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.