## 3 Framework for Automated Earthwork Network Architecture (ENA)
### Module-2: ENA Model Development  
In this module, various deep learning models were designed, trained, and implemented to predict earthwork quantities based on the pre-processed data from Module-1 (Topological Feature Collector). This module begins with the Feature Tokenizer Process, which is a critical step in converting input features from CAD drawings or other sources into a structured format suitable for deep learning models. The tokenizer is responsible for transforming each feature, such as spatial attributes and geometrical properties, into tokens that can be processed by models like Transformers and Large Language Models (LLMs). Figure 5 defines the tokenization process centered on the training of Transformers-based ENA models (M3, M4). Note that other models, M1 and M2, differ in the one-hot encoding part and output. It shows how these tokens were structured and embedded before being fed into the model, highlighting the multi-step transformation from raw input to a trainable format. The process began with the extraction of key features from CAD drawings, including ground lines, slope lines, and structure layers, which were then mapped into a token table using a predefined token mapping strategy (T1). Each feature was assigned a unique token identifier, representing its position and role in the dataset. Following this, the ENA Feature Tokenizer function (T2) converted these mapped features into corresponding tokens, generating a sequence that encapsulated the spatial and topological features of the input data. This sequence was subsequently used to create the ENA dataset, which categorized earthwork-related attributes into various classes, such as cut areas, fill areas, and slope conditions. Once tokenization was complete, the data were organized into a format that deep learning models could efficiently process. The figure illustrates how the data were represented using three main embedding components: token embeddings, segment embeddings, and position embeddings. Token embeddings captured the unique attributes and relationships of each feature in the dataset, translating the raw features into numerical vectors that the model could understand. Segment embeddings, on the other hand, provided additional context by indicating which part of the input data a particular token belonged to, helping the model interpret the grouping and segmentation of tokens within the dataset. Position embeddings specified the spatial position of each token within the sequence, ensuring that the model comprehended the order and positional relationships between features. This positional context was particularly critical for models like Transformers, which rely heavily on sequence information to learn complex patterns and dependencies. Figure 5: The process flow of the ENA Feature Tokenizer in Module-2. The structured and tokenized representation of input data, as depicted in Figure 5, served as the foundation for training the ENA models. The figure demonstrates how this tokenization process enabled models to effectively learn from unstructured data by organizing and encoding the spatial and topological features into a cohesive format. This approach allowed the ENA framework to process intricate spatial relationships and geometrical attributes, thereby facilitating the development of models capable of making precise earthwork quantity estimations. The tokenization process enhanced the framework's ability to handle diverse and complex datasets within the construction domain, providing a robust and scalable solution for automating the estimation process. It illustrated the ENA Feature Tokenizer Process of this module, demonstrating the stages from feature formatting to model training. This workflow was integral to the ENA framework, which aims to automate and enhance earthwork quantity estimation using multiple deep learning architectures.