Large Language Model Agent for Structural Drawing Generation Using ReAct Prompt Engineering and Retrieval Augmented Generation

• URL: http://arxiv.org/abs/2507.19771v1
• Date: Sat, 26 Jul 2025 03:47:12 GMT
• Title: Large Language Model Agent for Structural Drawing Generation Using ReAct Prompt Engineering and Retrieval Augmented Generation
• Authors: Xin Zhang, Lissette Iturburu, Juan Nicolas Villamizar, Xiaoyu Liu, Manuel Salmeron, Shirley J. Dyke, Julio Ramirez,
• Abstract summary: In civil engineering, structural drawings serve as the main communication tool between architects, engineers, and builders.<n>Despite advances in software capabilities, the task of generating a structural drawing remains labor-intensive and time-consuming.<n>Here we introduce a novel generative AI-based method for generating structural drawings employing a large language model (LLM) agent.
• Score: 3.326690511274941
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Structural drawings are widely used in many fields, e.g., mechanical engineering, civil engineering, etc. In civil engineering, structural drawings serve as the main communication tool between architects, engineers, and builders to avoid conflicts, act as legal documentation, and provide a reference for future maintenance or evaluation needs. They are often organized using key elements such as title/subtitle blocks, scales, plan views, elevation view, sections, and detailed sections, which are annotated with standardized symbols and line types for interpretation by engineers and contractors. Despite advances in software capabilities, the task of generating a structural drawing remains labor-intensive and time-consuming for structural engineers. Here we introduce a novel generative AI-based method for generating structural drawings employing a large language model (LLM) agent. The method incorporates a retrieval-augmented generation (RAG) technique using externally-sourced facts to enhance the accuracy and reliability of the language model. This method is capable of understanding varied natural language descriptions, processing these to extract necessary information, and generating code to produce the desired structural drawing in AutoCAD. The approach developed, demonstrated and evaluated herein enables the efficient and direct conversion of a structural drawing's natural language description into an AutoCAD drawing, significantly reducing the workload compared to current working process associated with manual drawing production, facilitating the typical iterative process of engineers for expressing design ideas in a simplified way.

Related papers

• CADDesigner: Conceptual Design of CAD Models Based on General-Purpose Agent [15.288461787523604]
  We present an agent for CAD conceptual design powered by large language models (LLMs)<n>Built upon a novel Context-Independent Imperative Paradigm (CIP), the agent generates high-quality CAD modeling code.
  arXiv  Detail & Related papers  (2025-08-01T19:15:56Z)
• Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text [75.77648333476776]
  This paper introduces an automated pipeline for extracting BPMN models from text.<n>A key contribution of this work is the introduction of a newly annotated dataset.<n>We augment the dataset with 15 newly annotated documents containing 32 parallel gateways for model training.
  arXiv  Detail & Related papers  (2025-07-11T07:25:55Z)
• From Idea to CAD: A Language Model-Driven Multi-Agent System for Collaborative Design [0.06749750044497731]
  We present an approach that mirrors this team structure with a Vision Language Model (VLM)-based Multi Agent System.<n>A model is generated automatically from sketches and/ or textual descriptions.<n>The resulting model can be refined collaboratively in an iterative validation loop with the user.
  arXiv  Detail & Related papers  (2025-03-06T13:21:27Z)
• OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Models [58.45517851437422]
  Visually-situated text parsing (VsTP) has recently seen notable advancements, driven by the growing demand for automated document understanding.<n>Existing solutions often rely on task-specific architectures and objectives for individual tasks.<n>In this paper, we introduce Omni V2, a universal model that unifies VsTP typical tasks, including text spotting, key information extraction, table recognition, and layout analysis.
  arXiv  Detail & Related papers  (2025-02-22T09:32:01Z)
• An Agentic Approach to Automatic Creation of P&ID Diagrams from Natural Language Descriptions [2.8039483625021258]
  We introduce a novel copilot for automating the generation of P&IDs from natural language descriptions.<n>We demonstrate the feasibility of the generation process by evaluating the soundness and completeness of the workflow, and show improved results compared to vanilla zero-shot and few-shot generation approaches.
  arXiv  Detail & Related papers  (2024-12-17T13:21:26Z)
• GLDesigner: Leveraging Multi-Modal LLMs as Designer for Enhanced Aesthetic Text Glyph Layouts [53.568057283934714]
  We propose a Vision-Language Model (VLM)-based framework that generates content-aware text logo layouts.<n>We introduce two model techniques that reduce the computational cost for processing multiple glyph images simultaneously.<n>To support instruction tuning of our model, we construct two extensive text logo datasets that are five times larger than existing public datasets.
  arXiv  Detail & Related papers  (2024-11-18T10:04:10Z)
• Text2CAD: Text to 3D CAD Generation via Technical Drawings [45.3611544056261]
  Text2CAD is a novel framework that employs stable diffusion models tailored to automate the generation process. We show that Text2CAD effectively generates technical drawings that are accurately translated into high-quality 3D CAD models.
  arXiv  Detail & Related papers  (2024-11-09T15:12:06Z)
• Geometric Deep Learning for Computer-Aided Design: A Survey [76.3325417461511]
  Geometric Deep Learning techniques have become a transformative force in the field of Computer-Aided Design.<n>The ability to process the CAD designs represented by geometric data and to analyze their encoded features enables the identification of similarities.<n>This survey offers a comprehensive overview of learning-based methods in computer-aided design across various categories.
  arXiv  Detail & Related papers  (2024-02-27T17:11:35Z)
• Zero-Shot RTL Code Generation with Attention Sink Augmented Large Language Models [0.0]
  This paper discusses the possibility of exploiting large language models to streamline the code generation process in hardware design. The ability to use large language models on RTL code generation not only expedites design cycles but also facilitates the exploration of design spaces.
  arXiv  Detail & Related papers  (2024-01-12T17:41:38Z)
• Parrot Mind: Towards Explaining the Complex Task Reasoning of Pretrained Large Language Models with Template-Content Structure [66.33623392497599]
  We show that a structure called template-content structure (T-C structure) can reduce the possible space from exponential level to linear level. We demonstrate that models can achieve task composition, further reducing the space needed to learn from linear to logarithmic.
  arXiv  Detail & Related papers  (2023-10-09T06:57:45Z)
• InstructCV: Instruction-Tuned Text-to-Image Diffusion Models as Vision Generalists [66.85125112199898]
  We develop a unified language interface for computer vision tasks that abstracts away task-specific design choices. Our model, dubbed InstructCV, performs competitively compared to other generalist and task-specific vision models.
  arXiv  Detail & Related papers  (2023-09-30T14:26:43Z)
• Physics of Language Models: Part 1, Learning Hierarchical Language Structures [51.68385617116854]
  Transformer-based language models are effective but complex, and understanding their inner workings and reasoning mechanisms is a significant challenge.<n>We introduce a family of synthetic CFGs that produce hierarchical rules, capable of generating lengthy sentences.<n>We demonstrate that generative models like GPT can accurately learn and reason over CFG-defined hierarchies and generate sentences based on it.
  arXiv  Detail & Related papers  (2023-05-23T04:28:16Z)
• Natural Language Processing for Systems Engineering: Automatic Generation of Systems Modelling Language Diagrams [0.10312968200748115]
  An approach is proposed to assist systems engineers in the automatic generation of systems diagrams from unstructured natural language text. The intention is to provide the users with a more standardised, comprehensive and automated starting point onto which subsequently refine and adapt the diagrams according to their needs.
  arXiv  Detail & Related papers  (2022-08-09T19:20:33Z)
• Intelligent requirements engineering from natural language and their chaining toward CAD models [0.6091702876917279]
  This paper assumes that design language plays an important role in how designers design and on the creativity of designers. Designers use and develop models as an aid to thinking, a focus for discussion and decision-making and a means of evaluating the reliability of the proposals.
  arXiv  Detail & Related papers  (2020-07-14T17:53:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.