###### Abstract  
Accurate earthwork quantity estimation is essential for effective project planning and cost management in the Architecture, Engineering, and Construction (AEC) industry. Traditional methods for quantity takeoff are often time-consuming and susceptible to human error, particularly when working with unstructured datasets such as CAD drawings. This study introduces the Earthwork Network Architecture (ENA), a novel deep learning framework that incorporates Large Language Models (LLMs), Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM) networks, and Transformers to automate and enhance the accuracy of earthwork quantity estimation. We assume that if LLMs can be trained effectively using such unstructured construction dataset, the effects such as improved accuracy and the challenges of LLMs can be clearly examined. Among the architectures tested, the LLM-based ENA demonstrated superior performance, achieving faster convergence, greater loss reduction, and higher classification accuracy, with a Quantity Takeoff Classification accuracy of 97.17%. However, the LLMs required significantly more computational resources compared with other models. These findings suggest that LLMs, typically used in natural language processing, can be effectively adapted for complex AEC datasets. This study lays the groundwork for future AI-driven solutions in the AEC industry, underscoring the potential of LLMs and Transformers to automate the quantity takeoff process and manage multimodal data in construction projects. eurm  
estimation is indispensable for maintaining the financial viability, operational efficiency, and environmental sustainability of construction projects. Estimating earthwork quantities is typically carried out by estimators before the start of construction projects. However, despite their expertise, this process is highly dependent on the skill and judgment of engineers and surveyors, making it particularly prone to frequent human errors [4]. This vulnerability is even more evident in large-scale projects [5], where the sheer volume and complexity of data exponentially increase the likelihood of mistakes. Human errors in interpreting design drawings, misreading topographical contours, or performing manual volume calculations can occur at multiple stages, often leading to inaccurate estimates [6; 7; 8]. The frequent occurrence of such errors highlights the critical need for automated earthwork quantity estimation systems. By leveraging advanced technologies such as deep learning and CAD integration, automation can greatly minimize human error, improve accuracy, and enhance efficiency [9]. Automating these calculations ensures more reliable estimates, protects project profitability, optimizes resource management, and helps maintain project schedules, underscoring the essential role of automation in modern construction practices. This paper presents a novel approach called the Earthwork Network Architecture (ENA), which utilizes deep learning to optimize and automate the quantity takeoff process for road cross-sections (x-sections). The proposed algorithm transforms data from Computer-Aided Design (CAD) drawings into a structured and trainable dataset, facilitating the extraction of critical features for effective training and evaluation of the ENA model. This paper develops and evaluates the ENA by (1) employing state-of-the-art deep learning algorithms identified through an extensive literature review and (2) implementing the proposed ENA into a real-world road construction project in Korea as a case study. The proposed ENA extracts data from Computer-Aided Design (CAD) drawings and converts them into structured, trainable datasets. The ENA applies various deep learning models, particularly Large Language Models (LLMs) and Transformers, which offer significant advantages in processing complex CAD data and delivering precise estimates.