# Voxel-Based Digital Twin Framework for Earthwork Construction
## 4 Implementation of the Proposed System
### Graphical Simulation Environment for Model-Equipment Interaction
#### 4.1.3 Model Integration in Platform
Additionally, the voxels' semantics are used, which provides semantic tracking along with volumetric information. Moreover, the comparison of the as-planned and as-built models provides quality information about the excavation at any instant of the construction. The proposed digital twin framework was validated with real-world case studies, and its effectiveness was demonstrated through two different scenarios. The first scenario integrates the digital twin environment with earthwork equipment for the simulation of model-equipment manipulations. Unity 3D was utilized, which enabled efficient interaction and manipulation between voxels and equipment through different excavation activities, such as digging, loading, hauling, unloading, and visualizing volumetric deformation at the excavation site. The accuracy of the system was validated by comparing the excavation volumes and activity durations of the real and simulated models. Three different material semantics were used, and the system provided an average accuracy of more than 95%. This shows that the system is reliable for use in different earthwork conditions. Additionally, the gravel material achieved a higher accuracy of 97.8% by using a Rigidbody interaction mechanism from the Unity 3D platform, which simulates granular interaction better than cohesion. Simulation activity duration showed a deviation of less than 6% from the field data. In addition, the developed interactive UI provides valuable insights into project dynamics, assisting earthwork construction managers and equipment operators in interacting with the system and manipulating the work environment and equipment to improve construction site planning, understanding, and management. The proposed system offers a training platform for earthwork practitioners in simulations and can improve earthwork quality and productivity. On the other hand, the second scenario uses the digital twin models for earthwork progress monitoring. The trench excavation site was used as a demonstration example. The as-planned and as-built models were regularly integrated and updated during construction, facilitating operational-level monitoring and bridging the gap between physical and digital construction sites. The case study demonstrates that the proposed system is capable of integrating multi-source earthwork data, developing 3D as-planned and as-built models, and updating the model regularly, providing progress visualization and quantification information. Additionally, material information and quality assessment can be achieved from the developed digital twin. The accuracy of the system is greatly affected by the voxel size. However, the proposed system provides a flexible algorithm, and the size of the voxel can be selected according to the allowable error. For instance, the proposed system achieved a progress quantification error below 3% using 60% point cloud subsampling, demonstrating its robustness under reduced data density. Similarly, the size also affects the time required for voxel creation when new data arrives during construction and for modelupdating. The proposed system generates and integrates updated voxels in less than 60 s, confirming the system's suitability for near real-time operational use. ## 6 Limitations and Future Work

Despite the benefits, there are several limitations of the proposed system. The accuracy and reliability of the digital twins are dependent on the accuracy of the input data. For instance, in our proposed system, the geotechnical information is collected from boreholes, and construction data is achieved through scans using laser scanners and photogrammetry. However, sparse distribution in boreholes can result in uncertainties that might incorporate some level of inaccuracies in voxel semantics or missed subsurface features. High-fidelity voxel modeling requires dense and well-structured data, which may not be available or affordable in real-world projects. Data-driven techniques can be integrated in the future to enhance the accuracy of the proposed system [43].