With the rapid advancement in information technology, the digital twin and smart assembly process simulation have become an integral part of the design and manufacturing of high-precision products. However, conventional Tunnel Boring Machine (TBM) cutterhead design and on-site assembly planning remain largely experience-driven and fragmented, with limited interoperability between geological characterization, structural verification, and constructability validation. This study proposes a digital twin-driven framework for TBM cutterhead design optimization and assembly process simulation that integrates geology-aware design inputs, BIM-based information modelling, FEM-based structural assessment, and immersive virtual environments within a unified virtual–physical workflow. To ensure consistent data exchange across platforms, an IFC4.3-compliant ontology is established using a non-intrusive property-set (Pset) extension strategy to represent cutterhead components, geological parameters, FEM load cases/results, and assembly tasks. Tunnel-scale stress analysis and cutter–rock interaction modelling are used to define project-representative cutter loading envelopes, which are mapped to a high-fidelity cutterhead FEM model for iterative structural refinement. The optimized configuration is then transferred to a game-engine/VR environment to support full-scale design inspection and assembly rehearsal, followed by manufacturing and field deployment with bidirectional feedback. To validate the proposed framework, an implementation case study of a deep hard-rock tunnelling project is presented where five design iterations were tracked across BIM–FEM–VR and nine constructability issues detected and resolved prior to assembly. The results indicate that the proposed digital twin approach strengthens traceability from geology to loading to structural response, reduces localized stress concentration at critical interfaces, and improves assembly readiness for complex tunnelling projects.

Earthwork operations constitute a substantial share of infrastructure project costs and are critical to overall project efficiency. However, the construction industry still relies on conventional approaches and there is a lack of integrated fleet management systems for collaboratively working equipment. While telematics is widely used in other industries, its applications to monitor the complex interactions between excavators, dump trucks, and dozers in real time remain limited. This study proposes an intelligent fleet monitoring system that utilizes only satellite navigation data (GNSS) to analyze the real-time productivity of multiple earthwork machines without relying on additional sensors, such as IMU or accelerometers, thereby eliminating the need for separate measurement procedures. A lightweight site configuration step is required to define the work area/loading/dumping geofences on an existing site map. This research provides novel developed algorithms that facilitate a real-time productivity assessment for several earthwork equipment and provide planning-level recommendations for equipment deployment combinations. Dedicated motion classification algorithms were developed for excavators, dump trucks, and dozers to distinguish activity states, to compute working and idle times, and to quantify operational efficiency. The system integrates a web-based e-Fleet Management platform and a mobile e-Map application for visualization and equipment optimization. Field validation was conducted on two active earthwork projects to evaluate accuracy and feasibility. The results demonstrate that the developed algorithms achieved classification and productivity estimation errors within 2.5%, while enabling optimized equipment combinations and improved cycle time efficiency. The proposed system offers a practical, sensor-independent approach for enhancing productivity monitoring, real-time decision-making, and cost efficiency in large-scale earthwork operations.