## 6 Conclusions  
The rapid development of LLM (Large Language Model) technology, which excels at converting multimodal data formats in latent space, offers significant potential for application in the AEC (Architecture, Engineering, and Construction) industry. This is particularly valuable in a field that frequently deals with informal and unstructured data, such as CAD drawings and complex earthwork features.  
This study aimed to develop an ENA model for automatic classification of earthwork quantity, design the model learning strategy for it, and evaluate its performance. Specifically, we extracted earthwork features from the road cross-section designed in CAD format. These were examples of unstructured data, and these features were trained using various deep learning models, including MLP-, LSTM-, Transformers-, and LLM-based ENA models.  
As expected from the hypothesis, the results demonstrate that pre-trained LLMs exhibited superior performance, achieving better loss values compared with traditional deep learning models during earthwork classification tasks. For instance, the LLM-based model showed a significant improvement in convergence speed, with a loss of 0.1685 by epoch 10, outperforming other models. However, it is important to note that LLM-based models require substantial GPU resources (at least 129\(\times\) more GPU RAM than other models), emphasizing the need to select the appropriate model based on the use case and available computational environment.  
Through this study, we identified several key advantages of the LLM-based ENA model. One of the primary benefits is its ability to handle multimodal data types through a simpler input sequence format, which enables more efficient feature encoding of unstructured data, such as CAD entities. This feature positions LLMs as a transformative technology with the potential to significantly advance AI applications in the AEC industry, particularly in scenarios involving informal and multimodal datasets.  
Although Transformer-based LLMs are already recognized as a cornerstone for generating AI solutions, their application in the AEC domain is still in its infancy. In AEC, graph data such as CAD drawings and Industrial Foundation Classes (IFCs) are frequently used in decision-making processes. Looking ahead, there is substantial opportunity to expand on this study by exploring more effective embedding techniques, refining LLM architectures, and developing generative AI systems that support multimodal data processing within the AEC industry. As LLM technology continues to evolve, it will likely play an increasingly pivotal role in addressing the unique challenges of handling unstructured data in construction and engineering applications.  
In the future, we plan to further develop cases and methods in which generative AI technologies such as LLMs can be used in various construction fields, and to develop them into multi-agent systems in AEC.  
Writing--original draft, T.K.; Writing--review & editing, K.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Development of BIM Management and Building Code Checking Automation System Based on Generative AI grant number 20240302-001, And the APC was funded by KICT (Korea Institute of Civil Engineering and Building Technology).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
We would like to acknowledge the contribution of the road crossing drawings used for model training, provided by engineers [PERSON] and [PERSON] at Gunhwa Engineering (Seoul, Republic of Korea). Research for this paper was carried out under the KICT Research Program (project no. 20240302-001, Development of BIM Management and Building Code Checking Automation System Based on Generative AI) funded by the Ministry of Science and ICT.  
The authors declare no conflict of interest.