## 2 Related Work  
The application of deep learning and AI in automated earthwork quantity estimation is still evolving, showing promising potential to automate complex tasks. While significant research has explored AI's role in improving construction productivity (e.g., equipment tracking, crack detection, and 3D point cloud enhancement [11]), challenges such as limited structured datasets, model interpretability, and autonomous decision-making persist. Current implementations in construction machinery and robotics have demonstrated enhanced perception, navigation, and control [12]. However, the application of deep learning to automate earthwork quantity takeoff remains underdeveloped. Some studies, like vision systems for tracking equipment and analyzing posture, have shown promise in enhancing efficiency [13; 14]. Similarly, object detection models have been applied to boost project efficiency [15]. However, issues like data variability and lack of structured collection hinder widespread deployment. Emerging tools like Vision Transformers (ViT) facilitate real-time monitoring and advanced design simulations in the Architecture, Engineering, and Construction (AEC) sector [16], highlighting the need for focused research on automating estimation processes. AI-based machine guidance (MG) and control systems have led to significant productivity gains in trench excavation [17]. While models such as CNN-based object detection for excavator tasks [18] and DNN-based productivity benchmarking [19] provide real-time insights, they have yet to fully address the complexities of automated quantity estimation. Moreover, powerline detection using UAV-based LiDAR data [20; 21] and road extraction models like Seg-Road [22] suggest potential for adapting these techniques to automate earthwork quantity calculations. Yet, successful adoption in earthwork applications remains limited due to the need for high-quality input data. Deep learning has shown promise in various earthwork-related tasks, such as tunnel point cloud segmentation [23] and BIM-GIS integration for real-time spatial information [24; 25]. Technologies like K-Net have further potential in material classification for real-time earthwork estimation [26]. Additionally, graph-based learning and automated CAD analysis have been explored to capture geometric features for architectural design [9; 27], and similar approaches could enhance earthwork estimation automation. Simulation models like the LSTM-Transformer fusion have been applied for transit flow prediction [28], while reinforcement learning has been explored for optimizing cut and fill operations [29]. However, these models are not yet widely adopted for earthwork automation. In construction management, AI has been used to predict vehicle speeds in earthwork simulations using improved XGBoost models [30], and progress monitoring systems integrated with UAV-based image analysis have shown potential despite challenges in complex data segmentation [3; 31; 32]. GPT models have been used to predict construction accidents, contributing to safer and more efficient operations [33; 34]. Combining domain knowledge with AI, such as in subway construction risk management [35] and 3D modeling of earthwork sites using deep learning [36; 37; 38; 39; 32], underscores AI's expanding role in optimizing construction management and quantity estimation. It can be seen that most of the deep learning models applied in the AEC field investigated above are the MLP, CNN, U-NET, LSTM, etc., and the emerging LLM-based research is lacking. Especially, the Transformer-based models such as LLM are not widely used, which shows that it is not easy to build learning data related to LLM in the field of construction engineering. To utilize LLM in a specific construction field, it is necessary to develop learning data from a more specialized and engineering perspective rather than using cases such as translation, writing, summarization, and proposal, in which LLM is active.