Self-shaping wood bilayers exhibit uncertainties in bending behaviour due to natural material variations such as growth rings and grain patterns that influence properties such as differential swelling coefficients and stiffness. While previous physics-based models use simplified assumptions about these variations, our method uses machine learning to capture spatially varying local features from each sample. This data-driven method learns complex, non-linear relationships of these features with bilayer curvature. Our framework encompasses bilayer production, data acquisition and preparation utilising computer vision, as well as the development of machine learning models. We analysed 64 sycamore maple ( Acer pseudoplatanus L. ) bilayers, yielding 1,216 data points on spatially local natural variations that correlate with their curvatures. Random Forest proved to be the most accurate, achieving a coefficient of determination (R 2 ) above 0.9 during training and over 0.8 on unseen test data. This framework demonstrates the integration of fine-grained material data into predictive models for material-driven wood design, adaptable to different species and data-driven methods.

Semantic Web technologies are increasingly used in the architecture, engineering, and construction (AEC) industry, yet the Resource Description Framework (RDF) and its query language, SPARQL, still lack native support for 3D geometry. Existing approaches either reduce geometry to 2D, rely on external spatial databases, or require processing workflows outside the semantic layer. This paper introduces geof3D, an extension to SPARQL that enables 3D geometric computation directly inside RDF triple stores. The framework is grounded in a formal function space derived from Architectural Geometry and provides typed operators for measurement, spatial predicates, constructive solid modeling, and affine transformations. These functions are implemented as SPARQL built-ins in RDF4J, supported by an execution backend that uses Java-based processing together with SFCGAL, a robust computational geometry engine accessed through the Java Native Interface (JNI). The system supports operations including geometric validation, Boolean solids, 3D spatial queries, and shape transformations without leaving the RDF environment. We evaluate geof3D using real building models from the Large-Scale Construction Robotics Laboratory and show that the framework supports spatial alignment, clash detection, and algorithmic modeling entirely through RDF-native queries. The evaluation examines both expressiveness and implementation performance, combining in-browser benchmarking with direct JNI measurements and comparative testing against a PostGIS configuration to assess performance, scalability, and geometric fidelity. All code, queries, datasets, and benchmarks are openly released. This work shows that SPARQL can serve not only as a semantic query language but also as a computational interface for 3D co-design, enabling integrated, interoperable, and geometry-aware workflows for building information management.

Digital Twins (DTs) hold promise for Construction 4.0, yet current applications remain fragmented, especially at the fabrication and construction level, where heterogeneous machines and workflows must be coordinated in real time. This paper addresses this gap in two steps. First, by understanding what the requirements are for developing a DT architecture for fabrication and construction. Second, using the derived requirements to propose a DT architecture for digital fabrication and construction structured around three components: tasks with a semi-structured data schema, virtual–physical pairs that bridge machines through protocol-specific virtual actors, and modular services for monitoring, simulation, and adaptive control. An execution engine coordinates these components via an event-driven mechanism, ensuring real-time task management and robust traceability. The DT architecture is validated through three case studies: prefabricated timber assembly, collective robotic construction, and large-scale 3D printing, demonstrating its capacity to integrate diverse machines, manage sequential and adaptive processes, and support on-the-fly task injection. By combining flexibility, interoperability, and data integration, the proposed framework contributes a generalisable foundation for DT-enabled fabrication and construction workflows, advancing towards more adaptive and resilient Construction 4.0 practices. • Proposes a scalable digital twin architecture for fabrication and construction. • Introduces data cores and virtual–physical pairs for heterogeneous systems. • Integrates monitoring, simulation, and adaptive control as modular services. • Validates the approach in timber assembly, robotics, and large-scale 3D printing.

Human–robot collaboration (HRC) offers promising potential for more flexible and sustainable production practices in architecture and construction. This requires HRC setups to scale up from light-payload collaborative robots to conform with the scale of building construction while considering the safety and teamwork culture for workers. This research proposes a system for large-scale multi-user HRC using head-mounted augmented reality (AR) devices. To achieve this, we contribute three methods that work in conjunction: (1) an AR system that enables multiple users to share tasks and work together with robots; (2) a dynamic human task allocation engine that reacts to the changing production teams and task types; and (3) a safety zone generation and allocation method to configure human collaboration in shared space with large-scale robots. The system is evaluated using a case study of prefabricated timber cassettes combining discrete event simulations , a user study and a fabrication process demonstrator with an industry partner.