using local shapes for identifying painted structural steel and color analysis for characterising featureless components such as interior walls and floors. This integration, along with multi-sensor approaches that incorporate non-visible data, presents significant research opportunities. In future studies, the breadth and depth of digitisation sources should be increased. This includes the integration of non-geometric sensors, such as thermal cameras, to provide additional insights in scenarios where other methods may lack detail or clarity, deeper testing of mobile-phone LiDAR for accessibility, and usability of NeRF-based photogrammetry. A matchmaking database that combines data collected through photogrammetry, LiDAR, and sensing with data extracted through ML and CV should be created. This comprehensive database would enable architects to design innovative buildings based on the available stock, thanks to a new design approach.\u003c/p\u003e \u003cp\u003eFor \u003cb\u003edistribution\u003c/b\u003e, material tracking and tracing in building construction have been streamlined through the use of QR codes linked to unique DPPs. These passports provide detailed histories and potential applications of building materials, information derived from data collected during the monitoring phase. This innovative system has the potential to transform the industry by creating a dynamic marketplace for the redistribution of materials from deconstructed buildings, effectively matching supply with demand. Furthermore, it could seamlessly integrate with broader material marketplaces, establishing a more extensive and sustainable network for material reuse across the construction industry. This approach not only facilitates efficient material management but also contributes to the development of more sustainable construction practices. The construction industry\u0026rsquo;s fragmented supply chain presents challenges in material traceability.\u003c/p\u003e \u003cp\u003eBlockchain technology, a secure distributed peer-to-peer system, is offers a potential solution for transparent value transactions without the need for central authorities and intermediaries. Part of the challenges associated with reusing elements is the lack of information availability and distribution, both digital and physical. Data from physically monitoring components allows for an understanding of how properties change over time, which could inform its future use case. The subsequent question is how they are being monitored and where that information is being stored. Future data distribution and sharing can leverage linked data principles, distributed ledger technologies, cloud computing, decentralised identity and storage, and open data platforms.\u003c/p\u003e \u003cp\u003eFor the \u003cb\u003edesign\u003c/b\u003e step, AI-driven designs, while currently showing limited constructability, have significantly advanced traditional architectural thinking and practice. While AI could generate numerous innovative design possibilities, these showcased limited practical applications in terms of actual construction. However, these outcomes proved invaluable in demonstrating the potential of AI to inspire creative thought processes among architects. The designs, although not always directly usable, sparked new ideas and discussions about the possibilities of material reuse, thus contributing significantly to the conceptual phase of the architectural design of future domes. This underscores the need for continual refinement of AI models to better meet practical construction requirements, merging human oversight with AI\u0026rsquo;s innovative capabilities for a balance between creativity and functionality. The future of AI in circular architecture is promising, particularly for enhancing sustainable design by facilitating effective material reuse and encouraging a culture of innovation within the AEC industry. Using three-dimensional data to train machines and produce outputs holds the potential to further integrate AI in circular design strategies.\u003c/p\u003e \u003cp\u003eEarly integration of design optimisation is also critical, even if the changes it introduces are minor. The connection of optimisation tools with design tools at the initial stages of design development and adapting design mindsets to broader definitions of optimisation are essential. This involves considering factors like future usability and production processes of components. Additionally, the design system\u0026rsquo;s approach to changes\u0026mdash;as mere material reassignments\u0026mdash;overlooks significant acceptance and aesthetic impacts, highlighting an area for improvement in optimisation strategies. Design optimisation scenarios should cover more realistic conditions, especially multi-source to multi-design over single-source to single-design, to further assess the affects of variable transport distances, component conditions, and manufacturers. While the problems of one- and two-dimensional material cutting and matching are well studied, a more generalisable strategy should be developed for assessing the matchability of arbitrary components.\u003c/p\u003e \u003cp\u003eThe \u003cb\u003edeployment\u003c/b\u003e of buildings with reused materials can be transformed by recent advancements in XR technologies, AI, robotics, and digital fabrication research in construction. These innovations improve the efficiency and effectiveness of complex assembly processes in the AEC industry, particularly in reassembly and disassembly, as showcased in the dome case studies. However, further research is necessary to quantify the benefits of XR technologies and develop a multifaceted evaluation strategy with diverse spatio-temporal metrics. This will provide a deeper understanding of XR's value and allow comparison with traditional methods. Understanding how XR enhances assembly and human motor performance could lead to interfaces that augment user capabilities and automate labor-intensive tasks, freeing engineers and architects to focus on high-level decision-making [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e], [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIntegrating multi-sensory feedback (sound and tactile) in the assembly of reused materials can enhance performance by reducing reliance on visual cues and leveraging human sensorimotor strengths [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e], [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e], [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. By incorporating XR technologies with robotics, enhanced sensory feedback allows users to intuitively interact with their surroundings, enabling robots to automate complex tasks through advanced AI methods [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. Such integration of XR, AI, and robotics facilitates the replication of complex human behaviors and skills, optimizing the automation of demanding, lengthy, and arduous tasks in robotic manipulation and assembly [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e], [\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn conclusion, this paper presents a unique approach that harnesses digital innovations from other sectors to enhance the skills of stakeholders in the AEC sector through integration from the broader field \u003cb\u003eof computer science and in the narrower context of human-computer interaction\u003c/b\u003e. By leveraging these digital tools, our research aims to disrupt the current linear value chain of the construction sector and establish a data-driven digital circular design and construction approach that promotes effective, user-friendly, and widespread reuse of building materials. The learnings from the case studies not only contribute to the advancement of a sustainable architecture workflow yet also showcasesthe power of interdisciplinary collaboration. The 5D Digital Circular Workflow is essential in achieving the ambitious goal of zero-carbon buildings by 2050. Furthermore, our hands-on, project-based learning approach demonstrates that it is an effective way to acquire engineering and design skills in a real-world design and construction project. Future research aims to expand the workflow into a matchmaking service that pairs supply (materials available for reuse, skills, tools, etc.) and demand (builders in need of materials, skills, tools, etc.) through cloud computing, blockchain, automation, robotics, and big data analytics.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe adopted an action research-based methodology to constructively bridge the gap between theoretical insights and practical applications in the AEC sector, as endorsed in recent studies highlighting the efficacy of action research in real-world construction settings [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e]. Following problem identification, we developed an action plan for digitizing circular construction processes, called the 5D Digital Circular Workflow, and evaluated the learnings in each of the 4D Steps. Subsequently, these 5D steps were discretely validated through multiple case studies. To be able apply this circular model to the current and future AEC sector, we worked closely together with construction industry practitioners to exchange and disseminate knowledge [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e]. The case studies are described in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and the integrated learnings from each implemented action are synthesised in the results.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCase studies in which we applied the steps of the 5D Digital Circular Workflow\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase Study\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5D Steps\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZurich City\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGIS data, cadastral data, demolition audit data, and google streetview data were collected\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDetection\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e], [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneva Warehouse\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA steel and timber floor structure were disassembled pre-demolition. The timber beams, pipes, and OSB plates were reused for Dome 5.1.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDisassembly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZurich\u003c/p\u003e \u003cp\u003eMusic Pavilion\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAn entire two-floors timber building was disassembled. The timber beams were reused for Dome 5.2.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDisassembly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK118\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA reused steel structure and interior of the building was scanned, making a material inventory.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDisassembly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e], [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDome 5.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA first dome was built with the reused elements from the Geneva warehouse. The dome has been disassembled and re-assembled at 4 different locations. QR codes were engraved for the material passport tracking. CNC milling was used for the connection fabrication.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDistribution\u003c/p\u003e \u003cp\u003eDesign\u003c/p\u003e \u003cp\u003eDeployment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDome 5.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA second dome was built, improving the tracking technologies, design modelling, and fabrication technologies.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDistribution\u003c/p\u003e \u003cp\u003eDesign\u003c/p\u003e \u003cp\u003eDeployment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomes 5.x\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenerative AI and XR workflows were tested out on building new domes with reused materials.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDesign\u003c/p\u003e \u003cp\u003eDeployment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eUrban-scale case study: Zurich City\u003c/h2\u003e \u003cp\u003eThe city of Zurich was taken as a case study to explore how patterns for material reuse could be identified using CV, ML and MLGIS data. This investigation aimed to identify opportunities for material reuse in buildings that are marked for demolition, enhancing circular construction at urban scale.\u003c/p\u003e \u003cp\u003eSince data on existing building materials was limited, ML and CV were applied to assist in documenting building facade materials. The method combines street-level imagery and CV techniques to scale up the documentation of building facade materials [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]. The use of publicly accessible street view imagery and GIS data ensures that the methodology is not restricted by proprietary data limitations. This open-access approach democratises the technology, allowing even smaller companies and independent researchers to implement and build upon it.\u003c/p\u003e \u003cp\u003eIn a second step, a method that extends previous research to create a data-driven model for estimating material stock, specifically tailored to datasets available in Zurich was developed [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e]. A dataset was compiled by merging open-access cadastre data with semi-open demolition audit records, resulting in information on 409 residential buildings. Three machine-learning algorithms were employed for predictive analysis: linear regression (LR), random forest regressor (RFR), and extreme gradient boosting (XGBoost). The proof of concept focused on predicting quantities of various materials like wood, mineral, metal, glass, and roof tiles in residential building stock. This approach has the potential to create a more structured, quality-assured, and up-to-date material stock dataset at the urban level, thus facilitating better planning and management. By integrating ML techniques with real-world data in Zurich, this study advances the capability to accurately estimate material stock in urban environments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDisassembly case studies: Geneva warehouse and Zurich music pavilion\u003c/h2\u003e \u003cp\u003eA car warehouse in Geneva, Switzerland, and a music pavilion of a hospital in Zurich, Switzerland, were carefully disassembled by the authors to test the automation of material cataloguing and sorting using reality capture technologies.\u003c/p\u003e \u003cp\u003eFirst, physical digitisation methods were evaluated for use in initial assessments of buildings about to be demolished. Each method explicitly or implicitly produces geometric information in the form of point clouds, coloured 2D imagery, and a spatial record of the locations of capture in the site. Capture methods were first evaluated for their feasibility in terms of accuracy and efficiency at the building scale [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAvailable capture systems vary by both their underlying technology as well as the specifics of user interaction. First, photogrammetry reconstructs spatial data based on dense collections of 2D site photography. The relevant software may be an all-in-one product, such as Agisoft Metashape, or a modular pipeline such as COLMAP that can be further integrated with other analysis tools. Additionally, software may operate off-site after data collection (as in the previous examples), or reconstruct live geometry as successive photos are taken (for example RealityCapture) [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo investigate their density of data and ease of use, several methods of imagery capture were compared, including perspective capture using a smartphone and spherical capture using a helmet-mounted camera, capturing both individual frames and video. These methods of human-guided image capture, appropriate for interior and detail work, differ significantly from drone-based capture, which is appropriate for larger areas and facades but more difficult for interiors. Next, we tested the efficiency and accuracy of LiDAR-based sensor systems. This covered the application of smartphone-based and handheld models with greater operator control, and tripod models with higher density and accuracy. The capture methods were compared based on their capture time, point density, overall accuracy, and applicability for simple geometric analysis. LiDAR methods were also evaluated for their use in material volume estimation [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eScan-to-BIM methods were compared in terms of accurate element counts, major dimensions, and element relationships, given that the intended goal of the project was to pre-assess recoverable components from a space before deconstruction. Methods based on point-density statistics and computer-vision detection (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e14\u003c/span\u003e) were used to determine the location and bounds of elements for disassembly. Individual adjacent elements were progressively connected to the BIM model to represent the system as a graph, assigning each element an ease-of-removal score based on how many elements depend on it [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e], [\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e]. A stack-based model using both a bag-of-visual-words and CNN classifier achieved a 0.78 accuracy in classifying interior materials under varying conditions, with the CNN submodel consuming most of the training time [\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eReuse building case study: K118\u003c/h2\u003e \u003cp\u003eAs part of the development of reality capture technologies for the detection and disassembly steps, the K118 Halle [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e], [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e], a building famous for its reused materials in Switzerland, has been used as a case study. The quality of scan sources was measured on factors including the density by surface area, degree of statistically detected noise, average deviation from a ground truth BIM model, and degree of surface coverage compared to a model (examples of this method tested on different buildings are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e15\u003c/span\u003e). The noise profiles of each capture method also affected component classification and localisation. Throughout the application of scan-to-BIM to identify steel beam and column systems, photogrammetric methods outperformed mobile-phone LiDAR capture. There was also significant variation between the results for different software used to control the LiDAR capture.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eFull workflow case studies: Dome 5.1 and Dome 5.2\u003c/h2\u003e \u003cp\u003eWith the materials disassembled from the disassembly sites (see above), two domes were built on the campus, then disassembled, distributed, and re-assembled. This enabled the full-scale application of our workflow. Tracking and tracing technologies were explored for the Dome 5.1 and Dome 5.2 case studies [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. In the monitoring data phase of the projects, data collected from building audits (primarily through the visual audits mentioned above), deconstruction, and reprocessing was compiled in DPPs that were used to create new designs. Next, to manage the data, materials had to be labelled to enable tracking and tracing. To ensure the continuity of information over several life cycles, approaches were developed to connect the web-stored DPPs to the physical building elements. The case studies primarily used QR codes as a means for easy production and read access to the data (Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e16\u003c/span\u003e). The material data collected in the monitoring phase is now visible when reading the QR codes of the components.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTracking and tracing enables us to store all information in one extendable database that can then be used to design new structures with reused materials. The amount of new cutoff waste was used as the objective value for the one-to-many stock cutting problem (Fig.\u0026nbsp;\u003cspan refid=\"Fig19\" class=\"InternalRef\"\u003e17\u003c/span\u003e). With the ultimate goal of optimising material usage, an objective to minimize waste was tested against one maximizing the contiguous length of remaining pieces to potentially enhance the value of larger components. In simulations matching a test dome design requiring 170 m of material to various inventory scenarios, the waste-minimizing objective generally resulted in a more favorable waste score, typically by 4 m or less, but up to 12 m as the available inventory increased. However, the objective focusing solely on contiguous lengths produced scores that were only marginally better, within 1% of those achieved by the waste objective and both within 10% of the theoretical maximum, suggesting that it might be more suitable as a secondary factor.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNew objectives focusing on production order and fabrication tooling changes were also compared. An objective centered on production order generally resulted in waste scores that were 15 m worse than those achieved by the waste-minimizing objective, and it did not surpass 95% of the theoretical maximum contiguous score. Similarly, the tool-change objective led to waste scores that were 20 m or more and did not exceed 90% of the theoretical maximum contiguous score. These findings suggest that while these objectives may not be as suitable for smaller-scale constructions like the dome, they could be more beneficial in larger projects with more complex scheduling requirements.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe large difference in objective results and timing between goals indicated the need to precisely define the production needs of a particular design. While goals focusing first on cut-off waste will consistently produce the best results for this one factor, this is unlikely to be the only consideration in real-world scenarios.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eFurther emerging technology case studies: Domes 5.x\u003c/h2\u003e \u003cp\u003eThe design, dis-, and re-assembly of the domes have been explored further, to evaluate rapidly evolving technologies such as generative AI and XR, as well as to test the ease of reuse of the materials from the original demolition sites throughout several different life cycles. This research used generative AI tools, specifically Text-to-Image engines from RunwayML [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e] and Midjourney [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e], in the early design stage of dome projects to inspire architects with creative designs for repurposed materials, although the generated two-dimensional images were not structurally practical or material-specific.\u003c/p\u003e \u003cp\u003eMoreover, XR technologies were employed for the case study as a proof-of-concept for the structural assembly of a timber dome on campus, composed of the reused materials from previously disassembled dome structures (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e12\u003c/span\u003e). XR offers real-time, step-by-step visual guidance that transcends the spatial and temporal limitations of conventional methods. This case study highlights XR’s potential in enhancing assembly processes, with promising prospects for integrating XR with robotics to automate complex tasks through advanced AI, thereby improving efficiency and reducing manual labor. Future research on the intersection between XR technologies and robotics is being explored on these Domes 5.x.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eData will be made available.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Author contributions\u003c/p\u003e\n\u003cp\u003eConceptualisation: CDW; Analysis Design: CDW, BB, DR, MG; Data collection: BB, DR, MG, VS; Analysis: CDW (supervision), BB (distribution), DR (detection), MG (disassembly, design), VS (design), ET (deployement); Writing: CDW, BB, DR, MG, VS, ET; Editing: CDW, BB, VS, ET; Figures: CDW, BB, DR, VS, ET.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Acknowledgments\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to the industry partners who participated closely in the dis- and re-assembly of the case studies with reused materials, including baub\u0026uuml;ro in situ, Wiederverwerckle, Herzog \u0026amp; de Meuron, Rotor, and Materiuum, as well as the students who participated in the de- and re-construction sites as well as the design and logistics processes and greatly contributed to the learnings. \u0026nbsp;The authors also thank Design++ for making the digital equipment available for experimentation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Competing Interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eY. 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Thesis Master Degree,\u0026rdquo; Institute for Advanced Architecture of Catalonia, Vargas Calvo, R., 2021.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-materials-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Materials Sustainability](https://www.nature.com/npjmatsustain)","snPcode":"44296","submissionUrl":"https://submission.springernature.com/new-submission/44296/3","title":"npj Materials Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"LiDAR scanning, photogrammetry, scan-to-Building Information Modeling (BIM), computer vision, extended reality (XR), Digital Product Passports (DPP)","lastPublishedDoi":"10.21203/rs.3.rs-4349460/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4349460/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe intersection of digital transformation, circular economy, and innovative construction practices presents a nascent field with significant potential to mitigate environmental impacts through optimised material reuse. This research aims to understand how digital technologies can augment the reuse of construction materials. We evaluated a plethora of digital tools, including digital product passports, artificial intelligence (AI)-assisted material classification, reality capture, computational and generative AI-aided design, digital fabrication techniques, and blockchain technology, for their efficacy in facilitating building material reuse. The practical component of the study involved disassembling buildings and then designing and executing a construction project reusing the salvaged materials. Findings demonstrate that the successful application of circular economy principles is facilitated by digital forms of cataloguing, inventory management, design, and construction. 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