Automated Conversion of BIM Models into Multi-Style 2D Architectural Drawings Using Deep Learning
Building Information Modeling (BIM) has become a central workflow in the architecture, engineering, and construction (AEC) industry, yet 2-dimensional (2D) architectural drawings remain indispensable for documentation, communication, and construction execution. Despite BIM’s advantages, generating 2D drawings from BIM models still requires substantial manual effort, with current practices demanding significant additional time for conversion. This research addresses this inefficiency by proposing an automated, deep learning–based framework capable of transforming BIM models into accurate and stylistically diverse 2D architectural drawings.
Persistent Role of 2D Drawings in BIM-Based Workflows
Although BIM offers rich parametric and information-embedded models, 2D drawings continue to dominate regulatory submissions, on-site coordination, and professional communication. The reliance on manual or semi-automated conversion processes introduces redundancy and inefficiency into BIM workflows. This section examines the ongoing relevance of 2D drawings in the AEC industry and highlights the practical need for intelligent automation to bridge the gap between BIM environments and conventional drawing deliverables.
Hybrid Architectural Drawing Recognition (Hyb-ADR) Framework
The study introduces a Hybrid Architectural Drawing Recognition (Hyb-ADR) program that integrates deep learning–based detection and classification models. Hyb-ADR identifies architectural elements within 2D drawings, forming the analytical foundation for automated drawing generation. This topic discusses the structure of the hybrid framework and explains how combining recognition and classification enhances the system’s ability to interpret architectural representations with higher reliability.
Deep Learning Models for Element Detection and Classification
At the core of the proposed framework are deep learning models trained to detect and classify architectural components within drawings. These models enable the system to understand spatial and symbolic relationships embedded in architectural graphics. This section focuses on how learning-based recognition improves automation accuracy and supports the extraction of meaningful architectural information necessary for reliable 2D drawing generation from BIM data.
Parametric Stylization and Multi-Style Drawing Generation
To extend automation beyond basic conversion, a parametric algorithm is developed to enable stylization of 2D drawings. This algorithm allows the system to generate multiple drawing styles from a single BIM model, responding to varying documentation standards and presentation requirements. This topic highlights the role of parametric control in achieving stylistic flexibility, positioning the framework as both a technical and representational advancement in architectural documentation.
Validation Results and Industry Implications
Validation using two reference drawings in different styles demonstrates an accuracy rate of 81.85% for the Hyb-ADR program, while the parametric algorithm successfully generates multi-style 2D drawings from BIM models. These results confirm the feasibility and effectiveness of the proposed framework. This final section discusses the broader implications for construction efficiency, workflow optimization, and the future integration of artificial intelligence into BIM-centric architectural practice.
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